CN115169745A - Production quality prediction method, system and computer readable medium - Google Patents
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Abstract
The invention provides a production quality prediction method, a production quality prediction system and a computer readable medium. Wherein the method is adapted to predict the quality of a product produced by at least part of a continuous production process. The method comprises the following steps: providing a plurality of sensors in a plurality of production runs of a produced product, including a continuous production run, the sensors adapted to collect process data; acquiring the acquisition time of each sensor in the production process of a product; calculating a production completion time after any sensor for any sensor; calculating the product time corresponding to any sensor according to the acquisition time and the production completion time; and when the product is obtained after all production processes are finished, estimating the quality condition of the product according to the process data and the product time acquired by the plurality of sensors. By correlating the process data collected by the sensor with the product time for completion of the product, the present invention can predict the quality of each product unit of the product.
Description
Technical Field
The present invention relates generally to the field of intelligent manufacturing, and more particularly to a method, system, and computer readable medium for production quality prediction.
Background
The manufacture of a product often needs to be completed through a plurality of continuous production processes, for example, a battery diaphragm is used as an important component of a new energy automobile battery, and the diaphragm is used for isolating the positive electrode and the negative electrode of the battery and enabling electrons in the battery not to freely pass through the battery, so that ions in an electrolyte can freely pass between the positive electrode and the negative electrode. A common separator manufacturing process includes melting, extruding and blowing a polyolefin resin to form a crystalline polymer film, then performing crystallization heat treatment and annealing to obtain a highly oriented film structure, and then stretching at a high temperature to test separation of crystalline sections to form a porous structure battery separator. The whole production process is a black box, and the quality of each meter of diaphragm cannot be predicted in advance. The production of the diaphragm can only be checked, and the currently common diaphragm check method is to take the last meter from the rolled diaphragm for detection, and take the quality of the last meter of diaphragm as the quality of the whole roll of diaphragm. The quality detection method ensures that the quality of each meter of diaphragm is not controllable, and delivery of the diaphragm with quality problems can possibly cause spontaneous combustion of batteries and new energy automobiles, thereby bringing great economic loss to customers and companies.
Disclosure of Invention
The invention aims to provide a production quality prediction method, a production quality prediction system and a computer readable medium, and solve the problem that the quality of each product unit cannot be predicted.
In order to solve the above-mentioned technical problems, the present invention provides a production quality prediction method suitable for performing quality prediction of a product produced through at least part of continuous production processes. The method comprises the following steps: providing a plurality of sensors in a plurality of production runs including the continuous production run that produce the product, the sensors adapted to collect process data; acquiring the acquisition time of each sensor in the production process of the product; calculating, for any sensor, a production completion time after the any sensor; calculating the product time corresponding to any sensor according to the acquisition time and the production completion time; and when all production processes are finished to obtain the product, estimating the quality condition of the product according to the process data and the product time acquired by the plurality of sensors.
In some embodiments of the present invention, the product includes a plurality of product units, and the step of estimating the quality condition of the product specifically includes estimating the quality condition of each product unit according to the process data collected by the plurality of sensors and the product time.
In some embodiments of the invention, the product comprises one or more sets of full rolls of membrane, the product units comprise every n meters of membrane in each full roll of membrane, and estimating the quality of the product comprises estimating the quality of every n meters of membrane in each full roll of membrane.
In some embodiments of the present invention, the last production process for producing each whole roll of membrane is a rolling process, and the method further includes performing manual quality inspection on the last N meters of membrane after rolling of each whole roll of membrane while estimating the quality of each N meters of membrane in each whole roll of membrane, where N is a constant greater than 0.5.
In some embodiments of the present invention, the method further comprises predetermining a process production distance element and a process production speed for each of said production processes, and calculating a process production time based on said process production distance and process production speed to calculate the off-process remaining production time.
In some embodiments of the present invention, the step of calculating the production completion time specifically includes calculating an intra-process remaining production time and an extra-process remaining production time, wherein, for any process Px, n sensors S1, S2 … Si … Sn are provided in the process Px, and for the sensor Si, the intra-process remaining production time is a time required from a production location of the sensor Si to a production location where the process Px is completed; and for the sensor Si, the off-process remaining production time is a time required for all production processes after completion of the any one process Px.
In some embodiments of the invention, the method further comprises: reading the process data from one or more of the plurality of sensors at preset intervals; comparing the process data with a good product parameter range constrained by a good product parameter model; and in the production process of the product, when any process data exceeds the range of the good product parameters, carrying out sound alarm and/or prompting through a human-computer interaction interface.
In some embodiments of the invention, the method further comprises: before the product is formally produced, at least one good product meeting the quality requirement is produced; and determining the range of the good product parameters according to the process data acquired by the sensors during the production of the good product, thereby determining the good product parameter model.
In some embodiments of the invention, the method further comprises: in the production process of the product, drawing a process data change curve taking the production time as a horizontal axis for each sensor according to the process data collected by the plurality of sensors; and adjusting the process parameters corresponding to the production process of each sensor in real time according to the variation trend of the process data variation curve.
In some embodiments of the present invention, the method further includes using the process data and the product time collected by the plurality of sensors as input data of the good product parameter model, and estimating the quality of the product after calculating through the good product parameter model.
In order to solve the above technical problem, the present invention provides a production quality prediction system, including: a memory for storing instructions executable by the processor; and a processor for executing the instructions to implement the aforementioned production quality prediction method.
To solve the above technical problem, the present invention provides a computer readable medium storing computer program code, which when executed by a processor implements the aforementioned production quality prediction method.
Compared with the prior art, the invention has the following advantages:
the production quality prediction method of the invention can predict the quality condition of each product unit of the product by carrying out time translation on the acquisition time of the process data acquired by the sensor and associating the process data acquired by the sensor with the product time of the finished product; the method comprises the following steps of establishing a good product parameter range for each sensor by learning process data of a plurality of good products, and making a production standard of the product; the process data can be monitored in real time by comparing the process parameters acquired in real time with the ranges of good product parameters, so that the stability of the product quality is ensured; relevant process parameters are automatically adjusted through the sensor data curve and the variation trend which are obtained in real time, and the stability of the process parameters is ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the principle of the invention. In the drawings:
FIG. 1 is a flow diagram of a production quality prediction method according to an embodiment of the present invention;
FIG. 2 is a timing diagram of a manufacturing process according to an embodiment of the present invention;
FIG. 3 is a product unit quality report table according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of real-time monitoring of process data according to one embodiment of the present invention;
FIG. 5 is a system block diagram of a production quality prediction system according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to include 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.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be discussed further in subsequent figures.
In the description of the present application, it is to be understood that the orientation or positional relationship indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally based on the orientation or positional relationship shown in the drawings, and are used for convenience of description and simplicity of description only, and in the case of not making a reverse description, these directional terms do not indicate and imply that the device or element being referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore, should not be considered as limiting the scope of the present application; the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
For ease of description, spatially relative terms such as "over … …", "over … …", "over … …", "over", etc. may be used herein to describe the spatial positional relationship of one device or feature to another device or feature as shown in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of protection of the present application is not to be construed as being limited. Further, although the terms used in the present application are selected from publicly known and used terms, some of the terms mentioned in the specification of the present application may be selected by the applicant at his or her discretion, the detailed meanings of which are described in relevant parts of the description herein. Further, it is required that the present application is understood not only by the actual terms used but also by the meaning of each term lying within.
It will be understood that when an element is referred to as being "on," "connected to," "coupled to" or "contacting" another element, it can be directly on, connected or coupled to, or contacting the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly on," "directly connected to," "directly coupled to" or "directly contacting" another element, there are no intervening elements present. Similarly, when a first component is said to be "in electrical contact with" or "electrically coupled to" a second component, there is an electrical path between the first component and the second component that allows current to flow. The electrical path may include capacitors, coupled inductors, and/or other components that allow current to flow even without direct contact between the conductive components.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, various steps may be processed in reverse order or simultaneously. Meanwhile, other operations are added to or removed from these processes.
FIG. 1 is a flow diagram of a production quality prediction method 100 according to an embodiment of the invention. The production quality prediction method 100 is adapted to predict the quality of a product produced by at least a portion of a continuous production process. As shown in fig. 1, the production quality prediction method 100 includes the steps of:
step S110: providing a plurality of sensors in a plurality of production runs including the continuous production run that produce the product, the sensors adapted to collect process data;
step S120: acquiring the acquisition time of each sensor in the production process of the product;
step S130: calculating, for any sensor, a production completion time after the any sensor;
step S140: calculating the product time corresponding to any sensor according to the acquisition time and the production completion time; and
step S150: and when all production procedures are finished to obtain the product, estimating the quality condition of the product according to the process data acquired by the plurality of sensors and the product time.
The following describes steps S110 to S150 in detail.
In step S110, a plurality of sensors may be provided in each of the continuous production processes, and the number of sensors is not limited in the present application. The type of sensor includes, but is not limited to, temperature sensors, pressure sensors, rotational speed sensors, etc., and the application is not limited to the type of sensor. The sensors are adapted to collect process data including, but not limited to, temperature, pressure, rotational speed, current, etc. In some embodiments, the process data is collected by the sensor at predetermined intervals during the production process, and the predetermined intervals range from 50 to 500ms. For example, during the production process, process data of each process is collected by each sensor every 100 ms. The value of the preset time may be set within the range as desired.
In step S120, acquiring the acquisition time of each sensor may be achieved by a timestamp corresponding to the process data acquired by the sensor.
In step S130, a production completion time after any one sensor is calculated for the sensor. The step of calculating the production completion time specifically includes calculating an intra-process remaining production time and an extra-process remaining production time, wherein for any process Px, n sensors S1, S2 … Si … Sn are provided in the process Px, and for the sensor Si, the intra-process remaining production time is a time required from a production position of the sensor Si to a production position at which the process Px is completed; and for the sensor Si, the off-process remaining production time is a time required for all production processes after completion of the any one process Px. Fig. 2 is a timing diagram of a manufacturing process according to an embodiment of the present invention. As shown in fig. 2, the production process includes a production process 21, a production process 22, a production process 24, and a production process 25. The sensor 231 is arranged on the production process 23, and if the time when the sensor 231 collects the process data on the production process 23 is t, calculating the production completion time after the sensor 231 comprises calculating the in-process remaining production time Δ t1 and the out-of-process remaining production time Δ t2.
In some embodiments, the step of calculating the remaining in-process production time includes predetermining a distance from a location at which the sensor is located to its corresponding location at which the production process is completed, and a process production speed for the production process, and calculating the remaining in-process production time based on the distance and the process production speed. As shown in FIG. 2, the sensor 231 is located at a distance L from the completion of the production process 23 S And a process production speed V of the production process 23 3 And the residual production time delta t1= L in the process S /V 3 。
In some embodiments, the step of calculating the out-of-process remaining production time includes predetermining a process production distance element and a process production speed for each of the production processes, and calculating the out-of-process remaining production time based on the process production distance and the process production speed to calculate the out-of-process remaining production time. As shown in fig. 2, the production process 24 and the production process 25 are included after the production process 23, and the time required for production after the sensor 231 is equal to the time required for the production process 24 and the time required for the production process 25, the extra-process remaining production time Δ t2 can be obtained by the following calculation formula:
Δt2＝L 4 /V 4 +L 5 /V 5
wherein L is 4 Is a process of the production process 24 to produce a distance cell, L 5 Is a process of the production process 25 for producing a distance cell, V 4 Is the process production speed, V, of the production process 24 5 Is the process production rate of the production process 25.
In step S140, the product time corresponding to any sensor is equal to the collection time plus the production completion time. As shown in fig. 2, the product time T corresponding to the sensor 231 is denoted as T, and the product time T can be obtained by the following calculation formula:
T＝t+Δt1+Δt2
wherein t is the acquisition time, Δ t1 is the in-process remaining production time, and Δ t2 is the out-of-process remaining production time.
In step S150, when all the production processes are completed to obtain the product, the quality condition of the product is estimated according to the process data and the product time collected by the plurality of sensors. The product comprises a plurality of product units, and the step of predicting the quality condition of the product specifically comprises predicting the quality condition of each product unit according to the process data collected by the plurality of sensors and the product time. Through the steps S110 to S140, the acquisition time of the process data acquired by the sensor is time-shifted, the process data acquired by the sensor is associated with the product time of the finished product, and further the process data acquired by the sensor can be associated with the product unit of the product. In particular, on the one hand, the product unit can be located according to the product time, and on the other hand, the process data for producing the product unit can be found according to the product time. The quality of the product unit can be estimated based on the process data for producing the product unit. For example, process data for producing the product unit may be analyzed by an artificial intelligence model to predict the quality of the product unit.
In some embodiments, at least one good product meeting quality requirements is produced prior to formal production of the product; and determining the good product parameter range according to the process data acquired by the sensors during the production of the good products so as to determine the good product parameter model. Specifically, samples of some products are produced before the products are formally produced, process data and product time during sample production are collected, manual inspection is carried out on product units of the samples, and the quality of the product units is manually marked as good products or defective products. And taking the product process data and the quality of the manually marked product unit as a training set, a testing set and a verification set of the good product parameter model, and training the model to obtain the trained good product parameter model. The trained good product parameter model can classify the product quality according to the process data. In some embodiments, the prediction method further includes using the process data and the product time collected by the plurality of sensors as input data of a trained good product parameter model, and estimating the quality condition of each product unit of the product after calculating through the good product parameter model. Fig. 3 is a product unit quality report table 300 in accordance with an embodiment of the present invention. As shown in fig. 3, the first column of the quality report table 300 is the product time, and the remaining columns are the product quality results, which include good and bad products. Wherein the non-defective products are expressed by GREEN, the defective products are expressed by YELLOW, and the quality condition of each product unit of the product can be known through the quality report table 300, so that the problem that the delivered product quality is uncontrollable is solved.
In some embodiments, the product comprises one or more sets of full rolls of membrane, the product units comprise every n meters of membrane in each full roll of membrane, and estimating the quality of the product comprises estimating the quality of every n meters of membrane in each full roll of membrane.
In some embodiments, the last production process for producing each whole roll of membrane is a rolling process, and the method further includes performing manual quality inspection on the last N meters of membrane after rolling of each whole roll of membrane while estimating the quality of each N meters of membrane in each whole roll of membrane, where N is a constant greater than 0.5, such as the last meter. Illustratively, the quality result of the last N meters of diaphragms of the artificial quality detection can be compared with the estimated quality result of the last N meters of diaphragms, if the results are consistent, the corresponding process data and the quality result of the last N meters of diaphragms can be added into the data set of the good product parameter model to train the model, and the accuracy of the good product parameter model prediction is further improved. And if the results are inconsistent, adjusting the trained good product parameter model.
In some embodiments, the process data of different product units of a sample are analyzed by big data technology, key process parameters causing product quality fluctuation are obtained by comparison, and the process parameters are adjusted and optimized. And continuously producing a batch of samples according to the optimized process parameters, collecting process data and product time during sample production, manually checking the product units of the samples, and judging whether the yield of the product units of the samples reaches a certain threshold value (for example, the yield is 90%). If so, learning the process data acquired by the plurality of sensors corresponding to all product units with good quality, and learning to obtain a good product parameter range corresponding to each sensor, wherein the good product parameter range can be used as a constraint condition in the formal production process to monitor the production data in real time.
In some embodiments, the prediction method further comprises reading the process data from one or more of the plurality of sensors at preset time intervals; comparing the process data with a good product parameter range constrained by a good product parameter model; and in the production process of the product, when any process data exceeds the range of the good product parameters, carrying out sound alarm and/or prompting through a human-computer interaction interface. FIG. 4 is a schematic diagram of real-time monitoring of process data according to an embodiment of the present invention. As shown in fig. 4, a process data variation curve 40 is a curve plotted with production time as the horizontal axis according to process data collected by one of the sensors, and the range of good product parameters learned by the sensor includes a maximum threshold 41 and a minimum threshold 42. For example, in the setting of this embodiment, in the production process of the product, when any one of the process data on the process data change curve 40 exceeds the maximum threshold 41, a sound alarm may be performed and/or a prompt may be performed through a human-computer interface, as can be seen from fig. 4, the change curve of the process data is always maintained near the minimum threshold 42, which indicates that the production process is normal. The invention is not so limited and in some embodiments of the invention, conditions may be set where process data exceeds a maximum threshold or is less than a minimum threshold while acting as an alarm/prompt.
In some embodiments, the prediction method further comprises plotting a process data variation curve for each sensor with production time as the horizontal axis from the process data collected by the plurality of sensors during production of the product; and adjusting the process parameters corresponding to the production process of each sensor in real time according to the change trend of the process data change curve. As shown in fig. 4, the process parameters corresponding to the production process of each sensor can be adjusted in real time according to the variation trend of the process data variation curve 40. For example, when the process data 401 in a period of time on the process data change curve 40 exceeds the minimum threshold 42 and the slope of the curve in the period of time exceeds a certain threshold, which indicates that the process data 401 is changed violently in the period of time, the process parameters corresponding to the production process in which the sensor is located may be automatically adjusted, and the stability of the process data is ensured.
The production quality prediction method of the invention can predict the quality condition of each product unit of the product by carrying out time translation on the acquisition time of the process data acquired by the sensor and associating the process data acquired by the sensor with the product time of the finished product; the method comprises the following steps of establishing a good product parameter range for each sensor by learning process data of a plurality of good products, and making a production standard of the product; the process data can be monitored in real time by comparing the process parameters acquired in real time with the range of good product parameters, so that the stability of the product quality is ensured; relevant process parameters are automatically adjusted through a sensor data curve and a change trend which are obtained in real time, and the stability of the process parameters is ensured.
An embodiment of the present invention further provides a production quality prediction system 50 as shown in fig. 5. According to FIG. 5, the production quality prediction system 50 may include an internal communication bus 51, a Processor (Processor) 52, a Read Only Memory (ROM) 53, a Random Access Memory (RAM) 54, and a communication port 55. When implemented on a personal computer, the production quality prediction system 50 may also include a hard disk 56.
The internal communication bus 51 may enable data communication among the components of the production quality prediction system 50. Processor 52 may make the determination and issue the prompt. In some embodiments, processor 52 may be comprised of one or more processors. The communication port 55 may enable data communication between the production quality prediction system 50 and the outside. In some embodiments, the production quality prediction system 50 may send and receive information and data from the network through the communication port 55.
The production quality prediction system 50 may also include various forms of program storage units and data storage units, such as a hard disk 56, read Only Memory (ROM) 53 and Random Access Memory (RAM) 54, capable of storing various data files for computer processing and/or communication, and possibly program instructions for execution by the processor 52. Processor 52 executes these instructions to implement the production quality prediction method described above. The results processed by the processor are communicated to the user device through the communication port and displayed on the user interface.
In addition, another aspect of the present invention provides a computer readable medium storing computer program code, which when executed by a processor implements the production quality prediction method described above.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing disclosure is by way of example only, and is not intended to limit the present application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. 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 present application is included in at least one embodiment of the present application. 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 present application may be combined as appropriate.
Aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. The processor may be one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital signal processing devices (DAPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or a combination thereof. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, in one or more computer readable media. For example, computer-readable media can include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic tape … …), optical disks (e.g., compact Disk (CD), digital Versatile Disk (DVD) … …), smart cards, and flash memory devices (e.g., card, stick, key drive … …).
The computer readable medium may comprise a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer-readable medium may be any computer-readable medium that can be coupled to an instruction execution system, apparatus, or device for communicating, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, radio frequency signals, or the like, or any combination of the preceding.
Similarly, it should be noted that in the preceding description of embodiments of the application, 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 require more features than are expressly recited in the claims. 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.
Although the present application has been described with reference to the present specific embodiments, it will be recognized by those skilled in the art that the foregoing embodiments are merely illustrative of the present application and that various changes and substitutions of equivalents may be made without departing from the spirit of the application, and therefore, it is intended that all changes and modifications to the above-described embodiments that come within the spirit of the application fall within the scope of the claims of the application.
Claims (12)
1. A method for predicting production quality, adapted to predict the quality of a product produced by at least a portion of a continuous production process, comprising the steps of:
providing a plurality of sensors in a plurality of production runs including the continuous production run that produce the product, the sensors being adapted to collect process data;
acquiring the acquisition time of each sensor in the production process of the product;
calculating, for any sensor, a production completion time after the any sensor;
calculating the product time corresponding to any sensor according to the acquisition time and the production completion time; and
and when all production procedures are finished to obtain the product, estimating the quality condition of the product according to the process data acquired by the plurality of sensors and the product time.
2. The method of claim 1, wherein the product comprises a plurality of product units, and wherein the step of estimating the quality of the product comprises estimating the quality of each product unit based on the process data collected by the plurality of sensors and the product time.
3. The method of claim 2, wherein the product comprises one or more sets of full rolls of membrane, the product units comprise every n meters of membrane in each full roll of membrane, and estimating the quality of the product comprises estimating the quality of every n meters of membrane in each full roll of membrane.
4. The method according to claim 3, wherein the last process step of producing each whole roll of membrane is a rolling process step, and the method further comprises performing manual quality inspection on the last N meters of membrane after rolling while estimating the quality of each N meters of membrane in each whole roll of membrane, wherein N is a constant greater than 0.5.
5. The method of any one of claims 1 to 4, further comprising predetermining a process production distance element and a process production speed for each of said production processes, and calculating a process production time based on said process production distance and process production speed to calculate the off-process remaining production time.
6. The method of any of claim 5, wherein the step of calculating the production completion time specifically includes calculating an in-process remaining production time and an out-of-process remaining production time, wherein,
for any process Px, n sensors S1, S2 … Si … Sn are provided in the process Px, and for the sensor Si, the remaining in-process production time is the time required from the production position of the sensor Si to the production position where the process Px is completed; and
the extra-process remaining production time for the sensor Si is the time required for all production processes after completion of any one of the processes Px.
7. The method of any one of claims 1 to 4, further comprising:
reading the process data from one or more of the plurality of sensors at preset intervals;
comparing the process data with a good product parameter range constrained by a good product parameter model; and
and in the production process of the product, when any process data exceeds the non-defective product parameter range, carrying out sound alarm and/or prompting through a human-computer interaction interface.
8. The method of claim 7, further comprising:
before the product is formally produced, at least one good product meeting the quality requirement is produced; and
and determining the range of the good product parameters according to the process data acquired by the sensors during the production of the good product, thereby determining the good product parameter model.
9. The method of claim 8, further comprising:
in the production process of the product, drawing a process data change curve taking the production time as a horizontal axis for each sensor according to the process data collected by the plurality of sensors; and
and adjusting the process parameters corresponding to the production process of each sensor in real time according to the variation trend of the process data variation curve.
10. The method of claim 8, further comprising using the process data and the product time collected by the plurality of sensors as input data of the good parameter model, and estimating the quality of the product after calculation by the good parameter model.
11. A production quality prediction system comprising:
a memory for storing instructions executable by the processor; and a processor for executing the instructions to implement the method of any one of claims 1-10.
12. A computer-readable medium having stored thereon computer program code which, when executed by a processor, implements the method of any of claims 1-10.
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| CN202210967905.0A CN115169745A (en) | 2022-08-12 | 2022-08-12 | Production quality prediction method, system and computer readable medium |
| PCT/CN2023/112358 WO2024032740A1 (en) | 2022-08-12 | 2023-08-10 | Production quality prediction method and system, and computer readable medium |
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| WO2024032740A1 (en) * | 2022-08-12 | 2024-02-15 | 安徽新永拓新材料有限公司 | Production quality prediction method and system, and computer readable medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114760969A (en) * | 2019-11-28 | 2022-07-15 | 花王株式会社 | Mass measurement method and mass measurement device |
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| CN111679636B (en) * | 2020-05-11 | 2021-11-09 | 杭州睿疆科技有限公司 | System, method and computer equipment for processing production process parameters |
| KR102470836B1 (en) * | 2020-11-10 | 2022-11-25 | 경기대학교 산학협력단 | Artificial intelligence based continuous process control device, method of predicting quality and improving yield using the same |
| WO2022133828A1 (en) * | 2020-12-23 | 2022-06-30 | 西门子股份公司 | Product quality tracking method and apparatus, computing device, and storage medium |
| CN114444986B (en) * | 2022-04-11 | 2022-06-03 | 成都数之联科技股份有限公司 | Product analysis method, system, device and medium |
| CN114819082A (en) * | 2022-04-26 | 2022-07-29 | 升发云(绍兴)科技有限公司 | Method, device and medium for predicting one-time check qualification rate of factory products |
| CN115169745A (en) * | 2022-08-12 | 2022-10-11 | 芜湖云一新材料科技有限公司 | Production quality prediction method, system and computer readable medium |
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| CN114760969A (en) * | 2019-11-28 | 2022-07-15 | 花王株式会社 | Mass measurement method and mass measurement device |
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| WO2024032740A1 (en) * | 2022-08-12 | 2024-02-15 | 安徽新永拓新材料有限公司 | Production quality prediction method and system, and computer readable medium |
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