CN117500610A

CN117500610A - Adhesive dispensing system and method

Info

Publication number: CN117500610A
Application number: CN202280042833.7A
Authority: CN
Inventors: 约翰·A·麦钱特; 布丽安娜·L·麦考德; 艾莉萨·P·温纳; 艾琳·塞劳·德菲利波
Original assignee: 3M Innovative Properties Co
Current assignee: 3M Innovative Properties Co
Priority date: 2021-06-16
Filing date: 2022-06-15
Publication date: 2024-02-02

Abstract

Apparatus, systems (400) and methods for predicting parameters of a dispenser system, dispensing dispensable material, calibrating a dispenser system are described. The apparatus and system may use a machine learning algorithm based on environmental variables and other factors in a system using an adhesive dispenser (100). Further, the apparatus and system may adjust the operating parameters of the dispenser system based on at least one process parameter. Further, the devices and systems may provide one or more settings for one or more dispenser components based on the calibration model. The algorithm (412) may operate on remote or local software and control systems, or as part of an edge computing system or internet of things (IoT) system.

Description

Adhesive dispensing system and method

Background

Systems for dispensing adhesive typically include an inlet or interior region for holding adhesive and an outlet or tip through which the adhesive is dispensed to a surface. The flow rate of the adhesive can be directly controlled by using a metering system to meet the needs of the downstream manufacturing process. However, systems that actively control flow rate can be overly complex and expensive for most users. The indirect method may use a calibration curve to provide or recommend settings based on different variables such as air pressure, pump settings, application time, total volume dispensed, or other variables. However, such calibration curves may become overly complex in view of the amount of adhesive, varying temperatures, and chemical reactions that may occur throughout the dispensing process. Other systems involving neural networks may learn or predict settings, but these require hundreds or thousands of calibration points to become useful. Thus, there is a general need to more accurately predict dispenser settings and other dispensing parameters in a timely and cost-effective manner.

Disclosure of Invention

In one aspect, the present disclosure provides an apparatus comprising: a memory for storing data indicative of at least one parameter of the adhesive dispensing system. The apparatus further comprises: a processor coupled to the memory. The processor is configured to: the at least one parameter is retrieved. The processor is further configured to: a value of an operating parameter of the adhesive dispensing system is determined based on at least one parameter, the value of the operating parameter to achieve a flow rate of adhesive in the adhesive dispensing system. The processor is further configured to: the operating parameter is provided to the adhesive dispensing system.

The at least one parameter may relate to the viscosity of the adhesive in the adhesive dispensing system. The operating parameters may include a driving force pressure for the adhesive dispensing system. The processor may be configured to: the driving force pressure is determined from the relationship between pressure and viscosity based on the viscosity μ of the adhesive and parameters specific to the adhesive dispensing system. The relationship may include a constant determined based on a non-neural network machine learning algorithm. The relationship may include a constant determined based on a hybrid algorithm consisting of a neural network portion and a non-neural network machine learning algorithm. Similar methods and systems are also described.

In another aspect, the present disclosure describes a dispenser system including one or more dispenser components; and a processor operatively coupled to the one or more dispenser components. The one or more dispenser components include one or more sensors for providing at least one process parameter of the dispensable material. The one or more sensors include a temperature sensor including a probe disposed in a fluid path of the dispensable material and configured to sense a temperature of the dispensable material. The processor is configured to: receiving at least one parameter associated with the dispenser system; determining a value of an operating parameter of the dispenser system based on at least one parameter, the value of the operating parameter to achieve a flow rate of dispensable material in the dispenser system; and providing the operating parameter. The processor is further configured to: receiving the temperature of the dispensable material from the temperature sensor; adjusting a value of the operating parameter of the dispenser system based on the sensed temperature; and providing the adjusted operating parameters.

In another aspect, the present disclosure describes a method of dispensing dispensable material using a dispenser system. The method comprises the following steps: receiving at least one parameter associated with the dispenser system; determining a value of an operating parameter of the dispenser system based on at least one parameter, the value of the operating parameter to achieve a flow rate of dispensable material in the dispenser system; providing the operating parameter; receiving at least one process parameter comprising a sensed temperature of the dispensable material; adjusting a value of the operating parameter of the dispenser system based on the sensed temperature; and providing the adjusted operating parameter.

In another aspect, the present disclosure describes a dispenser system comprising: one or more dispenser components configured to provide dispensable material; and a processor operatively coupled to the one or more dispenser components. The processor is configured to: a plurality of calibration data points of the dispenser system and one or more parameters of the one or more dispenser components are received. The plurality of calibration data points is based on a plurality of dispensing samples of the dispensable material. The processor is further configured to: selecting one or more predetermined models based on the one or more parameters of the dispenser components; determining a calibration model based on the plurality of calibration data points and the one or more models; and adjusting one or more settings of the one or more dispenser components based on the calibration model.

In another aspect, the present disclosure describes a method for calibrating a dispenser system. The method comprises the following steps: receiving a plurality of calibration data points of the dispenser system based on a plurality of dispensed samples of dispensable material and one or more parameters of a dispenser component of the calibration system; selecting one or more predetermined models based on the one or more parameters of the dispenser components; determining a calibration model based on the plurality of calibration data points and the one or more predetermined models; and providing one or more settings for the dispenser system based on the calibration model.

The above summary of the present disclosure is not intended to describe each disclosed embodiment or every implementation of the present disclosure. The following description more particularly exemplifies illustrative embodiments. Guidance is provided throughout this application by a list of examples, which may be used in various combinations. In each case, the recited list serves only as a representative group and should not be construed as an exclusive list. Therefore, the scope of the present disclosure should not be limited to the particular illustrative structures described herein, but rather should be extended at least to the structures described by the language of the claims and the equivalents of those structures. Any elements of the alternatives positively recited in the present specification may be explicitly included in or excluded from the claims in any combination as required. While various theories and possible mechanisms may have been discussed herein, such discussion should not be taken to limit the claimable subject matter in any way.

Drawings

FIG. 1 is a block diagram of an adhesive dispenser in which exemplary embodiments may be implemented.

Fig. 2 is a linear calibration curve for determining the flow rate of an adhesive dispenser.

FIG. 3 is a non-linear calibration curve for determining the flow rate of an adhesive dispenser.

Fig. 4 is a system for predicting flow rate adjustments for multiple variables, according to some embodiments.

FIG. 5A illustrates an exemplary Graphical User Interface (GUI) according to some embodiments.

FIG. 5B illustrates an exemplary GUI for manually entering calibration data according to some embodiments.

FIG. 6 is a flow chart of a method for predicting adhesive dispenser pressure according to some embodiments.

Fig. 7 depicts a comparison between the performance of a neural network-based model and a learning model used in some embodiments.

Fig. 8 depicts a computing node according to some embodiments.

Fig. 9 depicts additional details regarding edge computing nodes according to some embodiments.

Fig. 10 is a schematic diagram of a feedback sensor according to some embodiments.

Fig. 11 is a flow chart of a method for calibrating a dispenser system according to some embodiments.

Fig. 12 is a flow chart for dispensing dispensable material according to some embodiments.

Detailed Description

As part of the manufacturing process, the adhesive dispenser provides liquid adhesive to the surface or substrate through a tip or nozzle. Fig. 1 is a block diagram of an adhesive dispenser 100. The adhesive dispenser is configured to deliver liquid adhesive from a source 104 of liquid adhesive to a dispensing member 106 using a driving force 102 such that the liquid adhesive can be dispensed using the dispensing member 106 as desired. The operator may use various settings or parameters of the adhesive dispenser, including using the controller 108 or 110 to dispense adhesive at a desired flow rate. These settings or parameters may vary depending on a number of factors, and it may be difficult to predict settings that will work for any particular combination of factors.

Given the viscosity or other identifying information of the adhesive used, a calibration curve may be used to predict or recommend adhesive dispenser 100 settings. Fig. 2 shows a linear calibration curve 200. In the exemplary graph 200, the mass of adhesive dispensed per unit time may be plotted based on the viscosity of the adhesive. The quality of the dispense will vary with the driving force 102 pressure and adhesive viscosity. The amount dispensed can be predicted by a linear curve. For example, as seen in curve 202, given the low pressure at the drive force 102 controller 108, less and less mass of adhesive is dispensed as the viscosity increases.

However, the obtained measurement of the actual dispensing quality may not fit such a curve exactly. For example, as seen at curve 202, the actual dispensing quality shown by measurement 204 is higher than curve 202. Another measurement 206 may be closer to the predicted linear curve 208. Thus, the linear fit gives only a rough prediction.

Other systems based on neural networks may be used to control the instructions for applying the adhesive. However, neural network based systems require hundreds or even thousands of calibration points to be useful and a significant amount of learning must be done before any control of adhesive dispensing can be exercised.

System and algorithm for adhesive dispensing

To address these and other complexities, systems, methods, and apparatus according to various embodiments use machine learning to predict dispenser settings or other parameters for accurate adaptable adhesive dispensing that works well with downstream manufacturing processes according to operator needs. The prediction may be based on any data related to the adhesive or adhesive dispensing process. Fig. 4 is a system 400 for predicting flow rate adjustments for multiple variables, according to some embodiments.

Referring to fig. 4, a system 400 may include a data component 402. The data component 402 can include input, such as user input, received from the human-machine interface 404. Further, the data component 402 can include an output provided for display to the human machine interface 404. The human-machine interface 404 may then provide input and receive output from the dispenser component 406. In addition, the human-machine interface 404 may provide data including visual and audio indicators to the human operator 408 and receive input from the human operator 408, such as keyboard entries. The human operator 408 may interact with the dispenser component 406, for example, by changing settings or parameters of the dispenser component 406.

The data component 402 can include sensor data 410. The sensor data 410 may include, for example, rheological data regarding the calibration liquid and the adhesive to be dispensed; data concerning adhesive manufacture, including in-plant measurements; environmental parameters or conditions (as provided by sensor 428); and lot information, cartridge information, or other information that may identify the adhesive batch. The data component 402 may include material properties 414 and dispenser properties 416 of the adhesive. The sensor 418 may include data regarding the adhesive during the supply chain process, including the temperature, or humidity, to which the adhesive has been subjected, and the amount of time that extreme temperature conditions or humidity conditions exist. The data component 402 may also include data 420 regarding the different modular components of the adhesive dispenser 100.

The data component 402 can also include algorithms 412, such as machine learning algorithms, curve fitting algorithms, minimization and optimization functions, and the like. The algorithm 412 according to an embodiment may generate predictions based on data associated with the calibration fluid. The calibration liquid may comprise a model of liquid, an ideal liquid or a special calibration liquid that is used only for the calibration process and not for example for the production process. In any case, the calibration liquid should be dispensed using, for example, the adhesive dispenser 100. In an example, the input data may include any of the above data, such as machine calibration data or dispenser calibration data, in addition to data entered by a user or another system. In some examples, a temperature sensor or humidity sensor or other sensor in the room or area in which adhesive dispensing is or will be taking place may provide input to algorithm 412.

As mentioned earlier herein, the relationship between the mass, viscosity, and pressure of the adhesive may be nonlinear, creating complexity in determining or predicting the appropriate pressure under which to dispense the adhesive. Formulas or relationships may be developed to show the non-linear relationship between pressure and viscosity. An exemplary equation (1) can be used to generalize the nonlinear relationship:

m＝G(F1(p),F2(μ)…) (1)

where m is the mass of adhesive dispensed, F1 is a function of pressure p, and F2 is a function of viscosity μ. Other functions and relationships may be included, and equation (1) should not be construed as limiting the embodiments to the relationship between pressure and viscosity. The constants within those relationships are solved using a machine learning algorithm as described herein. The constant may vary with environmental conditions or other conditions, and in some embodiments, different constants will be obtained in different iterations of the machine learning algorithm.

According to equation (1) or similar, a non-linear fit may be provided as in fig. 3. However, this increases the mathematical complexity of the prediction process. In addition, other variables may add dimensions to the prediction, thereby increasing complexity. For example, if temperature is considered, the prediction will become three-dimensional with three variables (e.g., temperature, mass, and pressure), and modeling using a calibration curve will become increasingly difficult. Chemical reactions within the adhesive or between adhesive portions, whether during storage or during the dispensing process, can further increase the difficulty of developing accurate calibration curves that will be suitable for real world conditions.

Algorithm 412 may generate predictions based on equation (1). In some exemplary embodiments, referring to equation (1), the constants associated with F1 and F2 may be solved using, for example, a machine learning algorithm or by solving an optimization problem as described later herein. These constants may include dispenser-specific constants or adhesive-specific constants that are calculated for each adhesive dispenser 100 and that may change over time or temperature. The viscosity μmay be determined based on direct or indirect measurements, polynomial fits, numerical regression, or formulas such as the anderred formula for the liquid viscosity provided in formula (2):

where T is the temperature and D and E are constants to be solved.

As described above, the constants associated with equation (1) and constants D and E may be calculated, for example, using a machine learning algorithm or by solving an optimization problem. Such an algorithm may estimate the viscosity (or receive a value indicative of the viscosity estimate) based on, for example, aging of the adhesive, and apply the estimate to the value of D or E. Some constants may be calculated based on a machine learning algorithm that minimizes the loss function. The loss function may have input parameters related to at least one of pressure, mass, volume, time, and temperature of the adhesive, the adhesive dispenser, or a process associated with the adhesive application.

The quality of the model and any predictions made may be estimated using, for example, a root mean square error algorithm that compares the predicted values to measured values of various parameters, including the quality of the dispense, pressure, temperature, viscosity, etc. Based on the determined constants, the mass to be dispensed and the ambient temperature and humidity, pressure, or setting under which the adhesive should be dispensed are provided to the human-machine interface 404.

Still referring to fig. 4, the human-machine interface 404 may include a user interface 422, local storage and processing 424, a connection to a server 426, a sensor 428, and a connection 430 to the dispenser 406. The sensors 428 may include an ambient temperature sensor and a humidity sensor. For example, the human-machine interface 404 may also include a two-dimensional (2D) bar code scanner or QR bar code scanner to scan batch information or other identifying information of the adhesive or adhesive container. Some components of the human-machine interface 404 are described in more detail herein with respect to fig. 7-8.

The human machine interface 404 may be in communication with a controller 432 of the dispenser unit 406 via a connection 430. The pressure or setting predicted by algorithm 412 may then be used by controller 432 to control dispenser hardware 434. For example, the controller 432 may control the dispenser hardware 434 (e.g., the same as or similar to the driving force 102) to dispense the dispensable material 436 at a pressure predicted or indicated by the algorithm 412. The dispenser component 406 may include additional sensors 438.

In addition to predicting the desired pressure under which the adhesive is dispensed, the machine learning model described above may also predict or suggest other settings for remedying the adhesive process or providing an adhesive flow rate that meets the needs of the downstream process. These suggestions may be provided by various human interface elements as described later herein.

Feedback of

Feedback may be used to incorporate new experiences into subsequent machine learning iterations. The data provided via the feedback may be indicative of the quality of the dispensing process or a process associated with the dispensing process. The data may also include qualitative data regarding, for example, the achieved flow rate. In some exemplary embodiments, the data for adhesive dispensing as intended may be provided by a human operator through visual inspection or other observation, through an indication, through human-machine interface 404, smart phone, or the like.

In other exemplary embodiments, machine sensors 438 (including, for example, machine vision sensors, mass sensors, etc.) or any device capable of detecting heat, humidity, weight, mass, adhesive quality, volume, bead profile, etc. may be used as a source of feedback information. Whether feedback data is provided through a human interface or through the machine-based sensor 438, the algorithm 412 may use the provided data to refine the constants used in the algorithm 412.

Suggesting data capture

Predictions and suggestions may be provided on a user interface, such as Graphical User Interface (GUI) 500. Fig. 5A illustrates an exemplary GUI 500 according to some embodiments.

GUI 500 may be provided by a smart phone or other user device, or a stand-alone device associated with human-machine interface 404 or any of the components of the system described later herein with respect to fig. 7 and 8. GUI 500 may display information regarding the adhesive being dispensed. Exemplary information may include product name, product color, images of tubes or other containers of the product, lot number and other manufacturing information, expiration date, and the like.

GUI 500 may display one or more parameters 502 indicating desired conditions for the dispense function. For example, GUI 500 may display a desired dispense flow rate. In an embodiment, the parameters 502 are user-editable so that a user may suggest a desired flow rate, quality, or dispense time based on, for example, the needs of a process downstream of the bonding process being controlled. GUI 500 may display such information for any number of remote or local dispensing machines. In some embodiments, a user may switch between different dispensing machines using interface elements 504, such as drop-down boxes, lists, or other elements.

GUI 500 may be displayed as part of a web application for providing suggestions of pressure settings or other operational settings for adhesive dispenser 100 (fig. 1), where such operational settings have been determined using algorithm 412 (fig. 4). Instead of, or in addition to, the periodic updates provided by algorithm 412 (FIG. 4), the user may request that pressure predictions or recommendations be made. For example, the user may press an interface element 506, such as a button, and the smartphone or other user device may wirelessly transmit a request that algorithm 412 execute to provide updated suggested pressures. Suggested values of parameters (e.g., pressure) may be displayed on GUI 500. In some examples, the user may then manually use the suggested pressure with the adhesive dispenser 100. In other examples, dispenser component 406, including controller 432 (fig. 4), may automatically control dispenser hardware 434 based on the suggested pressure.

The machine learning model described above may also predict when current environmental conditions (such as temperature and humidity, but embodiments are not limited thereto) will result in lower quality predictions. GUI 500 may then be used to request or suggest additional calibration points to be captured by the user, and to retrieve such calibration points from user input. This may improve the overall machine learning model.

In other exemplary embodiments, if the user requests a flow rate far outside of the previously requested flow rate such that the application pressure will be much higher than previously used, the user may be notified via GUI 500 or an audio alert or the like that additional calibration points should be captured. For example, if a user requests a flow rate that will require 50psi pressure, but typically a dispense range of 20psi-40psi, the user will be requested to provide a calibration point associated with 50 psi. Calibration points may be as shown in fig. 5B, for example, the calibration points may include lot number 512 information, pressure 514 information, dispensed quality 516 information, dispense time 518 information, and temperature 520 information. Then, in the illustrated example of a 50psi calibration point, the user will provide the information shown in FIG. 5B, such as temperature and dispense time at 50 psi. In an example, the time stamp may be added to the calibration point at the time of storage by, for example, the human-machine interface 404 or associated processing circuitry. In some embodiments, fewer or additional fields may be included in the calibration points.

The model described earlier herein can identify opportunities to enhance the predicted quality. In at least some example embodiments, suggestions for such opportunities can be generated and displayed on the GUI 500 or other user interface. If opportunities for predictive improvement are identified, systems and devices according to embodiments may indicate to a user (whether through audio or visual alerts or indications, text, voice messages, etc.) that the user should capture adhesive data that may result in predictive improvement.

For example, if the machine learning model determines that flow rate data has not been captured at a particular temperature, a system according to an embodiment may request that the user capture dispense data at that particular temperature. Alternatively, a system according to an embodiment may control the sensor to capture such data at that temperature. In these and other embodiments, additional GUI screens may be provided with which a user may interact to manually enter data related to data points that may enhance the machine learning algorithm. In at least these embodiments, some or all of the data may be automatically entered by dispenser component 406 (fig. 4) via wireless communication Near Field Communication (NFC) or other methods. For example, the user may manually enter calibration data in GUI screen 510 as shown in fig. 5B.

FIG. 5B illustrates an exemplary GUI for manually entering calibration data according to some embodiments. In an example, the user may enter a lot number 512 that identifies a manufacturing lot of the liquid adhesive or a manufacturing lot of one of the portions of the liquid adhesive. In other examples, the lot number 512 may be automatically provided, for example, by reading an RFID chip or bar code associated with the dispenser component 406 or by wireless communication from the dispenser component 406. The pressure used may be provided at 514. The quality of the dispense may be provided by dispenser component 406, e.g., by sensor 438, for automatic or manual entry into section 516. The sensor 438 may also provide feedback data to the algorithm 412 (including machine learning algorithms). For example, the amount of time 518 allocated may be entered manually by a user, or the controller 432 may provide such a time value. Other parameters affecting pressure prediction, such as temperature 520, may be included or entered. The user may delete the relevant data point or add a data point using interface buttons 522 and 524, respectively.

In-process adjustment

Feedback may also be used to adjust various settings to achieve or maintain operating parameters or performance set points as material is dispensed. For example, data or process parameters related to changes in viscosity of the dispensable material as it is dispensed may be used to adjust the driving force pressure to maintain a desired flow rate. The process parameters may include temperature, conductivity, viscosity, weight, mass, or other parameters of the dispensable material 436 or the dispenser member 406 during the dispensing process. The process parameters may be sensed by various sensors (such as, for example, sensor 438 of dispenser component 406) or determined from sensor data from these sensors. The sensor 438 may include, for example, a visual sensor, a mass sensor, a temperature sensor, a conductivity sensor, or any device capable of detecting heat, humidity, weight, mass, material quality, conductivity, or other parameters.

In one or more embodiments, one or more sensors (e.g., sensor 438) can provide at least one process parameter of the dispensable material. The at least one process parameter may include, for example, a temperature, conductivity, viscosity, weight, mass, or other parameter of the dispensable material (e.g., adhesive, sealant, thermal paste, etc.). The one or more sensors 438 may include sensors that may sense the temperature and/or conductivity of the dispensable material, such as the sensor 910 of fig. 10.

Fig. 10 illustrates an example of one or more sensors 910 that may sense a temperature and/or electrical conductivity of a dispensable material as it flows through a conduit member 900 (e.g., dispenser component 406 or dispenser hardware 434 of fig. 4). The conduit member 900 includes a conduit body 902 having a channel 904 extending from a conduit inlet 906 to a conduit outlet 908. The channel 904 may provide a fluid path for dispensable material (e.g., dispensable material) to flow from the conduit inlet 906 to the conduit outlet 908 during a dispensing process.

As the dispensable material flows through the channel 904, the dispensable material can contact various devices of one or more sensors 910 disposed in the piping component 900 to allow the one or more sensors 910 to sense a process parameter of the dispensable material. For example, the one or more sensors may include a probe 912 disposed in a fluid path defined by the channel 904. The probe 912 may be configured to react to the temperature of the dispensable material in a manner that is detectable by the one or more sensors 910. For example, the probe 912 may include a thermistor, and the one or more sensors 910 may be configured to determine a resistance of the thermistor and sense or determine a temperature of the dispensable material based on the resistance of the thermistor. In one embodiment, the probe 912 may be configured to directly contact the dispensable material in the fluid path. In another embodiment, the shield may separate the outer surface of the probe 912 from the dispensable material, and the probe may be configured to sense the temperature of the dispensable material through the shield. The shield may comprise any suitable material or materials such as, for example, plastic, metal, or other thermally conductive material.

Further, for example, the one or more sensors 910 may include electrodes 914 disposed in a fluid path defined by the channel 904. The one or more sensors 910 may be configured to provide a voltage across the electrodes 914 and measure the resulting current between the electrodes and through the dispensable material. Based on the measured current, one or more sensors 910 may sense the conductivity of the dispensable material.

The sensor 438 may include additional sensors and sensor devices such as described in PCT publication No. 2022/013786A1 (Munstermann et al), the disclosure of which is incorporated herein by reference.

Component library

The systems, apparatuses, and methods may generate a predictive model under the assumption that the dispenser is a "black box" or is simply a component with an input and an output without understanding the internal components of the dispenser. However, other predictive models may be constructed from mixing nozzles, dispensing tips, or other components of adhesive dispenser 100 (fig. 1), including, for example, tubes, cartridges, pressure valves, pumps, adapters, pinch tubes, and the like. In some embodiments, the predictive model may include a formula similar to formula (1). In some embodiments, machine learning algorithms may be used to solve for similar or different constants. By creating a predictive model of the dispenser components, when a new dispenser is added to an operator's process, calibration requirements may be reduced or eliminated by using settings based on component models previously generated or stored using any of the above processes. Furthermore, new dispenser components may be added to the dispenser without the need to calibrate the settings. For example, new dispensing tips may be added and pressures that have been generated and predicted may be applied using these dispensing tips, depending on factors such as viscosity, temperature, etc., as described earlier herein.

Exemplary method

Fig. 6 is a flow chart of a method 600 for predicting adhesive dispenser pressure, according to some embodiments. The operations of method 600 may be performed by, for example, processor 704 (fig. 8) on which algorithm 412 (fig. 4) may be executed. In an example, algorithm 412 may be partially or fully executed using machine learning and based on inputs automatically generated and provided by sensors or entered by a user, as well as other inputs.

The method 600 may begin at operation 602, where the processor 704 retrieves at least one parameter associated with the adhesive dispenser 100 (fig. 1). In some embodiments, at least one parameter relates to the viscosity of the adhesive in the adhesive dispenser 100. In some embodiments, operation 602 may include receiving at least one feedback parameter indicative of a quality of the bonding process. The feedback parameters may be received from user input or sensors. In some examples, the feedback parameters may include image data or may be based on image data or based on quality data. The quality data may indicate the amount of adhesive dispensed. In an exemplary embodiment, the quality feedback data may be used to determine whether the amount of adhesive dispensed is within a target range of a desired amount of adhesive.

In an exemplary embodiment, predictions and suggestions are provided on a user interface, and further, the processor 704 may request user input or capture calibration points. As described earlier herein, the calibration points may include information similar to that shown in fig. 5B. For example, the request may be based on a determination that the requested flow rate falls outside of a threshold range of the usual range. In other examples, the request may be in response to determining that the environmental parameter falls outside of a typical range or a threshold range of the typical range. For example, if the room temperature is-20 degrees Fahrenheit, the processor 704 may request that the user manually enter the adhesive viscosity at that temperature. In at least these examples, the predictive capabilities of the machine learning model may be limited due to anomalies in environmental factors, and thus manual data entry is requested.

Suggestions or requests for data capture may be provided on a GUI as described above with reference to fig. 5A and 5B.

In other exemplary embodiments, if the user requests a flow rate far outside of the previously requested flow rate such that the application pressure will be much higher than previously used, the user may be notified via GUI 500 or an audio alert or the like that additional calibration points should be captured. For example, if a user requests a flow rate that will require 50psi pressure, but typically a dispense range of 20psi-40psi, the user will be requested to provide a calibration point associated with 50psi, for example, using an interface similar to that shown in FIG. 5B.

FIG. 5B illustrates an exemplary GUI for manually entering calibration data according to some embodiments. In an example, a user may enter a lot number 512 identifying a manufacturing lot of liquid adhesive. In other examples, lot number 512 may be automatically provided, for example, by reading an RFID chip associated with dispenser component 406 or by wireless communication from dispenser component 406. The pressure used may be provided at 514. The quality of the dispense may be provided by dispenser component 406, e.g., by sensor 438, for automatic or manual entry into section 516. The sensor 438 may also provide feedback data to the algorithm 412 (including machine learning algorithms). For example, the amount of time 518 allocated may be entered manually by a user, or the controller 432 may provide such a time value. Other parameters affecting pressure prediction, such as temperature 520, may be included or entered. The user may delete the relevant data point or add a data point using interface buttons 522 and 524, respectively.

The method 600 may continue with operation 604 in which the processor 704 determines a value of an operating parameter of the adhesive dispenser 100. The value may be based on at least one parameter received in operation 602. The values of the operating parameters may be such that a desired flow rate will be achieved in the adhesive dispensing system.

The method 600 may continue with operation 606 in which the processor 704 provides the operating parameters to the adhesive dispenser 100. In embodiments, the operating parameter may be a driving force pressure for the adhesive dispensing system. In an embodiment, the driving force pressure is determined based on the viscosity and parameters specific to the adhesive dispenser 100. For example, the driving force pressure may be determined according to equation (1) described earlier herein: m=g (F1 (p), F2 (μ) …), where m is the mass dispensed by the adhesive dispensing system, F1 is a function of pressure p, and F2 is a function of viscosity μ.

The viscosity μmay be determined based on direct or indirect measurements, polynomial fits, numerical regression, or a formula such as the andersoid formula for the liquid viscosity provided in formula (2).

As described earlier, the constants associated with equations (1) and (2) may be determined using a machine learning algorithm. The machine learning algorithm may minimize a loss function associated with at least one of the driving force pressure, mass, viscosity, and temperature. The machine learning algorithm may be based on models other than neural network models (e.g., non-neural network models and machine learning algorithms), or may be based on a hybrid between neural network models and other models (e.g., hybrid algorithms).

As described earlier herein, other systems based entirely on neural networks may be used to control the instructions for applying the adhesive. However, only neural network based systems require hundreds or even thousands of calibration points to be useful and a significant amount of learning must be done before any control of adhesive dispensing can be exercised. The exemplary embodiments described earlier herein may be based on other machine learning models, or on non-neural networks, or a combination of neural networks and other machine learning models.

FIG. 7 depicts a comparison between the performance of a neural network-based model and a learning model used in some example scenarios. For this comparison, a dataset of laboratory generated data was used. The dataset was sampled for a given number of data points and then divided into two groups, with 70% of the data being used to train the model and 30% remaining for testing the quality of the model after training. The split data is passed to each model so that the model analyzes the same experimental data in each case. The result is a system using a model according to an embodiment with a lower average root mean square error at each sample level, as shown at curve 630 (in contrast to curve 632 which shows a system using a neural network model), where the difference between curves 630 and 632 is particularly large at the lower sample level 634. As will be appreciated, because the average root mean square error is small even at low sample levels using the method according to embodiments, cost savings (which in turn reduces manufacturing, laboratory, and material costs) can be achieved by reducing the need for a large number of samples. The ability to obtain useful predictions with relatively few data points is a significant advance over existing neural network approaches.

Fig. 11 is a flow chart of a method 1000 for calibrating a dispenser system according to some embodiments. The operations of method 1000 may be performed by, for example, processor 704 (fig. 8).

Each dispenser system may differ in both subtle and significant aspects such that calibration data from one dispenser system cannot be used to calibrate another dispenser system. To calibrate (e.g., set) the dispenser system, a user may load a dispensable material (e.g., dispensable material 436) into the dispenser system and configure all of the associated portions of the dispenser system, including the plunger, air line, controller, static mixer, pinch tube or valve, tip, and other components. When ready, the user dispenses the dispensable material into the cup under different pressures and weighs the dispensed dispensable material. Each combination of assigned mass and pressure settings may form a calibration data point. However, various conditions may result in "bad" calibration data points. For example, if there is a bubble in the system, if the material sticks to the tip of the dispenser system, if the material from a previous dispenser attempt is included in the dispensing mass of another attempt, if the pressure or mass is incorrectly set or recorded, or if there is another source of error, the data from the calibration may not yield a useful model.

The method 1000 may correct a "bad" calibration data point by requesting a set of calibration data points, evaluating the quality of the set of calibration data points, removing "bad" or low quality calibration data points, and requesting additional calibration data points as needed.

The method 1000 may begin at operation 1002, where the processor 704 receives a plurality of calibration data points and one or more parameters of a dispenser component of a dispenser system. The user may be instructed to set a pressure gauge (e.g., dispenser pressure) to a particular pressure and record the mass dispensed. The instructions may also include an allocation time for each calibration data point. Alternatively, the processor may automatically set the pressure, dispense dispensable material, and record each calibration data point and associated parameters. Typical calibration methods may require hundreds or thousands of calibration data points. In contrast, the plurality of calibration data points of method 1000 may include at least 2 calibration data points to no more than 100 calibration data points or any suitable range therebetween. For example, the plurality of calibration data points may include a plurality of calibration data points ranging from at least 2 calibration data points, 5 calibration data points, 10 calibration data points, or 15 calibration data points to no more than 20 calibration data points, 40 calibration data points, 60 calibration data points, 80 calibration data points, or 100 calibration data points. Each of these calibration data points may include any suitable parameter. For example, each calibration data point of the plurality of calibration data points may include one or more of manufacturing lot information of the dispensable material, dispenser pressure, dispensing quality, dispensing time, ambient temperature, temperature of the dispensable material, and the like. In one embodiment, each calibration point of the plurality of calibration points includes a dispenser pressure, a dispensing time, and a mass of dispensable material dispensed.

The one or more parameters of the dispenser component may include any suitable parameters. For example, the one or more parameters of the dispenser component may include manufacturing lot information of the dispensable material, a model of the dispenser component, a type of dispensable material (e.g., adhesive, sealant, thermal paste, etc.), and so forth. One or more parameters may be received from a dispenser component, database, user, image, etc.

The method 1000 may continue with operation 1004 in which the processor selects one or more predetermined models based on one or more parameters of the dispenser component. In one or more embodiments, the one or more parameters include a manufacturing lot of dispensable material. One or more predetermined models may be retrieved based on the manufacturing lot of dispensable material. For example, each manufacturing lot or type of dispensable material can correspond to a given set of predetermined models.

The method 1000 may continue with operation 1006 in which the processor determines a calibration model based on the plurality of calibration data points and the one or more predetermined models. For example, the calibration model may include one or more settings corresponding to a predetermined model of the one or more predetermined models to which the modified set of calibration data points best corresponds based on the statistical method and modeling. In some examples, the plurality of calibration data points may fit or match one of the predetermined models, and the calibration model may be determined based on the plurality of calibration data points and the one or more predetermined models without modifying the plurality of calibration data points. However, in some examples, the plurality of calibration data points may not fit or match one of the predetermined models due to one or more outliers or an insufficient number of calibration data points. Thus, determining the calibration model may include generating a modified set of calibration data points that may have removed outliers of the plurality of calibration data points and/or have added additional calibration data points.

Determining the calibration model may include determining one or more outliers of the plurality of calibration data points based on the one or more models. Determining the one or more outliers may include one or more of any suitable technique. For example, determining one or more outliers may include using one or more statistical models or methods. In one embodiment, determining the one or more outliers may include regression analysis based on the plurality of calibration points and the one or more predetermined models. Additionally or alternatively, determining the one or more outliers may include determining one or more of the plurality of calibration points that differ from corresponding data points of the one or more predetermined models by more than a threshold. The threshold may be a percentage difference. In other words, the difference between the dispensing quality of the outlier calibration data point and the dispensing quality of the data point corresponding to the same dispenser pressure and time in one of the predetermined models may have a difference that exceeds a predetermined percentage of the dispensing quality of the data point of the predetermined model. The threshold may be determined based on a predetermined model.

Determining the calibration model may also include removing one or more outliers from the plurality of calibration data points to produce a modified set of calibration data points. The method 1000 may optionally include an operation in which the processor determines that the modified set of calibration data points includes less than a threshold number of data points. If the modified set of calibration data points includes less than the threshold number of data points, the modified set of calibration data points may not be sufficient to determine whether the calibration data points fit a particular one of the one or more predetermined models.

Accordingly, the method 1000 may further include an operation wherein the processor determines one or more dispenser pressures for one or more additional calibration data points based on the modified set of calibration data points and the one or more predetermined models. The one or more additional calibration data points may include a dispenser pressure between the dispenser pressures of the two missing or removed data points. Additionally or alternatively, the one or more additional calibration data points may include the same dispenser pressure as the one or more outliers.

The method 1000 may also include an operation wherein the processor requests one or more additional calibration data points using the display. Each of the one or more additional calibration data points may correspond to one of the one or more dispenser pressures. The method 1000 may also include an operation wherein the processor receives one or more additional calibration data points using the user interface and modifies the modified set of calibration data points to include the one or more additional calibration data points.

Thus, determining the calibration model may be based on the modified set of calibration data points and one or more predetermined models. For example, the calibration model may be determined after one or more outliers have been removed or after one or more additional calibration data points have been added to the modified set of data points.

The method 1000 may continue with operation 1008 in which the processor provides one or more settings for the dispenser system based on the calibration model. In embodiments, providing the one or more settings may include adjusting one or more settings of a dispenser component of the dispenser system. In other words, the processor may adjust various settings of the dispenser system. Additionally or alternatively, the processor may present various calibration settings to the user using a display such as GUI 500.

Fig. 12 is a flow chart of a method 1100 for dispensing a dispensable material (e.g., dispensable material 436) according to some embodiments. The operations of method 1100 may be performed by, for example, processor 704 (fig. 8).

The method 1100 may begin at operation 1102, where the processor 704 receives at least one parameter associated with the dispensable material dispenser 100 (fig. 1). At least one parameter may be received from a dispenser component, database, user, image, etc. In some embodiments, at least one parameter relates to the viscosity of the dispensable material in the dispensable material dispenser 100. In some embodiments, the at least one parameter may relate to a density of the dispensable material in the dispensable material dispenser 100. In some embodiments, operation 1102 may include receiving at least one feedback parameter of the dispensable material. The process parameters may be received from user inputs or sensors. In some examples, the feedback parameters may include image data or may be based on image data or based on quality data. The quality data may indicate an amount of dispensable material dispensed. For example, mass data and density of the dispensable material can be used to determine the volume of dispensable material dispensed. Accordingly, the volumetric flow rate of the dispensable material can be determined and/or monitored based on the mass data and the density of the dispensable material. In an exemplary embodiment, the quality feedback data may be used to determine whether the amount of dispensable material dispensed is within a target range of a desired amount of dispensable material.

The method 1100 may continue with operation 1104, where the processor 704 determines a value of an operating parameter of the dispensable material dispenser 100. The value may be based on at least one parameter received in operation 1102. The values of the operating parameters may be such that a desired flow rate will be achieved in the dispensable material dispensing system.

The method 1100 may continue with operation 1106, where the processor 704 provides operating parameters. In embodiments, the operating parameters may be provided to the dispensable material dispenser 100. In other embodiments, the operating parameters may be provided to the human-machine interface 404. In embodiments, the operating parameter may be a driving force pressure for the dispensable material dispensing system. In an embodiment, the driving force pressure is determined based on the viscosity and parameters specific to the dispensable material dispenser 100 as described earlier herein.

The method 1100 may continue with operation 1108, where the processor receives at least one process parameter including a temperature of the dispensable material. The at least one process parameter may also include, for example, conductivity, viscosity, weight, mass, or other parameters of the dispensable material (e.g., adhesive, sealant, thermal paste, etc.). At least one process parameter may be received from one or more sensors. For example, the temperature of the dispensable material can be received from the temperature sensor 910 of FIG. 10. Probes 912 of temperature sensor 910 may be disposed or disposed in the fluid path of the dispensable material, allowing temperature sensor 910 to sense or measure the temperature of the dispensable material.

The method 1100 may continue with operation 1110 in which the processor adjusts a value of an operating parameter of the dispensable material dispensing system based on the temperature of the dispensable material. The viscosity of the dispensable material can change as the temperature of the dispensable material changes. Thus, adjusting the value of the operating parameter (e.g., the driving force pressure) may achieve a more consistent flow rate of dispensable material. Additional process parameters may affect the viscosity and flow rate of the dispensable material. Such process parameters may be sensed using sensor 910 or any other suitable sensor or device. For example, the sensor 910 may also include a conductivity sensor to sense the conductivity of the dispensable material. The method 1100 may also include the processor receiving the sensed conductivity of the dispensable material from the conductivity sensor. Further, the method 1100 may include determining a cure state of the dispensable material based on the sensed temperature of the dispensable material and the sensed conductivity of the dispensable material; and adjusting a value of an operating parameter of the dispensable material dispensing system based on the sensed temperature and the curing state.

Once the dispensable material has been mixed during the dispensing process, the dispensable material can begin to cure before the dispensable material has been dispensed. The viscosity of the dispensable material can change as the dispensable material cures. In addition to temperature and conductivity, the cure state may also be determined based on the time after initial mixing (e.g., residence time in the static mixer and longer). With known cure conditions, the viscosity of the dispensable material can be predicted and the effect on the flow rate can be calculated. This information may be used to adjust an operating parameter (e.g., driving force pressure) to maintain a more constant flow rate of dispensable material 436. Additionally, the method 1100 may include initiating a purge in the event that the viscosity of the dispensable material exceeds a threshold viscosity level. The threshold viscosity level may be set or determined based on operational limitations of the dispensable material dispensing system, parameters set by a user, or parameters of the current dispensing job or process.

The method 1100 may continue with operation 1112, where the processor provides the operating parameters. In embodiments, the processor may provide operating parameters to one or more dispenser components. In other embodiments, the processor may provide the operating parameters to the human interface device. Thus, the flow rate of dispensable material can be maintained by continuously or periodically adjusting an operating parameter and providing the adjusted operating parameter to one or more dispenser components. For example, the driving force pressure may be continuously adjusted to account for curing, temperature, and/or viscosity changes of the dispensable material. Further, the adjusted operating parameters may be provided to one or more dispenser components at any time that the operating parameters are adjusted. The adjusted operating parameters may be provided using wired or wireless communications as described herein. In addition, the processor may initiate the purge by communicating with a motion controller configured to move or direct the dispenser to the purge vessel.

The method 1100 may also include an operation for determining parameters of the cleaning of the dispensable material. The method 1100 may include an operation in which the processor determines a maximum idle time or a purge time for one or more dispenser components based on at least one parameter and at least one process parameter. The method 1100 may also include an operation in which the processor provides a maximum idle time or a flush time. In embodiments, one or more dispenser components may be provided with a maximum idle time or a purge time. Thus, the dispenser component may initiate automatic cleaning based on the maximum idle time and/or the cleaning time. In embodiments, a maximum idle time or a washing time may be provided to the human-machine interface. Thus, the maximum idle time or the washing time may be displayed to the user.

Many factors can affect the shelf life of the dual portion dispensable material. Such factors may include, for example, temperature, aging of the material, density, initial manufacturing viscosity, and other potential factors. Such factors may help modify cure rate kinetics and, in turn, change the amount of time that dispensable material can stagnate in the dispenser before it may need to be purged. An additional factor that may contribute to the maximum idle time is the allocation rate. The period of time that the dispenser part is determined to be idle may begin after the last dispensing of dispensable material. If the active period (e.g., dispensing) includes a high flow rate dispensing attempt with little time in between, the dispensable material can be relatively fresh when dispensing is stopped. However, if the flow rate is low and/or the dispensing attempts are spaced apart for a longer period of time, the dispensable material can partially solidify when the dispensing is stopped. Thus, the maximum idle time and/or the purge time should be different for each of these cases. By determining the maximum idle time and/or the purge time based on at least one parameter and at least one process parameter, the maximum idle time and/or the purge time may be tailored to the temperature of the dispensable material, the age of the dispensable material, the density of the dispensable material, the viscosity of the dispensable material, the dispensing rate, the flow rate, the time between dispensing points, and the like.

Additionally, the method 1100 may consider safety factors and process control factors in determining the maximum idle time and the purge time. The method 1100 may also include an operation in which the processor receives one or more user inputs including a safety factor, a process control factor, and a waste factor, and determines a maximum idle time or a purge time based on the one or more user inputs, the at least one parameter, and the at least one process parameter. User input may be received using a user interface (e.g., GUI 500). In one example, a user may select or provide a safety factor such that the dispensable material does not prematurely cure if there is variability in the dispensing process. In other words, when a safety factor is provided, the maximum idle time may be determined based on the cure time of the dispensable material. In another example, a user may want to have tight control over the flow rate at the expense of waste of dispensable material. In other words, when a control factor is provided, the maximum idle time may be determined based on the variability of flow rates that may occur before the dispensable material cures. In yet another example, the user may select a waste factor that indicates a low amount of waste such that as little of the dispensable material is wasted as possible. When multiple factors are entered, particular values of these factors may be balanced against each other to determine the maximum idle time and the purge time.

The display may also allow the user to see how safety, process control, and waste factors affect the maximum idle time and the purge time, and vice versa. In some embodiments, the user may set the safety factor, the process control factor, and the waste factor to parameter values. The parameter values received from the user may be incorporated into an algorithm for determining the maximum idle time and the purge time.

Computer device

The systems, methods, and apparatus may implement the embodiments using a processor in firmware or software, remotely or locally to an operator process, or in a cloud or edge computing device, as will be described in more detail later herein. The machine learning may be distributed among several different devices and implemented wholly or partially within the adhesive dispensing device itself. For example, some identification processes may be performed by the adhesive dispenser 100 (fig. 1), and input from the adhesive dispenser may be provided to a local or remote device to formulate predictions regarding dispenser settings. Accordingly, the devices and circuitry of the adhesive dispenser 100, the data component 402, the human-machine interface 404, and the dispenser component 406 (fig. 4), as well as other components, may execute or partially execute on a computing system (e.g., an edge computing node). Fig. 8 depicts an edge computing node according to some embodiments.

In the simplified example depicted in fig. 8, an edge computing node 700 (e.g., an apparatus) includes a computing engine (also referred to herein as "computing circuitry") 702, an input/output (I/O) subsystem 708, a data storage device 710, a communication circuitry subsystem 712, and optionally one or more peripheral devices 714. In other examples, the respective computing device may include other or additional components, such as those typically found in a computer (e.g., a display, a peripheral device, etc.). Additionally, in some examples, one or more exemplary components may be incorporated into or otherwise form a portion of another component.

The computing node 700 may be embodied as any type of engine, device, or collection of devices capable of performing various computing functions. In some examples, computing node 700 may be embodied as a single device, such as an integrated circuit, an embedded system, a Field Programmable Gate Array (FPGA), a system on a chip (SOC), or other integrated system or device. In the illustrative example, computing node 700 includes or is embodied as a processor 704 and a memory 706. The processor 704 may be embodied as any type of processor capable of performing the functions described herein (e.g., executing an application). For example, the processor 704 may be embodied as a multi-core processor, microcontroller, or other processor or processing/control circuit. In some examples, the processor 704 may be embodied as, include, or be coupled with an FPGA, an application-specific integrated circuit (ASIC), reconfigurable hardware or hardware circuitry, or other special purpose hardware to facilitate the performance of the functions described herein.

The memory 706 may be embodied as any type of volatile (e.g., dynamic Random Access Memory (DRAM), etc.) or non-volatile memory or data storage device capable of performing the functions described herein. The volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory can include various types of Random Access Memory (RAM), such as DRAM or Static Random Access Memory (SRAM). One particular type of DRAM that may be used in a memory module is Synchronous Dynamic Random Access Memory (SDRAM).

In one example, the memory devices are block addressable memory devices, such as those based on NAND or NOR technology. In some examples, all or a portion of the memory 706 may be integrated into the processor 704. The memory 706 may store various software and data used during operation, such as one or more applications, data operated on by applications, libraries, and drivers.

The computing circuitry 702 is communicatively coupled with other components of the computing node 700 via the I/O subsystem 708, which may be embodied as circuitry or components for facilitating input/output operations with the computing circuitry 702 (e.g., with the processor 704 or main memory 706) and other components of the computing circuitry 702. For example, the I/O subsystem 708 may be embodied as or otherwise include a memory controller hub, an input/output control hub, an integrated sensor hub, a firmware device, a communication link (e.g., point-to-point link, bus link, wire, cable, light guide, printed circuit board trace, etc.), or other components and subsystems to facilitate input/output operations. In some examples, the I/O subsystem 708 may form part of a system on a chip (SoC) and be incorporated into the computing circuit 702 along with one or more of the processor 704, the memory 706, and other components of the computing circuit 702. The I/O subsystem 708 may receive input data 707 from other components of fig. 4 (e.g., sensors 428, sensors 438, etc.) and provide predictions and control 709 to other components of fig. 4 (e.g., the allocator component 406).

The one or more exemplary data storage devices 710 may be embodied as any type of device configured for short-term or long-term storage of data, such as memory devices and circuits, memory cards, hard disk drives, solid state drives, or other data storage devices. The separate data storage device 710 may include a system partition that stores data and firmware code for the data storage device 710. The separate data storage device 710 may also include one or more operating system partitions that store data files and executable files for the operating system depending on, for example, the type of computing node 700.

The communication circuit 712 may be embodied as any communication circuit, device, or collection thereof capable of enabling communication over a network between the computing circuit 702 and another computing device (e.g., an edge gateway implementing an edge computing system). The communication circuit 712 may be configured to use any one or more communication technologies (e.g., wired or wireless communication) and associated protocols (e.g., cellular networking protocols such as 3GPP 4G or 5G standards, wireless local area network protocols such as IEEE 802.11-Wireless wide area network protocol, ethernet, +.>Bluetooth low energy, ioT protocols such as IEEE 802.15.4 or +. >Low Power Wide Area Network (LPWAN), ultra wideband, or Low Power Wide Area (LPWA) protocols, etc.).

The exemplary communication circuit 712 includes a Network Interface Controller (NIC) 720.NIC 720 may be embodied as one or more add-on boards, daughter cards, network interface cards, controller chips, chipsets, or other devices that may be used by computing node 700 to connect with another computing device (e.g., an edge gateway node). In some examples, NIC 720 may be embodied as part of a system on a chip (SoC) that includes one or more processors or included on a multi-chip package that also contains one or more processors. In some examples, NIC 720 may include a local processor (not shown) or local memory (not shown) that are both local to NIC 720. In such examples, a local processor of NIC 720 may be capable of performing one or more functions of computing circuit 702 described herein. Additionally or alternatively, in such examples, the local memory of NIC 720 may be integrated into one or more components of the client computing node at a board level, socket level, chip level, or other level.

Additionally, in some examples, the respective computing node 700 may include one or more peripheral devices 714. Such peripheral devices 714 may include any type of peripheral device found in a computing device or server, such as an audio input device, a display, other input/output devices, interface devices, or other peripheral devices, depending on the particular type of computing node 700. In further examples, the computing nodes 700 may be embodied by respective edge computing nodes (whether clients, gateways, or aggregation nodes) in an edge computing system, or by similar forms of appliances, computers, subsystems, circuitry, or other components.

In a more detailed example, fig. 9 shows a block diagram of an example of components that may be present in edge computing node 850 for implementing the techniques (e.g., operations, processes, methods, and methodologies) described herein. When implemented as or as part of a computing device (e.g., as a computer, mobile device, server, smart sensor, control system, etc.), the edge computing node 850 provides a closer view of the corresponding components of node 700. Edge computing node 850 may include any combination of hardware or logic components referenced herein, and may include or be coupled with any device that may be used with an edge communication network or a combination of such networks. The components may be implemented as Integrated Circuits (ICs), portions thereof, discrete electronic devices, or other modules, sets of instructions, programmable logic or algorithms, hardware accelerators, software, firmware, or combinations thereof adapted in edge computing node 850, or as components otherwise incorporated within the chassis of a larger system.

Edge computing node 850 may include processing circuitry in the form of a processor 852, which may be a microprocessor, a multi-core processor, a multi-threaded processor, an ultra-low voltage processor, an embedded processor, or other known processing elements. Processor 852 may be part of a system on a chip (SoC) in which processor 852 and other components are formed into a single integrated circuit or a single package. Processor 852 and accompanying circuitry may be provided in a single-socket form factor, a multi-socket form factor, or various other formats, including in a limited hardware configuration or a configuration that includes fewer than all of the elements shown in fig. 9.

Processor 852 can communicate with system memory 854 via interconnect 856 (e.g., a bus). Any number of memory devices may be used to provide a quantitative amount of system memory. By way of example, the memory 854 may be Random Access Memory (RAM) designed according to the joint electronics engineering institute of electrical and electronics engineers (JEDEC). In various implementations, the individual memory devices may have any number of different package types, such as Single Die Packages (SDPs), dual Die Packages (DDPs), or quad die packages (Q17 Ps). In some examples, these devices may be soldered directly to the motherboard to provide a lower profile solution, while in other examples, the devices are configured as one or more memory modules that are in turn coupled to the motherboard by a given connector. Any number of other memory implementations may be used, such as other types of memory modules, for example, heterogeneous Dual Inline Memory Modules (DIMMs), including but not limited to microDIMMs or MiniDIMMs.

To provide persistent storage of information, such as data, applications, operating systems, etc., storage 858 may also be coupled to processor 852 via interconnect 856. In one example, the storage 858 may be implemented via a Solid State Disk Drive (SSDD). Other devices that may be used for storage 858 include flash memory cards, such as Secure Digital (SD) cards, microSD cards, extreme digital (XD) picture cards, and the like, as well as Universal Serial Bus (USB) flash drives.

The components may communicate via the interconnect 856. The interconnect 856 may comprise any number of technologies including Industry Standard Architecture (ISA), enhanced ISA (EISA), peripheral Component Interconnect (PCI), extended peripheral component interconnect (PCIx), PCI express (PCIe), or any number of other technologies. The interconnect 856 may be a dedicated bus used, for example, in SoC-based systems. Other bus systems may be included such as an inter-integrated circuit (I2C) interface, a Serial Peripheral Interface (SPI) interface, a point-to-point interface, a dedicated bus, a power bus, and the like.

An interconnect 856 may couple the processor 852 with the transceiver 866 for communication with the connected edge device 862. The connected edge device 862 may include other elements depicted in fig. 9 or portions of other elements, or other elements of the manufacturing system used by an operator (remote from or local to the tape automated system). The transceiver 866 may use any number of frequencies and protocols, such as 2.4 gigahertz (GHz) transmissions under the IEEE 802.15.4 standard, using as bySpecial interest group definition +.>Low Energy (BLE) standard, or +.>Standard, etc. Any number of radios configured for a particular wireless communication protocol may be used for connection with the connected edge device 862. For example, a Wireless Local Area Network (WLAN) unit may be used to implement +_ according to the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard >And (5) communication. Further, wireless wide area communications, e.g., according to cellular or other wireless wide area protocols, may occur via a Wireless Wide Area Network (WWAN) unit.

The wireless network transceiver 866 (or transceivers) may use multiple standards or radio communications for communications at different ranges. For example, edge computing node 850 may communicate with neighboring devices within about 10 meters, for example, using a Bluetooth Low Energy (BLE) based local transceiver or another low power radio to save power. More distant connected edge devices 862 (e.g., within about 50 meters) may passOr other intermediate power radio. The two communication techniques may be performed at different power levels on a single radio, or may be performed at separate transceivers (e.g., a local transceiver using BLE and useIs performed on a separate mesh transceiver).

A wireless network transceiver 866 (e.g., a radio transceiver) may be included to communicate with devices or services in the edge cloud 895 via a local area network or wide area network protocol. The wireless network transceiver 866 may be a Low Power Wide Area (LPWA) transceiver compliant with the IEEE 802.15.4 or IEEE 802.15.4g standard, or the like. Edge computing node 850 may use LoRaWAN developed by Semtech and LoRa alliance ^TM (remote wide area network) communicates over a wide area. The techniques described herein are not limited to these techniques, but may be used with any number of other cloud transceivers and other techniques that enable remote, low bandwidth communications, such as Sigfox. In addition, other communication techniques described in the IEEE 802.15.4e specification, such as time slot channel hopping, may be used.

In addition to the systems mentioned for wireless network transceiver 866, any number of other radios and protocols may be used, as described herein. For example, transceiver 866 may comprise a cellular transceiver that uses spread spectrum (SPA/SAS) communications to enable high-speed communications. In addition, any number of other may be usedProtocols, such as for medium speed communication and providing network communicationA network. The transceiver 866 may include a radio compatible with any number of 3GPP (third generation partnership project) specifications, such as Long Term Evolution (LTE) and 5 th generation (5G) communication systems. A Network Interface Controller (NIC) 768 may be included to provide wired communications to nodes of the edge cloud 895 or to other devices such as connected edge devices 862 (e.g., operating in a grid). The wired communication may provide an ethernet connection or may be based on other types of networks, such as a Controller Area Network (CAN), a Local Interconnect Network (LIN), deviceNet, controlNet, a data highway+, PROFIBUS or PROFINET, etc. Additional NICs 768 may be included to enable connection with a second network, for example, a first NIC 768 providing communication to the cloud over ethernet and a second NIC 768 providing communication to other devices over another type of network. Ultra-wideband sensors and transmitters may be used to facilitate accurate positioning of the band relative to defined transmitter beacons, as well as communications such as data transmissions.

The applicable communication circuitry used by a device may include or be embodied by any one or more of components 864, 866, 868, or 870 given the various types of applicable communications from the device to another component or network. Thus, in various examples, suitable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communication circuitry.

Edge compute nodes 850 may include or be coupled with acceleration circuitry 864, which may be embodied by one or more Artificial Intelligence (AI) accelerators, neural compute sticks, neuromorphic hardware, FPGAs, arrangements of GPUs, arrangements of Data Processing Units (DPUs) or Infrastructure Processing Units (IPUs), one or more socs, one or more CPUs, one or more digital signal processors, special purpose ASICs, or other forms of special purpose processors or circuitry designed to accomplish one or more special tasks. These tasks may include AI processing (including machine learning, training, reasoning, and classification operations), visual data processing, network data processing, object detection, rule analysis, and the like.

The interconnect 856 may couple the processor 852 with a sensor hub or external interface 870 for connecting additional devices or subsystems. The device may include sensors 872 such as accelerometers, level sensors, flow sensors, optical light sensors, camera sensors, temperature sensors or meters, global navigation system (e.g., GPS) sensors, pressure sensors, barometric pressure sensors, any sensor for detecting the condition of a tape or other adhesive, primer, substrate, etc. These sensors may be directly connected to the computing device or remotely located as part of the various manufacturing modules. Hub or interface 870 may also be used to connect edge computing node 850 with an actuator 874, such as a power switch, valve actuator, audible sound generator, visual warning device, and the like. These actuators may be directly connected to the computing device or remotely located as part of the various manufacturing modules.

In some optional examples, various input/output (I/O) devices may exist within or be connected to edge computing node 850. For example, a display or other output device 884 may be included to show information, such as sensor readings or actuator positions. An input device 886, such as a touch screen or keypad, may be included to accept input. Output device 884 may include any number of forms of audio or visual display, including simple visual outputs such as binary status indicators (e.g., light Emitting Diodes (LEDs)) and multi-character visual outputs, or more complex outputs such as display screens (e.g., liquid Crystal Display (LCD) screens) in which the output of characters, graphics, multimedia objects, etc. is generated or generated from operation of edge computing node 850. In the context of the present system, display or console hardware may be used to provide output of the edge computing system and to receive input of the edge computing system; managing components or services of an edge computing system; identifying a state of an edge computing component or service; or perform any other number of management or administration functions or service instances. These various input/output devices may be connected directly to the computing device or remotely located as part of the various manufacturing modules. In an example, notifications may be provided to more than one device at the same time, e.g., a user may view notifications on separate modules of system 400. At the same time or nearly the same time, notifications may be provided to a user's smart phone or other device (such as a tablet or computer) or to a stand-alone device or a device separate from the user's personal equipment based on proximity or other criteria.

Battery 876 may power edge computing node 850, but in examples where edge computing node 850 is installed in a fixed location, it may have a power source coupled to the grid, or the battery may be used as a backup or for temporary capability. The battery 876 may be a lithium ion battery, or a metal-air battery, such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, or the like.

Battery monitor/charger 878 can be included in edge computing node 850 to track the state of charge (SoCh) of battery 876, if included. The battery monitor/charger 878 can be used to monitor other parameters of the battery 876 to provide fault predictions, such as state of health (SoH) and state of function (SoF) of the battery 876. The battery monitor/charger 878 can communicate information about the battery 876 to the processor 852 via the interconnect 856. The battery monitor/charger 878 can also include an analog-to-digital (ADC) converter that enables the processor 852 to directly monitor the voltage of the battery 876 or the current from the battery 876.

A power block 880 or other power source coupled to the power grid may be coupled to the battery monitor/charger 878 to charge the battery 876. In some examples, power block 880 may be replaced with a wireless power receiver to wirelessly obtain power, for example, through a loop antenna in edge computing node 850. The particular charging circuit may be selected based on the size of the battery 876 and thus the current required.

The storage device 858 may include instructions 882 in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions 882 are shown as blocks of code included in the memory 854 and the storage 858, it can be appreciated that any of the blocks of code can be replaced with hardwired circuitry, for example, built into an Application Specific Integrated Circuit (ASIC).

In one example, instructions 882 provided via memory 854, storage 858, or processor 852 may be embodied as a non-transitory machine-readable medium 860 comprising code for directing processor 852 to perform electronic operations in edge computing node 850. The processor 852 may access the non-transitory machine-readable medium 860 via the interconnect 856. For example, the non-transitory machine-readable medium 860 may be embodied by a device described with respect to the storage 858, or may include a particular storage unit such as an optical disk, a flash drive, or any number of other hardware devices. The non-transitory machine-readable medium 860 may include instructions for directing the processor 852 to perform a particular sequence of actions or flow, e.g., as described with respect to the flowcharts and block diagrams of operations and functionality described above. The terms "machine-readable medium" and "computer-readable medium" as used herein are interchangeable.

Moreover, in particular examples, instructions 882 on processor 852 (alone or in combination with instructions 882 of machine-readable medium 860) may construct an execution or operation of Trusted Execution Environment (TEE) 890. In one example, TEE 890 operates as a protected area accessible to processor 852 for secure execution of instructions and secure access of data.

In further examples, machine-readable media also include any tangible medium capable of storing, encoding or carrying instructions for execution by a machine and that cause the machine to perform any one or more of the methods of the present disclosure, or any tangible medium capable of storing, encoding or carrying data structures utilized by or associated with such instructions. "machine-readable medium" can thus include, but is not limited to, solid-state memories, as well as optical and magnetic media. Specific examples of machine-readable media include non-volatile memory including, but not limited to, for example: semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disk; CD-ROM and DVD-ROM disks. The instructions embodied by the machine-readable medium may also be transmitted or received over a communications network using a transmission medium via a network interface device using any of a number of transmission protocols, such as the hypertext transfer protocol (HTTP).

The machine-readable medium may be provided by a storage device or other apparatus capable of hosting data in a non-transitory format. In one example, information stored or otherwise provided on a machine-readable medium may represent instructions, such as the instructions themselves or the format from which the instructions may be derived. The format from which the instructions may be derived may include source code, encoded instructions (e.g., in compressed or encrypted form), packaged instructions (e.g., split into multiple packages), and so forth. Information representing instructions in a machine-readable medium may be processed by processing circuitry into instructions for implementing any of the operations discussed herein. For example, deriving instructions from information (e.g., processing by processing circuitry) may include: compilation (e.g., from source code, object code, etc.), interpretation, loading, organization (e.g., linked dynamically or statically), encoding, decoding, encrypting, unencrypted, packaged, unpackaged, or otherwise manipulating information into instructions.

In one example, the derivation of the instructions may include compilation, or interpretation of information (e.g., by a processing circuit) to create the instructions from some intermediate or pre-processing format provided by a machine-readable medium. When provided in multiple parts, the information may be combined, unpacked, and modified to create instructions. For example, the information may be in multiple compressed source code packages (or object code, or binary executable code, etc.) on one or several remote servers. The source code package may be encrypted when transmitted over a network and decrypted, decompressed, assembled (e.g., linked) if necessary, and compiled or interpreted (e.g., into a library, a separate executable file, etc.) at the local machine and executed by the local machine.

In this document, the terms "comprise" and variants thereof have no limiting meaning where these terms appear in the description and claims. Such terms will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. By "consisting of … …" is meant including and limited to what follows the phrase "consisting of … …". Thus, the phrase "consisting of … …" indicates that the listed elements are required or mandatory and that no other elements may be present. "consisting essentially of … …" is intended to include any element listed after the phrase and is limited to other elements that do not interfere with or contribute to the activity or effect specified for the listed elements in this disclosure. Thus, the phrase "consisting essentially of … …" indicates that the listed elements are desired or mandatory, but that other elements are optional and may or may not be present, depending on whether they substantially affect the activity or effect of the listed elements. Any element or combination of elements in the description recited in an open language (e.g., including and derivatives thereof) is intended to be additionally recited in a closed language (e.g., consisting essentially of … … and derivatives thereof) and in a partially closed language (e.g., consisting essentially of … … and derivatives thereof).

The words "preferred" and "preferably" refer to embodiments of the present disclosure that may provide certain benefits in certain circumstances. However, other embodiments may be preferred under the same or other circumstances. Furthermore, the recitation of one or more preferred embodiments does not imply that other claims are not useful, and is not intended to exclude other embodiments from the scope of the disclosure.

In this application, terms such as "a," "an," and "the" are not intended to refer to only a single entity, but rather include the general class of which specific examples are available for illustration. The terms "a," an, "" the, "and" said "are used interchangeably with the term" at least one. The phrases "at least one of … …" and "at least one of … …" inclusive "of the list refer to any one of the items in the list as well as any combination of two or more items in the list.

As used herein, the term "or" is generally employed in its sense including "and/or" unless the context clearly dictates otherwise.

The term "and/or" means one or all of the listed elements, or a combination of any two or more of the listed elements.

Also herein, all numerical values are assumed to be modified by the term "about" and, in certain embodiments, preferably by the term "precisely". As used herein, with respect to a measured quantity, the term "about" refers to a deviation in the measured quantity that is commensurate with the objective of the measurement and the accuracy of the measurement device used, as would be expected by a skilled artisan taking the measurement with some care. Herein, "at most" a number (e.g., at most 50) includes the number (e.g., 50).

In addition, herein, recitation of numerical ranges by endpoints includes all numbers subsumed within that range as well as the endpoints (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.) and any subrange (e.g., 1 to 5 includes 1 to 4, 1 to 3, 2 to 4, etc.).

As used herein, the term "room temperature" refers to a temperature of 20 ℃ to 25 ℃.

The terms "in a range" or "within a range" (and similar expressions) include the endpoints of the range.

Reference throughout this specification to "one embodiment," "an embodiment," "certain embodiments," or "some embodiments," etc., means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of such phrases in various places throughout this specification are not necessarily referring to the same embodiment in the present disclosure. Furthermore, the features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

Examples

These examples are for illustrative purposes only and are not intended to unduly limit the scope of the claims herein. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the disclosure are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

All chemicals used in the examples were obtained from the mentioned suppliers unless otherwise indicated. The adhesives used in exemplary embodiments may include the following.

Adhesive agent

In general, structural adhesives can be divided into two broad categories: one-part adhesives and two-part adhesives. For a one-part adhesive, a single composition contains all the materials necessary to obtain the final cured adhesive. Such adhesives are typically applied to the substrates to be bonded and exposed to elevated temperatures (e.g., temperatures greater than 50 ℃) to cure the adhesive. In contrast, a two-part adhesive comprises two components. The first component (commonly referred to as the "base resin component") comprises a curable resin. The second component (commonly referred to as the "accelerator component") comprises a curing agent and a catalyst. A variety of other additives may be included in one or both components.

Other adhesives for use herein may include hot melt adhesives, such as one-component moisture-cure hot melt adhesives. The product package is characterized by extremely high heat resistance compared to conventional thermoplastic PO hot melts. In some examples, polyurethane (PUR) hot melts containing isocyanate for use in chemical crosslinking processes are used. In other examples, a Polyolefin (POR) hot melt using silane as the reactive component is used herein. Two-component adhesives are 100% solids systems that achieve the storage stability of these two-component adhesives by separating the reactive components. These two-component adhesives are supplied as "resins" and "hardeners" in separate containers. It is important to maintain a specified ratio of resin and hardener to obtain the desired cure and physical properties of the adhesive. The two components are mixed together to form an adhesive only a short time before application and cure occurs at room temperature. Since the reaction generally begins immediately after mixing the two components, the viscosity of the mixed adhesive increases over time until the adhesive can no longer be applied to the substrate or the bond strength decreases due to reduced wetting of the substrate. The formulation is available at various cure speeds to provide various working times (working times) after mixing and strength enhancement rates after bonding. Final strength is achieved minutes to weeks after bonding, depending on the formulation. The adhesive must be removed from the mixing and application equipment before curing has progressed to the point where the adhesive is no longer soluble. Depending on the working life, the two-component adhesive may be applied by a mud shovel, bead or tape, sprayer or roller. The assembly is typically secured until sufficient strength is obtained to allow further processing. If a faster cure rate (strength enhancement) is desired, heat may be used to accelerate the cure. This is particularly useful when parts need to be processed more quickly after bonding or additional working time is needed but slower strength enhancement rates cannot be accommodated. When cured, two-component adhesives are generally tough and rigid, with good temperature and chemical resistance.

For small applications, the two-component adhesive may be mixed and applied manually. However, this requires great care to ensure proper ratios of the components and thorough mixing to ensure proper cure and performance. Manual mixing also typically involves considerable wastage. Accordingly, adhesive suppliers have developed packages that allow the components to remain stored separately and provide a means for dispensing mixed adhesives, such as side-by-side syringes, concentric cartridges. The package is typically inserted into the applicator handle and the adhesive is dispensed through a disposable mixing nozzle. Proper ratios of the components are maintained by the design of the package and proper mixing is ensured by the use of mixing nozzles. The adhesive may be dispensed from these packages multiple times, provided that the time between uses does not exceed the shelf life of the adhesive. If the working period is exceeded, a new mixing nozzle must be used. For larger applications, metering and mixing equipment may be used to meter, mix and dispense adhesive packaged in containers ranging from quarts to barrels.

The two-part adhesive is composed of a resin and hardener component, which cure once the two components are mixed together. As long as the two components are separated from each other, they remain stable upon storage. Two-part adhesives are typically designed to be dispensed in a set ratio to obtain desired properties from a specifically formulated adhesive; the usual ratios include 10:1, 1:1, 2:1, etc. Once the two components are mixed, the reaction between the two components typically begins immediately and the viscosity increases until the two components are no longer available. As described above, this can be described as a working period, an open time, and a shelf life. Once cured, the two-component adhesive is tough and rigid, with good temperature and chemical resistance.

Epoxy resin adhesive

As described earlier herein, the epoxy adhesive of the exemplary embodiments may include a one-part adhesive and a two-part adhesive. The one-part epoxy adhesive may include a resin. Similar to the one-part congener of the one-part epoxy adhesive, the two-part epoxy is also formulated from an epoxy. Two-part epoxy resins are widely used in structural applications and for bonding many materials including, for example: metal, plastic, fiber Reinforced Plastic (FRP), glass and some rubber. Two-part epoxies typically cure rapidly and provide relatively rigid bonds. Some compositions may generally be brittle, but toughening agents and elastomers may be used to reduce this tendency.

The two-part structural epoxy resin adhesive is composed of a resin (part a or part 1) and a hardener (part B or part 2). Accelerators or chemical catalysts may accelerate the reaction between the resin and hardener.

The two-part epoxy resin can be cured at room temperature and therefore does not necessarily require heat when it is used. The two-part epoxy typically reaches handling strength between five minutes and eight hours after mixing, depending on the curing agent. A chemical catalyst or heat may be applied to accelerate the reaction between the resin and hardener.

The resin on which all the epoxy resins are based is bisphenol a diglycidyl ether (DGEBA). Bisphenol a is prepared by reacting phenol with acetone under suitable conditions. "A" represents acetone, "phenyl" means phenolic groups, and "bis" means two. Bisphenol A is thus a product obtained by chemically combining two phenols with one acetone. Unreacted acetone and phenol are separated from bisphenol a and then reacted with a material called epichlorohydrin. This reaction binds two ("bis") glycidyl groups to the ends of bisphenol a molecules. The resulting product is a diglycidyl ether of bisphenol A, or a basic epoxy resin. It is these glycidyl groups that react with amine hydrogen atoms on the hardener to produce a cured epoxy resin. Unmodified liquid epoxy resins are very viscous and are not suitable for most uses except as very thick glues.

The most common chemical raw materials for making the curing or hardening agent for room temperature cured epoxy resins are polyamines. They are organic molecules containing two or more amine groups. Amine groups are structurally indistinguishable from ammonia, except that they are attached to an organic molecule. Like ammonia, amines are strongly basic. Because of this similarity, epoxy hardeners generally have an ammonia-like smell, most pronounced in the air space in the container immediately after opening the container. Epoxy hardeners are generally referred to as "part B".

A reactive amine group is a nitrogen atom with one or two hydrogen atoms attached to the nitrogen. These hydrogen atoms react with oxygen atoms from the glycidyl groups on the epoxy resin to form a cured resin, a highly crosslinked thermoset. The heat will soften but not melt the cured epoxy. The three-dimensional structure imparts excellent physical properties to the cured resin.

The final resin to hardener ratio is determined by taking into account the various molecular weights and densities involved. The proper ratio results in a "fully crosslinked" thermoset. Changing the recommended ratio will leave unreacted oxygen atoms or hydrogen atoms, depending on which is in excess. The resulting cured resin will have lower strength because it is not fully crosslinked. Excess portion B results in increased moisture sensitivity in the cured epoxy resin and should generally be avoided.

Amine hardeners are not "catalysts". The catalyst promotes the reaction but does not become chemically part of the final product. Amine hardeners cooperate with the epoxy resin to greatly contribute to the final properties of the cured system. The curing time of the epoxy resin system depends on the reactivity of the amine hydrogen atoms. Although the attached organic molecule does not directly participate in the chemical reaction, the attached organic molecule does affect the ease with which the amine hydrogen atom leaves the nitrogen and reacts with the glycidyl oxygen atom. Thus, the cure time is set by the kinetics of the particular amine used in the hardener. The cure time of any given epoxy resin system can be modified only by adding accelerators to the accelerator-containing system or by changing the temperature and quality of the resin/hardener mixture. The addition of more hardener will not "accelerate the progress of the matter" and the addition of less hardener will not "slow the progress of the matter".

The epoxy curing reaction is exothermic. The rate at which the epoxy resin cures depends on the curing temperature. The higher the cure temperature, the faster the rate. The cure rate will vary by about half or twice for each 18°f (10 ℃) change in temperature. For example, if the epoxy system takes 3 hours at 70F to become tack-free, the epoxy system will become tack-free within 1.5 hours at 88F or within 6 hours at 52F. Everything related to the reaction speed follows this general rule. The initial temperature of the resin and hardener being mixed greatly affects the pot life and working time. For example, on a hot day, the two materials may be cooled prior to mixing in order to increase the working time.

The gel time of a resin is the time it takes for a given mass to harden while remaining in a compact volume. The gel time depends on the initial temperature of the substance and follows the above rules. One hundred grams (about three fluid ounces) of silver tipped laminate epoxy with a fast hardener (as an illustrative example) will set within 25 minutes of 77°f; gel time at 60℃F. Was about 50 minutes. If the same mass is spread over 4 square feet at 77°f, the gel time will slightly exceed three hours. In addition to temperature sensitivity, cure time is also surface area/mass sensitive.

As the reaction proceeds, the reaction is exothermic. If the generated heat is immediately dissipated to the environment (as occurs in a film), the temperature of the cured resin does not rise and the reaction speed proceeds at a uniform speed. If the resin is confined (e.g., in a mixing tank), the exothermic reaction increases the temperature of the mixture, thereby accelerating the reaction.

The working time or Working Life (WL) of the epoxy formulation is about 75% of the gel time of the tank geometry. The working time or WL may be extended by increasing the surface area, working with less mass, or cooling the resin and hardener prior to mixing. The material remaining in the tank will increase in absolute viscosity (e.g., measured at 75°f) due to polymerization, but initially decrease in apparent viscosity due to heating. The material left in the can for up to 75% of the gel time may appear quite thin (due to heating), but actually will be quite thick when cooled to room temperature. An experienced user mixes the batch material that will be applied almost immediately, or increases the surface area to slow down the reaction.

Although the cure rate of the epoxy resin is dependent on temperature, the cure mechanism is independent of temperature. The reaction proceeds most rapidly in the liquid state. As curing proceeds, the system changes from a liquid to a coherent, viscous, soft gel. After gelation, the reaction rate slows down as the hardness increases. The chemical reaction proceeds more slowly in the solid state. From a soft adhesive gel, the system becomes stiffer, slowly losing its cohesiveness. Over time, the system becomes tack-free and continues to become harder and stronger.

At normal temperatures, the system will reach about 60% to 80% of the ultimate strength after 24 hours. The curing then proceeds slowly over the next few weeks, eventually reaching the point where no further curing occurs without a significant increase in temperature. However, for most purposes, room temperature cure systems can be considered to be fully cured after 72 hours at 77°f. High modulus systems (like two-phase epoxy resins) must be post-cured at elevated temperatures to achieve complete cure.

If the particular system being used provides this choice, it is generally more efficient for current applications to work with as fast a cure time as possible. This allows the user to move on to the next stage without wasting time waiting for the epoxy to cure. Faster curing films with shorter tack times will have less chance of sticking to flies, bed bugs and other airborne contaminants.

The epoxy resin composition generally includes a first liquid portion including an epoxy resin and a second liquid portion including a curing agent. Although the first and second portions are liquid at ambient temperature, the liquid portion may comprise solid components dissolved or dispersed within the liquid.

The first part of the two-part composition comprises at least one epoxy resin. Epoxy resins are low molecular weight monomers or higher molecular weight polymers that typically contain at least two epoxy groups. The epoxy group is a cyclic ether having three ring atoms, sometimes also referred to as a glycidyl or oxirane group. The epoxy resin is typically liquid at ambient temperature.

Various epoxy resins are known, including, for example, bisphenol a type epoxy resins, bisphenol F type epoxy resins, bisphenol S type epoxy resins, phenol novolac type epoxy resins, alkylphenol novolac type epoxy resins, cresol novolac type epoxy resins, biphenyl type epoxy resins, aralkyl type epoxy resins, cyclopentadiene type epoxy resins, naphthalene type epoxy resins, naphthol type epoxy resins, epoxy resins of condensates of phenol and aromatic aldehydes having a phenolic hydroxyl group, biphenyl aralkyl type epoxy resins, fluorene type epoxy resins, xanthene type epoxy resins, triglycidyl isocyanurate, rubber modified epoxy resins, phosphorus based epoxy resins, and the like.

Blends of various epoxy-containing materials may also be used. Suitable blends may include two or more epoxide-containing compounds of weight average molecular weight distribution, such as low molecular weight epoxides (e.g., weight average molecular weight below 200 g/mole), medium molecular weight epoxides (e.g., weight average molecular weight in the range of about 200 to 1000 g/mole), and higher molecular weight epoxides (e.g., weight average molecular weight above about 1000 g/mole).

In one embodiment, the first part of the two-part composition comprises at least one bisphenol (e.g., a) epoxy resin. Bisphenol (e.g., a) epoxy resins are formed by reacting epichlorohydrin with bisphenol a to form diglycidyl ether of bisphenol a. Such simplest resins are formed by reacting two moles of epichlorohydrin with one mole of bisphenol a to form bisphenol a diglycidyl ether (commonly abbreviated as DGEBA or BADGE). The DGEBA resin is a transparent colorless to pale yellow liquid at ambient temperature and typically has a viscosity in the range of 5 Pa-s to 15 Pa-s at 25 ℃. Technical grades usually contain a certain molecular weight distribution, as pure DGEBA shows a strong tendency to form crystalline solids when stored at ambient temperature. The same reaction can be carried out with other bisphenols, such as bisphenol F. The choice of epoxy resin used depends on its intended end use. If the tie layer requires a greater amount of ductility, it may be desirable to have an epoxide that softens the backbone. Materials such as bisphenol a diglycidyl ether and bisphenol F diglycidyl ether can provide the desired structural adhesive properties of these materials when cured, while the hydrogenated product of these epoxides can be used to conform to substrates having an oleaginous surface.

Aromatic epoxy resins can also be prepared by the reaction of aromatic alcohols such as biphenyl diols and triphenyl triols with epichlorohydrin. Such aromatic biphenyl and triphenyl epoxy resins are not bisphenol epoxy resins.

There are two main types of aliphatic epoxy resins, namely glycidyl epoxy resins and cycloaliphatic epoxides. Glycidyl epoxy resins are typically formed by reacting epichlorohydrin with aliphatic alcohols or polyols to give glycidyl ethers or with aliphatic carboxylic acids to give glycidyl esters. The resulting resin may be monofunctional (e.g., dodecanol glycidyl ether), difunctional (diglycidyl hexahydrophthalate) or have a higher functionality (e.g., trimethylolpropane triglycidyl ether). The cycloaliphatic epoxide contains one or more cycloaliphatic rings (e.g., 3, 4-epoxycyclohexylmethyl-3, 4-epoxycyclohexane carboxylate) in the molecule to which the oxirane ring is fused. They are formed by reacting cycloolefins with peracids, such as peracetic acid. These aliphatic epoxy resins generally exhibit low viscosity (10 mpa·s to 200mpa·s) at ambient temperature and are generally used as reactive diluents. Thus, these aliphatic epoxy resins are used to modify (reduce) the viscosity of other epoxy resins. This results in the term "modified epoxy resins" to denote those containing reactive diluents with reduced viscosity. In some embodiments, the resin composition may further include a reactive diluent. Examples of reactive diluents include: 1, 4-butanediol diglycidyl ether, cyclohexane diglycidyl ether, resorcinol diglycidyl ether, p-tert-butylphenyl glycidyl ether, tolyl glycidyl ether, neopentyl glycol diglycidyl ether, trimethylolethane triglycidyl ether, trimethylolpropane triglycidyl ether, triglycidyl para-aminophenol, N, N ' -diglycidyl aniline, N, N, N ', N ' -tetraglycidyl meta-xylene diamine, and vegetable oil polyglycidyl ether. The resin composition may comprise at least 1 wt%, 2 wt%, 3 wt%, 4 wt% or 5 wt% and typically no more than 15 wt% or 20 wt% of such reactive diluents.

In some embodiments, the resin composition comprises (e.g., bisphenol a) epoxy resin in an amount of at least about 50 weight percent of the total resin composition including a mixture of boron nitride particles and cellulose nanocrystals. In some embodiments, the amount of (e.g., bisphenol a) epoxy resin is no greater than 95 wt%, 90 wt%, 80 wt%, 85 wt%, 80 wt%, 75 wt%, 70 wt%, or 65 wt% of the total resin composition.

The epoxide is generally cured with a stoichiometric or near stoichiometric amount of curing agent. In the case of a two-part epoxy resin composition, the second part comprises a curing agent (curing agent), also referred to herein as curing agent. The equivalent weight or the amount of epoxide is used to calculate the amount of co-reactant (hardener) to be used in curing the epoxy resin. The epoxide number is the number of epoxide equivalents (equivalents/kg) in 1kg of resin; and equivalent weight is the weight (grams/mole) of the resin containing 1 mole equivalent of epoxide in grams. Equivalent weight (g/mol) =1000/epoxide number (equivalents/kg).

Common classes of curing agents for epoxy resinsIncluding amines, amides, ureas, imidazoles, and thiols, among others. In typical embodiments, the curing agent comprises a reactive-NH group or a reactive-NR group ¹ R ² A group, wherein R is ¹ And R is ² Independently H or C ₁ To C ₄ Alkyl, and most typically H or methyl.

The curing agent is typically highly reactive with the epoxy groups at ambient temperature. Such curing agents are typically liquid at ambient temperature. However, the first curing agent may also be a solid, provided that it has an activation temperature equal to or lower than ambient temperature.

One class of curing agents is primary, secondary and tertiary polyamines. The polyamine curing agent may be linear, branched or cyclic. In some advantageous embodiments, the polyamine cross-linker is aliphatic. Alternatively, aromatic polyamines may be utilized.

Useful polyamines have the general formula R ⁵ —(NR ¹ R ² ) _x Wherein R is ¹ And R is ² Independently H or alkyl, R ⁵ Is a polyvalent alkylene or arylene group, and x is at least two. R is R ¹ And R is ² The alkyl group of (2) is typically C ₁ To C ₁₈ Alkyl groups, more usually C ₁ To C ₄ Alkyl, and most typically methyl. R is R ¹ And R is ² May be taken together to form a cyclic ether. In some embodiments, x is two (i.e., diamine). In other embodiments, x is 3 (i.e., triamine). In still other embodiments, x is 4.

Examples include hexamethylenediamine; 1, 10-diaminodecane; 1, 12-diaminododecane; 2- (4-aminophenyl) ethylamine; isophorone diamine; 4,4' -diaminodicyclohexylmethane; and 1, 3-bis (aminomethyl) cyclohexane. Exemplary six membered cyclic diamines include, for example, piperazine and 1, 4-diazabicyclo [2.2.2] octane ("DABCO").

Other useful polyamines include polyamines having at least three amino groups wherein the three amino groups are primary amino groups, secondary amino groups, or combinations thereof. Examples include 3,3' -diaminobenzidine, hexamethylenetriamine and triethylenetetramine.

The particular composition of the epoxy resin may be selected based on its intended end use. For example, in one embodiment, the resin composition may be used for insulation, as described in US2014/0080940, the disclosure of which is incorporated herein by reference.

The resin composition may optionally contain additives including silane treated or untreated fillers, anti-sagging additives, thixotropic agents, processing aids, waxes and UV stabilizers. Examples of typical fillers include glass bubbles, fumed silica, mica, feldspar, and wollastonite. In some embodiments, the resin composition further comprises other thermally conductive fillers such as aluminum oxide, aluminum hydroxide, fused silica, zinc oxide, aluminum nitride, silicon nitride, magnesium oxide, beryllium oxide, diamond, and copper.

Methyl Methacrylate (MMA) adhesive

Methyl Methacrylate (MMA) adhesives of exemplary embodiments may include single part MMA adhesives and two part MMA adhesives. The one-part MMA adhesive may comprise a resin. Two-part Methyl Methacrylate (MMA) adhesives have a faster strength enhancement compared to epoxides. MMA adhesives are commonly used to bond plastics and metal to plastics. These MMA adhesives are also extremely effective in joining solid surface materials together, and since they can be colored, they are widely used for worktop manufacture and installation.

Methyl methacrylate adhesives are structural acrylic adhesives made from part a (part 1) resin and part B (part 2) hardener. Most MMAs also contain rubber and additional reinforcing agents. MMA cures rapidly at room temperature and has full bond strength shortly after application. The adhesive is resistant to shear, peel and impact stresses. Looking more technically at the bonding process, these adhesives work by creating an exothermic polymerization reaction. Polymerization is the process of reacting monomer molecules together in a chemical reaction to form a polymer chain. This means that the adhesive produces a strong bond while still being flexible. These adhesives can form bonds between dissimilar materials (like metals and plastics) that have different flexibilities. Unlike some other structural adhesives (like two-part epoxies), MMA does not require heat to cure. There are MMAs available to suit particular needs over a range of working times.

MMA has higher peel strength and is more resistant to temperature. They develop strength faster, allowing the parts to be used faster. Also notable are the different processing conditions for MMA. For example, two components of MMA may each be applied separately to one of the materials that are bonded together, and MMA will not begin to cure until the joints are brought together, thereby combining the components. This means that it is not necessary to handle the exact mixing ratio to obtain good bonding. It is important to remember that MMAs do tend to have a strong odour, meaning that they should have good ventilation when applied and that they are flammable, thus requiring some care.

MMA was formulated to have a working period of between 5 minutes and 20 minutes.

All of these acrylic structural adhesive types provide excellent bond strength and durability-approaching that of epoxy adhesives-but have the advantage of faster cure speed, less sensitivity to surface preparation, and bonding more types of materials.

Silicone adhesive

The silicone adhesive of the exemplary embodiments may include a one-part adhesive and a two-part adhesive. Two-part silicone adhesives are typically used when large bond areas are present or when insufficient relative humidity is present to complete curing. Common applications for these silicone adhesives are electronic applications, including the manufacture of household appliances, automobiles, and window manufacturing.

Suitable silicone resins include moisture-cured silicone resins, condensation-cured silicone resins, and addition-cured silicone resins such as hydroxyl-terminated silicone resins, silicone rubbers, and fluorosilicones. Examples of suitable commercially available silicone PSA compositions comprising silicone resins include 280A, 282, 7355, 7358, 7502, 7657, Q2-7406, Q2-7566, and Q2-7735 of dakaning (Dow Coming); PSA 590, PSA 600, PSA 595, PSA 610, PSA 518 (medium phenyl content), PSA 6574 (high phenyl content), PSA 529, PSA 750-D1, PSA 825-D1 and PSA 800-C of General Electric). An example of a commercially available two-part silicone resin is the resin sold under the trade designation "SILATIC J" by the Dow chemical company (Dow Chemical Company, midland, mich) of Midland, michigan.

The Pressure Sensitive Adhesive (PSA) may include: natural or synthetic rubbers such as styrene block copolymers (styrene butadiene; styrene-isoprene; styrene-ethylene/butylene block copolymers); nitrile rubber, synthetic polyisoprene, ethylene-propylene rubber, ethylene-propylene-diene monomer rubber (EPDM), polybutadiene, polyisobutylene, butyl rubber, styrene-butadiene random copolymer, and combinations thereof.

Additional pressure sensitive adhesives include poly (alpha-olefins), polychloroprene, and silicone elastomers. In some embodiments, polychloroprene and silicone elastomers may be preferred because polychloroprene contains halogens, which may contribute to flame retardancy, and silicone elastomers are resistant to thermal degradation.

Polyurethane adhesives

Exemplary polyurethane adhesives as used in embodiments may include both one-part polyurethane adhesives and two-part polyurethane adhesives. Two-part polyurethane adhesives can be formulated to have a wide range of properties and characteristics upon curing. For example, dissimilar materials (such as glass) are commonly used when bonding them to metals or aluminum to steel.

Most polyurethane adhesives are polyester or polyether based. These polyurethane adhesives are present in isocyanate prepolymers and in active hydrogen containing hardener components (polyols). These polyurethane binders form the soft segments of the polyurethane, while the isocyanate groups form the hard segments. The soft segments typically comprise a relatively large portion of the elastomeric polyurethane binder and thus determine its physical properties. For example, polyester-based polyurethane adhesives have better oxidation and high temperature stability than polyether-based polyurethane adhesives, but polyester-based polyurethane adhesives have lower hydrolytic stability and low temperature flexibility. Polyethers, however, are generally more expensive than polyesters.

Many polyurethane adhesives are sold as two-component polyurethane adhesives. The first component contains a diisocyanate and/or isocyanate prepolymer (part 1) and the second component consists of a polyol (and an amine/hydroxyl chain extender) (part 2). Catalysts (typically tin salts or tertiary amines) are typically added to accelerate curing. Reactive ingredients are typically blended with additives and plasticizers to achieve desired processing and/or final properties and to reduce costs.

For example, polyurethanes may be prepared by the reaction of one or more polyols and/or polyamines and/or aminoalcohols with one or more polyisocyanates, optionally in the presence of non-reactive components. For applications where weathering may occur, it is generally desirable that the polyols, polyamines and/or aminoalcohols and the polyisocyanates be free of aromatic groups.

For example, suitable polyols include materials commercially available under the trade name DESMOPHEN from bayer corporation (Bayer Corporation, pittsburgh, pa) of Pittsburgh, pa. The polyols may be polyester polyols (e.g., desmophen 631A, 650A, 651A, 670A, 680, 110 and 1150), polyether polyols (e.g., desmophen 550U, 1600U, 1900U and 1950U), or acrylic polyols (e.g., desmphen A160SN, A575 and A450 BA/A).

Suitable polyamines include, for example: aliphatic polyamines, such as ethylenediamine, 1, 2-diaminopropane, 2, 5-diamino-2, 5-dimethylhexane, 1, 11-diaminodecane, 1, 12-diaminododecane, 2, 4-and/or 2, 6-hexahydrotoluenediamine and 2,4' -diamino-dicyclohexylmethane; and aromatic polyamines such as 2, 4-and/or 2, 6-diaminotoluene and 2,4 '-and/or 4,4' -diaminodiphenylmethane; amine-terminated polymers such as, for example, those available under the trade name JEFFAMINE polypropylene glycol diamine (e.g., JEFFAMINE XTJ-510) from heny micania (Salt Lake City, utah) (Huntsman Chemical (Salt Lake City, utah)) and those available under the trade name Hycar ATBN (amine-terminated acrylonitrile butadiene copolymer) from Noveon corp (Cleveland, ohio) of Cleveland, ohio, as well as those disclosed in U.S. patent No. 3,436,359 (Hubin et al) and U.S. patent No. 4,833,213 (Leir et al) (amine-terminated polyethers and polytetrahydrofuran diamines); and combinations thereof.

Suitable amino alcohols include, for example, 2-amino ethanol, 3-amino propanal-1-ol, alkyl substituted versions of the foregoing compounds, and combinations thereof.

Suitable polyisocyanate compounds include, for example: aromatic diisocyanates (e.g., 2, 6-toluene diisocyanate; 2, 5-toluene diisocyanate; 2, 4-toluene diisocyanate; m-phenylene diisocyanate, p-phenylene diisocyanate, methylenebis (o-chlorophenyl diisocyanate), methylenediphenyl-4, 4 '-diisocyanate; polycarbodiimide-modified methylenediphenyl diisocyanate, (4, 4' -diisocyanato-3, 3', 5' -tetraethyl) diphenylmethane, (4, 4 '-diisocyanato-3, 3' -dimethoxybiphenyl (o-dimethoxyaniline diisocyanate), 5-chloro-2, 4-toluene diisocyanate and 1-chloromethyl-2, 4-diisocyanato benzene, aromatic aliphatic diisocyanates (e.g., m-xylylene diisocyanate and tetramethyl-m-xylylene diisocyanate), aliphatic diisocyanates (e.g., 1, 4-diisocyanato butane, 1, 6-diisocyanato hexane, 1, 12-diisocyanato dodecane; and 2-methyl-1, 5 '-diisocyanato-3, 3' -dimethoxydiphenyl diisocyanate; 5-chloro-2, 4-toluene diisocyanate and 1-chloromethyl-2, 4-diisocyanato, aromatic diisocyanates (e.g., m-xylylene diisocyanate and tetramethyl-m-xylylene diisocyanate), aliphatic diisocyanates (e.g., 1, 4-diisocyanato butane, 1, 6-diisocyanato hexane, 1, 12-diisocyanato dodecane; and 2-methyl-1, 5-diisocyanato-1, 5-dicyclohexyl (e) Polymeric or oligomeric compounds (e.g., polyalkylene oxides, polyesters, polybutadiene, etc.) terminated with two isocyanate functional groups (e.g., a dicarbamate of toluene-2, 4-diisocyanate-terminated polyoxypropylene glycol); polyisocyanates commercially available under the trade names MONDUR or DESMODUR (e.g., desmodur XP7100 and Desmodur N3300A) from Bayer corporation of Pittsburgh, pa. (Bayer Corporation, pittsburgh, pa.); and combinations thereof.

In some embodiments, the polyurethane comprises the reaction product of components comprising at least one polyisocyanate and at least one polyol. In some embodiments, the polyurethane comprises the reaction product of components comprising at least one polyisocyanate and at least one polyol. In some embodiments, the at least one polyisocyanate comprises an aliphatic polyisocyanate. In some embodiments, the at least one polyol comprises an aliphatic polyol. In some embodiments, the at least one polyol comprises a polyester polyol or a polycarbonate polyol.

Typically, the polyurethane is malleable and/or pliable. For example, the percent elongation at break (under ambient conditions) of the polyurethane or any layer containing the polyurethane may be at least 10%, 20%, 40%, 60%, 80%, 100%, 125%, 150%, 175%, 200%, 225%, 250%, 275%, 300%, 350% or even 400% or more.

In certain embodiments, the polyurethane has hard segments (typically segments corresponding to one or more polyisocyanates in any combination) in an amount from 35 wt%, 40 wt% or 45 wt% up to 50 wt%, 55 wt%, 60 wt% or even 65 wt%.

As used herein, wt% means weight percent based on the total weight of the material, and

hard segment wt% = (weight of short chain diol and polyol + weight of short chain di-or polyisocyanate)/total weight of resin

Wherein:

the equivalent weight of the short-chain diol and the polyol is less than or equal to 185 grams per equivalent, and the functionality is less than or equal to 2; and is also provided with

The equivalent weight of the short-chain isocyanate is less than or equal to 320 g/eq and the functionality is less than or equal to 2.

One or more catalysts are typically included with the two-component urethane. Catalysts for the two-part urethanes are well known and include, for example, catalysts based on aluminum, bismuth, tin, vanadium, zinc, tin, and zirconium. Tin-based catalysts have been found to significantly reduce the amount of outgassing during the formation of polyurethane. Examples of tin-based catalysts include dibutyltin compounds such as dibutyltin diacetate, dibutyltin dilaurate, dibutyltin diacetylacetonate, dibutyltin dithioates, dibutyltin dioctanoate, dibutyltin dimaleate, dibutyltin acetonylate and dibutyltin oxide. If present, the amount of any catalyst is typically at least 200 parts per million (200 ppm), 300ppm or higher by weight; however, this is not necessary.

Additional suitable two-part polyurethanes are described in U.S. patent No. 6,258,918B1 (Ho et al) and U.S. patent No. 5,798,409 (Ho), the disclosures of which are incorporated herein by reference.

In general, the amount of polyisocyanate to polyol, polyamine and/or aminoalcohol in the two-part urethane is selected to be about stoichiometric equivalent, but in some cases it may be desirable to adjust the relative amounts to other ratios. For example, a slight stoichiometric excess of polyisocyanate may be suitable to ensure a high degree of binding of the polyol, polyamine, and/or aminoalcohol, but any excess isocyanate groups present after polymerization will typically react with materials having reactive hydrogen (e.g., extraneous moisture, alcohols, amines, etc.).

The entire disclosures of the patents, patent documents, and publications cited herein are incorporated by reference in their entirety as if each were individually incorporated. In the event of any conflict or conflict between the written specification and the disclosure in any document incorporated by reference, the written specification will control. Various modifications and alterations to this disclosure will become apparent to those skilled in the art without departing from the scope and spirit of this disclosure. The present disclosure is not intended to be unduly limited by the illustrative embodiments and examples set forth herein and such examples and embodiments are presented by way of example only with the scope of the disclosure intended to be limited only by the claims set forth herein as follows.

Claims

1. A dispenser system, comprising:

one or more dispenser components including one or more sensors for providing at least one process parameter of a dispensable material, the one or more sensors including a temperature sensor including a probe disposed in a fluid path of the dispensable material and configured to sense a temperature of the dispensable material; and

a processor operably coupled to the one or more dispenser components, the processor configured to:

receiving at least one parameter associated with the dispenser system;

determining a value of an operating parameter of the dispenser system based on the at least one parameter, the value of the operating parameter to achieve a flow rate of dispensable material in the dispenser system;

providing the operating parameter;

receiving the temperature of the dispensable material from the temperature sensor;

adjusting a value of the operating parameter of the dispenser system based on the sensed temperature; and

providing the adjusted operating parameter.

2. The dispenser system of claim 1, wherein the operating parameter is a driving force pressure of the dispenser system.

3. The dispenser system of claim 1, wherein the temperature sensor further comprises a shield separating an outer surface of the probe from the dispensable material, and the probe is configured to sense the temperature of the dispensable material through the shield.

4. The dispenser system of claim 1, wherein the probe is configured to directly contact the dispensable material in the fluid path.

5. The dispenser system of claim 1, wherein the one or more sensors further comprise a conductivity sensor configured to sense a conductivity of the dispensable material, and wherein the processor is further configured to:

receiving sensed conductivity of the dispensable material from the conductivity sensor;

determining a cure state of the dispensable material based on the sensed temperature of the dispensable material and the sensed conductivity of the dispensable material; and

the values of the operating parameters of the dispenser system are adjusted based on the sensed temperature and the cure state.

6. The dispenser system of claim 5, wherein the temperature sensor comprises the conductivity sensor, and wherein the temperature sensor is further configured to apply a voltage to the dispensable material in the fluid path of the dispensable material.

7. The dispenser system of claim 1, further comprising: a user input device, and wherein the at least one process parameter is received from user input at the user input device.

8. The dispenser system of claim 1, wherein the at least one parameter of the dispenser system comprises a density of the dispensable material.

9. The dispenser system of claim 8, wherein:

the one or more sensors further include a quality measurement device configured to sense quality data of the dispensable material;

the at least one process parameter includes the quality data; and is also provided with

The processor is further configured to: a volumetric flow rate of the dispensable material is determined based on the density of the dispensable material and the mass data of the dispensable material.

10. The dispenser system of claim 1, further comprising: a wireless communication interface.

11. The dispenser system of claim 10, wherein the processor is further configured to: and communicate with the dispenser system through the wireless communication interface.

12. The dispenser system of claim 10, wherein the processor is further configured to: and communicating with a smart phone through the wireless communication interface.

13. The dispenser system of claim 12, wherein the processor is configured to: a request for input data for determining the operating parameter is provided to the smart phone over the wireless communication interface.

14. The dispenser system of claim 1, wherein the processor is further configured to:

determining a maximum idle time or a purge time for the one or more dispenser components based on the at least one parameter and the at least one process parameter; and

providing the maximum idle time or the purge time.

15. The dispenser system of claim 14, wherein the one or more sensors are further configured to provide at least one environmental parameter, and the processor is further configured to: the maximum idle time or the purge time is determined based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter.

16. The dispenser system of claim 14, further comprising: a user interface operably coupled to the processor, and wherein the processor is further configured to:

Receiving one or more user inputs including one or more of a safety factor, a process control factor, and a waste factor; and

the maximum idle time or the purge time is determined based on the one or more user inputs, the at least one parameter, and the at least one process parameter.

17. The dispenser system of claim 1, wherein the processor is configured to: the operating parameters and the adjusted operating parameters are provided to the one or more dispenser components.

18. The dispenser system of claim 1, wherein the processor is configured to: the operating parameters and the adjusted operating parameters are provided to a human-machine interface.

19. The dispenser system of claim 1, wherein the dispensable material comprises an adhesive.

20. A method of dispensing dispensable material using a dispenser system, the method comprising:

receiving at least one parameter associated with the dispenser system;

determining a value of an operating parameter of the dispenser system based on the at least one parameter, the value of the operating parameter to achieve a flow rate of the dispensable material in the dispenser system;

Providing the operating parameter;

receiving at least one process parameter comprising a sensed temperature of the dispensable material;

providing the adjusted operating parameter.

21. The method of claim 20, wherein the operating parameter is a driving force pressure of the dispenser system.

22. The method of claim 20, further comprising:

receiving the sensed conductivity of the dispensable material from a conductivity sensor;

23. The method of claim 20, wherein the at least one parameter of the dispenser system comprises a density of the dispensable material.

24. The method of claim 23, wherein the at least one process parameter further comprises quality data of the dispensable material, and wherein the method further comprises: a volumetric flow rate of the dispensable material is determined based on the density of the dispensable material and the mass data of the dispensable material.

25. The method of claim 20, further comprising:

determining a maximum idle time or a cleaning time of one or more dispenser components of the dispenser system based on the at least one parameter and the at least one process parameter; and

providing the maximum idle time or the purge time.

26. The method of claim 25, further comprising:

receiving at least one environmental parameter provided by one or more sensors; and

the maximum idle time or the purge time is determined based on the at least one environmental parameter, the at least one parameter, and the at least one process parameter.

27. The method of claim 25, further comprising:

28. The method of claim 20, further comprising: the operating parameters and the adjusted operating parameters are provided to one or more dispenser components of the dispenser system.

29. The method of claim 20, further comprising: the operating parameters and the adjusted operating parameters are provided to a human-machine interface.

30. The method of claim 20, wherein the dispensable material comprises an adhesive.

31. A dispenser system, comprising:

one or more dispenser components configured to provide dispensable material; and

a processor operably coupled to the one or more dispenser components and configured to:

receiving a plurality of calibration data points of the dispenser system based on a plurality of dispensing samples of the dispensable material and one or more parameters of the one or more dispenser components;

selecting one or more predetermined models based on the one or more parameters of the dispenser component;

determining a calibration model based on the plurality of calibration data points and the one or more models; and

one or more settings of the one or more dispenser components are adjusted based on the calibration model.

32. The dispenser system of claim 31, wherein the plurality of calibration data points comprises at least 5 calibration data points and no more than 15 calibration data points.

33. The dispenser system of claim 31, wherein the one or more parameters of the one or more dispenser components include a parameter indicative of a manufacturing lot of the dispensable material, and wherein the processor is configured to: the one or more predetermined models are selected based on the manufacturing lot of the dispensable material.

34. The dispenser system of claim 31, wherein to determine the calibration model, the processor is further configured to:

determining one or more outliers of the plurality of calibration data points based on the one or more models;

removing the one or more outliers from the plurality of calibration data points to produce a modified set of calibration data points; and

the calibration model is determined based on the modified set of calibration data points and the one or more predetermined models.

35. The dispenser system of claim 34, wherein to determine the one or more outliers, the processor is further configured to: a regression analysis is performed based on the plurality of calibration points and the one or more predetermined models.

36. The dispenser system of claim 34, wherein to determine the one or more outliers, the processor is further configured to: one or more calibration points of the plurality of calibration points that differ from corresponding data points of the one or more predetermined models by more than a threshold value are determined.

37. The dispenser system of claim 36, wherein the threshold is a percentage difference.

38. The dispenser system of claim 34, further comprising: a display for displaying information; and a user interface for receiving user input, and wherein the processor is further configured to:

determining that the modified set of calibration data points includes less than a threshold number of data points;

determining one or more dispenser pressures for one or more additional calibration data points based on the modified set of calibration data points and the one or more predetermined models;

requesting, using the display, one or more additional calibration data points, each of the one or more additional calibration data points corresponding to one of the one or more dispenser pressures;

Receiving the one or more additional calibration data points using the user interface; and

the modified set of calibration data points is modified to include the one or more additional calibration data points.

39. The dispenser system of claim 31, wherein each calibration data point of the plurality of calibration data points includes a dispenser pressure, a dispensing time, and a mass of the dispensable material dispensed.

40. A method for calibrating a dispenser system, the method comprising:

receiving a plurality of calibration data points of the dispenser system based on a plurality of dispensed samples of dispensable material and one or more parameters of a dispenser component of the calibration system;

determining a calibration model based on the plurality of calibration data points and the one or more predetermined models; and

one or more settings are provided for the dispenser system based on the calibration model.

41. The method of claim 40, wherein the plurality of calibration data points comprises at least 2 calibration data points and no more than 100 calibration data points.

42. The method of claim 40, wherein the one or more parameters of the dispenser component comprise parameters indicative of a manufacturing lot of the dispensable material, and wherein the one or more predetermined models are retrieved based on the manufacturing lot of the dispensable material.

43. The method of claim 40, wherein determining the calibration model comprises:

44. The method of claim 43, wherein determining the one or more outliers comprises regression analysis based on the plurality of calibration points and the one or more predetermined models.

45. The method of claim 43, wherein determining the one or more outliers comprises determining one or more of the plurality of calibration points that differ from corresponding data points of the one or more predetermined models by more than a threshold.

46. The method of claim 45, wherein the threshold is a percentage difference.

47. The method of claim 43, further comprising:

requesting one or more additional calibration data points, each of the one or more additional calibration data points corresponding to one of the one or more dispenser pressures;

receiving the one or more additional calibration data points;

48. The method of claim 40, wherein each calibration data point of the plurality of calibration data points includes a dispenser pressure, a dispensing time, and a mass of the dispensable material dispensed.