CN114115141A

CN114115141A - Solder paste production method and system

Info

Publication number: CN114115141A
Application number: CN202111289528.1A
Authority: CN
Inventors: 肖大为; 卢克胜; 肖健
Original assignee: Jiangsu Sanwal Electronic Technology Co ltd
Current assignee: Jiangsu Sanwal Electronic Technology Co ltd
Priority date: 2021-11-02
Filing date: 2021-11-02
Publication date: 2022-03-01
Anticipated expiration: 2041-11-02
Also published as: CN114115141B

Abstract

The embodiment of the specification provides a solder paste production method, which comprises the steps of obtaining first viscosity data of a solder paste semi-finished product based on a viscometer, and mixing and stirring the solder paste semi-finished product based on solder powder and soldering flux to generate the solder paste semi-finished product; determining whether a preset condition is met through a judgment module based on the first viscosity data; responding to the judgment, and sending an alarm instruction by the early warning module; in response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate a finished solder paste product through vacuumizing and pouring of the semi-finished solder paste product.

Description

Solder paste production method and system

Technical Field

The specification relates to the technical field of welding, in particular to a solder paste production method and a solder paste production system.

Background

Solder paste is a new type of soldering material produced in association with Surface Mount Technology (SMT), and is a paste mixture formed by mixing solder powder, flux, other surfactants, thixotropic agents, and the like. With the development of electronic information equipment, SMT has become one of the mainstream technologies of electronic assembly, and the usage amount of solder paste is increasing. The quality of the solder paste affects the use performance of the solder paste, so the quality control of the solder paste is also very important.

Therefore, it is desirable to provide a solder paste production method, which can realize intelligent production and improve the quality of solder paste production by performing subsequent operations on data judgment of each stage of a solder paste semi-finished product.

Disclosure of Invention

One embodiment of the present specification provides a solder paste production method. The method comprises the following steps: acquiring first viscosity data of a semi-finished solder paste product based on a viscometer, wherein the semi-finished solder paste product is generated based on mixing and stirring of solder powder and soldering flux; determining whether a preset condition is met or not through a judging module based on the first viscosity data; responding to the judgment, and sending an alarm instruction by the early warning module; in response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate a finished solder paste product through vacuumizing and pouring of the semi-finished solder paste product.

One of the embodiments of the present specification provides a solder paste production system, including: the device comprises an acquisition module, a judgment module, an early warning module and a production module: the obtaining module is used for obtaining first viscosity data of a semi-finished solder paste product based on a viscometer, and the semi-finished solder paste product is generated based on mixing and stirring of solder powder and soldering flux; the judging module is used for determining whether a preset condition is met or not based on the first viscosity data; responding to the judgment, and sending an alarm instruction by the early warning module; in response, the production module sends a finished product preparation instruction to enable a finished product preparation device to generate a solder paste finished product through vacuumizing and pouring of the solder paste semi-finished product.

One of the embodiments of the present specification provides a solder paste production apparatus, including at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least a portion of the computer instructions to implement the solder paste production method.

One of the embodiments of the present specification provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the solder paste production method.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic view of an application scenario of a solder paste production system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow diagram of a method of producing solder paste in accordance with some embodiments of the present description;

FIG. 3 is an exemplary flow chart illustrating the determination of whether a preset condition is satisfied according to some embodiments of the present description;

FIG. 4 is a schematic illustration of a determination of predicted viscosity data based on a viscosity identification model in accordance with certain embodiments of the present description;

FIG. 5 is another exemplary flow chart illustrating determining whether a preset condition is satisfied according to some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is a schematic diagram of an application scenario of a solder paste production system 100 according to some embodiments of the present disclosure. The application scenario may include the terminal 110, the network 120, the processor 130, the solder paste production device 140, and the storage device 150.

The solder paste production system 100 can be used in facilities where solder paste production is required.

The terminal 110, the network 120, the processor 130, the solder paste manufacturing device 140, and the storage device 150 can exchange data and/or information via the network 120 to implement the solder paste manufacturing function. The memory device 140 can store all information in performing the solder paste manufacturing function. In some embodiments, the solder paste production apparatus 140 can send the solder paste production results to the processor 130 and receive feedback information from the processor 130. The processor 130 can process solder paste production data including current inspection data and historical environmental data, where the historical solder paste production data can be retrieved from a storage device via the network 120. The processor 130 can process the solder paste production data, determine whether processing is required, generate an instruction based on the determination result that processing is required, and send the instruction to the solder paste production equipment 140 through the network to instruct the solder paste production equipment to process the instruction. The above interaction relationship between the devices is only an example, and other interaction forms are possible according to actual situations.

The terminal 110 may be used to enter and/or retrieve data or information. For example, static data or dynamic data of the planting device may be obtained by the terminal 110. In some embodiments, the terminal 110 may be a personal terminal device or a public terminal device. For example, the terminal 110 may be a mobile terminal, a wearable device, or a computing device of the object to be served, or the like. For another example, the terminal 110 may be a terminal device held by a service provider, and the service provider may send an instruction through a terminal acquisition server. In some embodiments, the terminal 110 may include a mobile phone 110-1, a tablet computer 110-2, a laptop computer 110-3, a desktop computer 110-4, the like, or any combination thereof.

The network 120 may connect the various components of the system and/or connect the system with external resource components. Network 120 enables communication between the various components and with other components outside the system to facilitate the exchange of data and/or information. In some embodiments, the network 120 may be any one or more of a wired network or a wireless network. For example, network 120 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), and the like, or any combination thereof. The network connection between the parts can be in one way or in multiple ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or network switching points, through which one or more components of the access point system 100 may connect to the network 120 to exchange data and/or information.

Processor 130 may process data and/or information obtained from other devices or system components. The processor may execute program instructions based on the data, information, and/or processing results to perform one or more of the functions described herein. In some embodiments, the processor 130 may include one or more sub-processing devices (e.g., single core processing devices or multi-core processing devices). For example only, the processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a microprocessor, the like, or any combination thereof.

The solder paste manufacturing apparatus 140 can be used to manufacture solder paste, and includes a stirring apparatus, a vacuum-pumping apparatus, and a pouring apparatus. In some embodiments, the stirring device can stir and strongly disperse, and has stirring, dispersing, shearing, and mixing functions. In some embodiments, the evacuation device may be a nitrogen-filled device. In some embodiments, the perfusion apparatus may be a fully automated racking apparatus.

In some embodiments, the solder paste production apparatus 140 can determine whether the first viscosity data satisfies a first sub-predetermined condition. In some embodiments, in response to being satisfied, the solder paste production facility 140 can determine predicted viscosity data for the solder paste intermediate based on the production recipe and the production process parameters. In some embodiments, the solder paste production apparatus 140 may determine whether to issue an alarm instruction based on whether the relationship between the predicted viscosity data and the first viscosity data satisfies a second sub-preset condition. In some embodiments, in response to the satisfaction, image data of the solder paste intermediate product at different stirring speeds is acquired. In some embodiments, the solder paste production apparatus 140 can determine second viscosity data based on the image data at the different stirring speeds. In some embodiments, the solder paste production apparatus 140 may determine whether to issue the warning instruction based on whether a relationship between the first viscosity data and the second viscosity data satisfies a third sub-preset condition.

Storage device 150 may be used to store data and/or instructions. Storage device 150 may include one or more storage components, each of which may be a separate device or part of another device. In some embodiments, the storage device 150 may be implemented on a cloud platform.

It should be understood that the system and its modules shown in FIG. 1 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Those skilled in the art will appreciate that the above described methods and systems may be implemented using computer executable instructions and/or embodied in processor control code.

In some embodiments, the solder paste production system 100 includes an acquisition module, a determination module, a prediction module, an early warning module, and a production module.

In some embodiments, the acquisition module is configured to acquire first viscosity data of the solder paste intermediate based on the viscometer. In some embodiments, the solder paste semi-finished product is generated based on mixing and stirring of solder powder and soldering flux. In some embodiments, the obtaining module is further configured to, in response to the satisfaction, obtain image data of the solder paste semi-finished product at different stirring speeds.

In some embodiments, the determination module is configured to determine whether a predetermined condition is satisfied based on the first viscosity data. In some embodiments, the preset condition includes a first sub-preset condition and a second sub-preset condition, and the determining module is further configured to determine whether the first viscosity data satisfies the first sub-preset condition. In some embodiments, the determining module is further configured to determine whether to issue an alarm instruction based on whether a relationship between the predicted viscosity data and the first viscosity data satisfies a second sub-preset condition. In some embodiments, the preset condition includes a first sub-preset condition and a third sub-preset condition, and the determining module is further configured to determine whether to issue the alarm instruction based on whether a relationship between the first viscosity data and the second viscosity data satisfies the third sub-preset condition.

In some embodiments, the prediction module is further configured to determine, in response to the satisfaction, predicted viscosity data for the solder paste intermediate based on the manufacturing recipe and the manufacturing process parameter. In some embodiments, the prediction module is further to determine second viscosity data based on the image data at the different agitation speeds. In some embodiments, the determining module determines whether a preset condition is met, and in response to no, the early warning module issues an alarm instruction.

In some embodiments, the determining module determines whether the preset condition is met, and in response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate the finished solder paste product by vacuumizing and pouring the semi-finished solder paste product.

In some examples, the functions of the solder paste production method under one or more scenarios described in the embodiments of the present specification are implemented by respectively executing different functions on different devices, or by simultaneously executing multiple functions by one device.

It should be noted that the above description of the system and its modules is for convenience only and should not limit the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the acquisition module, the determination module, the prediction module, the early warning module, and the production module disclosed in fig. 1 may be different modules in a system, or may be a module that implements the functions of two or more of the above modules. For example, the prediction module and the early warning module may be two modules, or one module may have both the prediction function and the early warning function. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.

Fig. 2 is an exemplary flow diagram of a method of producing solder paste in accordance with some embodiments of the present disclosure. The process 200 shown in fig. 2 includes step 210, step 220, step 230, and step 240.

In step 210, first viscosity data of the solder paste semi-finished product is obtained based on a viscometer. In some embodiments, step 210 is performed by an acquisition module.

The semi-finished solder paste is an intermediate product obtained by respectively pretreating and stirring solder powder and soldering flux to a certain degree, but not finally preparing the solder paste into a finished solder paste. In some embodiments, the solder paste semi-finished product is generated based on mixing and stirring of solder powder and soldering flux.

The solder powder refers to tin powder. In some embodiments, the solder powder includes an alloy composition. For example, the solder powder includes tin bismuth, tin silver copper alloy, and the like.

Flux refers to a chemical substance that can aid and accelerate the soldering process, while protecting and preventing oxidation reactions during the soldering process. In some embodiments, a flux may be solid, liquid, and gas. In some embodiments, a solder flux includes an activator, a thixotropic agent, a resin, a solvent, and the like.

The first viscosity data refers to viscosity data of the solder paste intermediate determined by a viscometer. For example, the first viscosity data may be viscosity data of the solder paste semi-finished product at a preset detection time determined by the viscometer at the detection time.

In some embodiments, the viscometer includes a capillary viscometer, a rotational viscometer, and a falling ball viscometer. In some embodiments, the first viscosity data can be read using a viscometer at a suitable temperature, with the correct stirring speed selected.

And step 220, determining whether a preset condition is met or not through a judgment module based on the first viscosity data.

The preset condition refers to a condition that the first viscosity data itself or the difference value of the first viscosity data and the reference viscosity data acquired by other ways needs to be satisfied. In some embodiments, the preset condition may be that a value of the first viscosity data itself satisfies a preset threshold condition, or that a difference from the reference viscosity data acquired by other routes is not greater than a preset threshold. In some embodiments, the reference viscosity data obtained by other means includes predicted viscosity data and second viscosity data, and please refer to the related descriptions of fig. 3 to 5 for the related contents of the predicted viscosity data and the second viscosity data. In some embodiments, the value of the preset threshold may be preset by the system or by a user to set the preset condition.

In some embodiments, the preset condition includes three sub-preset conditions, which are a first sub-preset condition, a second sub-preset condition, and a third sub-preset condition. In some embodiments, the first sub-preset condition is mainly used for determining a condition to be satisfied by obtaining the first viscosity data of the solder paste semi-finished product based on the viscometer. In some embodiments, the second sub-preset condition is mainly used for judging a condition to be satisfied for determining the relationship between the predicted viscosity data and the first viscosity data of the solder paste semi-finished product based on the production recipe and the production process parameters. In some embodiments, the third sub-preset condition is mainly used for judging a condition to be satisfied by a relationship between the second viscosity data and the first viscosity data determined based on the image data at different stirring speeds. For more details about the first sub preset condition, the second sub preset condition and the third sub preset condition, reference is made to fig. 3 to 5 and the related description thereof.

In response to no, the prediction module issues an alarm instruction, step 230.

The alarm instruction comprises a sound alarm instruction, a flash alarm instruction, a picture alarm instruction and the like. In some embodiments, the alarm command is used as a warning of a machine failure, which may be based on field conditions or other conditions, and is not limited herein.

The validity of the working action of the machine can be ensured based on the alarm instruction, the redundant action or invalid action of the machine can be reduced or even avoided, and the timely monitoring of the machine is also beneficial to timely recognition and correction processing when the viscosity abnormal condition occurs. In some embodiments, the viscosity of the solder paste semi-finished product can be prevented from being too high or too low to influence the quality of the finished product based on the alarm instruction. For example, the viscosity of the solder paste semi-finished product is too high, and the solder paste is not easy to penetrate through the leak holes of the template. For example, if the viscosity of the solder paste intermediate is too low, the solder paste is likely to fall off when used.

In some embodiments, the alert instruction may be generated before generating the corrective instruction. The correction instruction can be generated in an auxiliary manner according to the abnormal degree of the current situation, for example, the current first viscosity data is excessively deviated from a conventional value, or other correction instructions can be generated under the subsequent situation obviously needing to intervene. Based on the correction instruction, the correction processing may be instructed.

In some embodiments, the modification process may be notifying a user to adjust a parameter of interest in the production process.

In some embodiments, the modification process may include notifying a user to adjust the specific gravity of the raw material component, the production environment, or the operating parameters of the instrumentation. For example, the specific gravity of the tin powder component or the temperature of the production environment is adjusted.

In response, the production module sends a finished product preparation instruction to the finished product preparation device to generate a finished solder paste product by vacuuming and pouring the semi-finished solder paste product, step 240.

The finished product preparation instruction is a code instruction for causing a machine to perform finished product preparation, and is sent by the production module. In some embodiments, the production module sends a finished solder paste product preparation instruction. And the finished product preparation instruction indicates that the current semi-finished solder paste product is fully stirred, and the next links of vacuumizing and filling the finished product can be performed.

The finished product preparation device is a device for generating a finished solder paste product by vacuumizing and pouring a semi-finished solder paste product, and comprises stirring equipment, vacuumizing equipment and pouring equipment. For example, the evacuation device may be a nitrogen gas charging device. In some embodiments, the evacuation device may be based on field conditions or other conditions.

Pouring means pouring the mixed semi-finished solder paste into a mold according to the requirement. In some embodiments, the filling apparatus may be a fully automatic dispensing apparatus or a manual dispensing, which may be specific to the field situation or other conditions.

The validity of solder paste production can be guaranteed by acquiring the first viscosity data of the solder paste semi-finished product based on the viscometer, so that the equipment can be reduced and even avoided from carrying out redundant production or invalid production, and the solder paste production efficiency is improved. Meanwhile, whether preset conditions are met or not is determined through the judgment module, and an alarm is given in time when abnormal conditions occur, so that intelligent production is realized, and the production quality of the solder paste is improved.

It should be noted that the above description of flow solder paste production is for illustration and description only and is not intended to limit the scope of applicability of the present description. Various modifications and alterations to the process solder paste production will be apparent to those skilled in the art in light of this description. However, such modifications and variations are intended to be within the scope of the present description.

FIG. 3 is an exemplary flow chart of a method 300 for solder paste production that determines whether predetermined conditions are met according to some embodiments of the present disclosure. The process 300 shown in FIG. 3 includes step 310, step 320, and step 330.

Step 310, determining whether the first viscosity data satisfies a first sub-preset condition. In some embodiments, step 310 is performed by a decision module.

For a description of obtaining the first viscosity data, refer to the related description of fig. 2.

The first sub-preset condition is mainly used for judging whether the numerical value of the first viscosity data meets the condition. For example, the first sub-preset condition may be to determine whether the first viscosity data meets a preset threshold, and reach a reasonable threshold interval. The preset threshold value can be 180 Pa/s-200 Pa/s, 170 Pa/s-200 Pa/s or other possible ranges, and can be set according to actual production needs.

And 320, responding to the satisfaction, determining the predicted viscosity data of the semi-finished solder paste product through a prediction module based on the production formula and the production process parameters. In some embodiments, step 320 is performed by a prediction module.

In some embodiments, the production recipe includes a content of the flux and a content of the solder powder, and the production process parameters include process parameters of stirring. In some embodiments, the process parameters of stirring mainly include the rotation speed of the stirrer, the stirring time period and the like.

By way of example only, the production formulation may include 20-40 parts by weight of rosin, 30-45 parts by weight of synthetic resin, 2-5 parts by weight of surfactant, 4-8 parts by weight of organic solvent, 1-3 parts by weight of cosolvent, 1-3 parts by weight of anticorrosive agent, 1-3 parts by weight of film-forming agent, 2-5 parts by weight of succinic acid, 1-3 parts by weight of corrosion inhibitor, 1-3 parts by weight of antioxidant, 1-3 parts by weight of organic amine, and the like.

In some embodiments, the production process parameters further include parameters related to production equipment and production environment. Such as the model of the equipment for producing solder paste, the temperature and humidity during production, etc.

The predicted viscosity data refers to a predicted viscosity value of the solder paste.

In some embodiments, the predicted viscosity data may be determined based on a recipe or a test time.

In some embodiments, the detection time is the time corresponding to the acquisition of the first viscosity, and may be a point in time during the preparation process.

In some embodiments, the predicted viscosity data may be empirical values obtained by table lookup.

In some embodiments, the table may be a table based on historical data statistics. The table contains the types, contents and corresponding viscosities of the solder powder and the soldering flux in different proportions of different formulations, and can also include parameters of the environment and instruments during preparation, such as temperature, the type of a stirrer and the like. In some embodiments, the table may be a test set of historical data, and the relevant content may be as shown in FIG. 5.

In some embodiments, the table may be retrieved and presented on the terminal device for query browsing based on manual input, cloud storage, and a third-party data source (internet).

In some embodiments, the look-up data is calculated to obtain predicted viscosity data by combining the table with the formulation and the time of the test.

In some embodiments, the predicted viscosity data may be calculated by setting different values of parameters corresponding to the alloy powder content and the particle size of the solder paste of the current formulation. For example, the viscosity increases due to the increase of the content of the solder paste alloy powder, and the viscosity decreases when the granularity of the solder paste alloy powder increases, and different values of parameters can be set based on the rule and historical data.

In some embodiments, determining predicted viscosity data may also take into account the effects of production environment and instrumentation, among other factors. For example, as the temperature increases in the production environment, the viscosity decreases and the force of agitation ceases to cause the viscosity to increase.

Step 330, determining whether to issue an alarm instruction based on whether the relationship between the predicted viscosity data and the first viscosity data satisfies a second sub-preset condition. In some embodiments, step 330 is performed by a decision module.

In some embodiments, the second sub-preset condition is mainly used for judging the relationship between the predicted viscosity data and the first viscosity data. For example, the difference between the first viscosity data and the predicted viscosity data may not be greater than 10Pa/s (without limitation, the specific value may be adjusted according to production requirements).

For the related description of the alarm command, refer to the corresponding part of fig. 2.

In some embodiments, the first viscosity data is compared with the predicted viscosity data, so that whether the viscometer has a fault or not can be found in time, and the accuracy of the first viscosity data is ensured, so that the normal operation of solder paste production is not interfered by the fault of the viscometer.

FIG. 4 is a schematic illustration of a method of solder paste production based on viscosity identification model determination of predicted viscosity data, in accordance with certain embodiments of the present description. The components of the schematic flow chart 400 shown in FIG. 4 include a production recipe 411, production process parameters 412, a viscosity identification model 420, and predicted viscosity data 430.

The viscosity identification model 420 has inputs including the production recipe 411 and the production process parameters 412 and outputs including predicted viscosity data 430 for the solder paste intermediate. Wherein the viscosity identification model 420 is a neural network model. For example, the types of the viscosity recognition model 420 include a Convolutional Neural Network (CNN), a Deep Belief Network (DBN), a Recurrent Neural Network (RNN), a Long Short Term Memory (LSTM), a Support Vector Machine (SVM), and the like.

In some embodiments, the input to the viscosity identification model 420 also includes second viscosity data (not shown). The second viscosity data is determined based on the image data at different stirring speeds, see the description in relation to fig. 5. In some embodiments of the present description, inputting the second viscosity data for training may improve the accuracy of the system.

In some embodiments, the input to the viscosity identification model 420 also includes a detection time (not shown).

In some embodiments of the present disclosure, by inputting the production recipe 411, the production process parameters 412, and other relevant influence parameters into the model for prediction, various influence parameters can be synthesized for prediction, thereby improving the comprehensiveness and accuracy of prediction.

In some embodiments, the viscosity recognition model 420 may be trained based on a large number of training samples with labels. For example, a training sample with the identifier is input into the viscosity recognition model 420, a loss function is constructed through the label and the prediction result of the viscosity recognition model, and the parameters of the model are updated iteratively based on the loss function. And when the trained model meets the preset condition, finishing the training. The preset conditions include loss function convergence, threshold reaching of iteration times and the like.

In some embodiments, the training samples may be production recipes and production process parameters of solder paste, inspection times, etc. recorded during the historical synthesis process. The label may be real viscosity data or the like.

FIG. 5 is an exemplary flow chart of another method of solder paste production to determine whether predetermined conditions are met, according to some embodiments of the present disclosure. The process 500 shown in FIG. 5 includes step 510, step 520, step 530, and step 540.

Step 510, determine whether the first viscosity data satisfies a first sub-predetermined condition. In some embodiments, step 510 is performed by a decision module.

For a description on whether the first viscosity data satisfies the first sub-preset condition, please refer to fig. 3.

And step 520, responding to the requirements, and acquiring image data of the semi-finished solder paste product at different stirring speeds. In some embodiments, step 520 is performed by an acquisition module.

In some embodiments, the image data at different agitation speeds may be image data acquired when only the agitation speed is different and the remaining conditions (e.g., production recipe, production process, production environment, and instrumentation) are the same.

In some embodiments, the different stirring speeds may be the stirring speeds often used in the production of multiple solder pastes. In some embodiments, the different stirring speeds may be a number of stirring speeds at intervals that are preset by a person within a range of stirring speeds suitable for solder paste production.

In some embodiments, the image data can be obtained by a camera, a monitoring probe, etc. on the solder paste production apparatus 140.

In some embodiments, the time of acquisition of the image may refer to the time at which the first viscosity is acquired, possibly at some point in the preparation process.

Step 530, determining second viscosity data based on the image data at the different stirring speeds. In some embodiments, step 530 is performed by a prediction module.

In some embodiments, the image may be sent to a user, user input data may be obtained, and the second viscosity data may be determined. For example, the image may be sent to a user in the form of a video, and the user estimates the viscosity based on experience with the image.

In some embodiments, the second viscosity data may be determined by a second viscosity identification model.

In some embodiments, the second viscosity identification model includes sub-identification models corresponding to a plurality of different stirring speeds, each sub-identification model for identifying an image corresponding to a stirring speed, and sub-second viscosity data corresponding to the stirring speed is determined. And calculating a plurality of sub-second viscosity data corresponding to a plurality of different stirring speeds to obtain second viscosity data. The operation method includes averaging, weighted averaging, and the like.

In some embodiments, the type of the sub-recognition model may be CNN.

In some embodiments, when the sub-second viscosity data corresponding to the plurality of different stirring speeds are weighted and summed, the weight of each sub-second viscosity data is determined based on the recognition accuracy of the corresponding sub-recognition model.

In some embodiments, the recognition accuracy of the sub-recognition model may be determined based on the test accuracy obtained during the test phase of the sub-recognition model training process. For example, the test accuracy of the model is taken as a weight when the training is directly satisfied.

In some embodiments, when weighted summing the corresponding sub-second viscosity data for a plurality of different agitation speeds, the weight for each sub-second viscosity data is determined based on the recognition confidence of the corresponding sub-recognition model output. For example, the recognition confidence is directly used as the weight.

In some embodiments, the respective recognition models may be trained separately.

In some embodiments, for a certain sub-recognition model, the sub-recognition model can be trained based on a large number of training samples with labels. For example, a training sample with a label is input into the sub-recognition model, a loss function is constructed through the label and the prediction result of the sub-recognition model, and the parameters of the model are updated iteratively based on the loss function. When the trained model satisfies the condition, the training is finished. Wherein the condition is that the loss function converges, the number of iterations reaches a threshold, etc.

The training sample is training data of the stirring speed corresponding to the sub-recognition model, and the training data is specifically an image of a sample tin paste semi-finished product or a historical tin paste semi-finished product at the stirring speed. The labels may be the true viscosity data to which the images correspond respectively. The label can be obtained by means of viscometer measurement and manual labeling.

And 540, determining whether to send out the alarm instruction or not based on whether the relation between the first viscosity data and the second viscosity data meets a third sub-preset condition or not. In some embodiments, step 540 is performed by a decision module.

In some embodiments, the third sub-preset condition is mainly used for judging the relationship between the second viscosity data and the first viscosity data. For example, the difference between the first viscosity data and the second viscosity data may not be greater than 10Pa/s (no limitation is made here, and the specific value may be adjusted according to production needs).

In some embodiments of the present description, the image data at different speeds is combined with the second viscosity identification model, and the first viscosity data is compared with the obtained second viscosity data, so that the first viscosity data measured by the viscometer can be effectively monitored, the accuracy of the value is ensured, and the quality monitoring effect in the solder paste production process is improved.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of producing solder paste, the method comprising:

acquiring first viscosity data of a semi-finished solder paste product based on a viscometer, wherein the semi-finished solder paste product is generated based on mixing and stirring of solder powder and soldering flux;

determining whether a preset condition is met or not through a judging module based on the first viscosity data;

responding to the judgment, and sending an alarm instruction by the early warning module;

in response, the production module sends a finished product preparation instruction to enable the finished product preparation device to generate a finished solder paste product through vacuumizing and pouring of the semi-finished solder paste product.

2. The solder paste production method of claim 1, wherein the preset conditions include a first sub-preset condition and a second sub-preset condition, and the determining, by the determination module, whether the preset conditions are satisfied based on the first viscosity data includes:

judging whether the first viscosity data meets the first sub-preset condition or not through the judging module;

in response to the satisfaction, determining, by a prediction module, predicted viscosity data of the solder paste semi-finished product based on a production recipe and production process parameters, the production recipe including a content of the flux and a content of the solder powder, the production process parameters including a process parameter of stirring;

and determining whether to send out the alarm instruction or not by the judging module based on whether the relation between the predicted viscosity data and the first viscosity data meets the second sub-preset condition or not.

3. The solder paste manufacturing method of claim 2, wherein determining the predicted viscosity data of the solder paste intermediate product based on the manufacturing recipe and the manufacturing process parameters comprises:

inputting the production formula and the production process parameters into a viscosity identification model in the prediction module, and outputting the predicted viscosity data of the semi-finished solder paste product;

wherein the viscosity identification model is a neural network model.

4. The solder paste production method of claim 1, wherein the preset conditions include a first sub-preset condition and a third sub-preset condition, and the determining, by the determination module, whether the preset conditions are satisfied based on the first viscosity data includes:

responding to the requirements, and acquiring image data of the semi-finished solder paste product at different stirring speeds;

determining, by a prediction module, second viscosity data based on the image data at the different stirring speeds;

and determining whether to send out the alarm instruction or not by the judging module based on whether the relation between the first viscosity data and the second viscosity data meets the third sub-preset condition or not.

5. The solder paste production system is characterized by comprising an acquisition module, a judgment module, an early warning module and a production module:

the obtaining module is used for obtaining first viscosity data of a semi-finished solder paste product based on a viscometer, and the semi-finished solder paste product is generated based on mixing and stirring of solder powder and soldering flux;

the judging module is used for determining whether a preset condition is met or not based on the first viscosity data;

in response, the production module sends a finished product preparation instruction to enable a finished product preparation device to generate a solder paste finished product through vacuumizing and pouring of the solder paste semi-finished product.

6. The solder paste production system of claim 5, wherein the preset conditions include a first sub-preset condition and a second sub-preset condition, and the determining module is further configured to:

judging whether the first viscosity data meets the first sub-preset condition or not;

the solder paste production system also includes a prediction module, the prediction module further to:

in response to the satisfaction, determining predicted viscosity data of the solder paste semi-finished product based on a production recipe and production process parameters, the production recipe including a content of the flux and a content of the solder powder, the production process parameters including a process parameter of stirring;

the determining module is further configured to:

determining whether to issue the alarm instruction based on whether a relationship between the predicted viscosity data and the first viscosity data satisfies the second sub-preset condition.

7. The solder paste production system of claim 6, the prediction module further to:

inputting the production formula and the production process parameters into a viscosity identification model, and outputting the predicted viscosity data of the semi-finished solder paste product;

wherein the viscosity identification model is a neural network model.

8. The solder paste production system of claim 5, wherein the preset conditions include a first sub-preset condition and a third sub-preset condition, and the determining module is further configured to:

the acquisition module is further configured to:

the prediction module is further to:

determining second viscosity data based on the image data at the different agitation speeds;

the determining module is further configured to:

and determining whether to send out the alarm instruction or not based on whether the relation between the first viscosity data and the second viscosity data meets the third sub-preset condition or not.

9. A solder paste production apparatus, the apparatus comprising at least one processor and at least one memory;

the at least one memory is for storing computer instructions;

the at least one processor is configured to execute at least a portion of the computer instructions to implement the solder paste production method of any of claims 1-4.

10. A computer-readable storage medium storing computer instructions, the computer instructions when read by a computer executing the method of producing solder paste according to any one of claims 1 to 4.