Process optimization method and system for net film composite product

Abstract

The invention belongs to the technical field of net films. The invention provides a process optimization method and system for net film composite products, and the method comprises the steps: carrying out the simulation of key process parameters through simulation software, and obtaining a second comprehensive quality evaluation value of each net film composite product according to the evaluation of each simulation result; comparing the second comprehensive quality evaluation value with a first comprehensive quality evaluation value corresponding to the pressing roller, and determining a pilot production quantity corresponding to the key process parameters according to a comparison result; importing the key process parameters into a control system of a pressing roller, so that the control system carries out trial production on samples of the net film composite products corresponding to the trial production quantity according to the key process parameters; and respectively evaluating each sample according to a preset rule to obtain a third comprehensive quality evaluation value, and if the third comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value, adopting the key process parameters. The number of pilot production samples can be reduced, so that the pilot production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of omentum technology, and in particular to a process optimization method and system for an omentum composite product. Background Art

[0002] Omentum composite products are products that combine omentum with other materials through a specific process to form products with new characteristics and functions. This type of product combines the advantages of different materials to meet the needs of specific application scenarios. Omentum composite products can be divided into many types according to the type of omentum used and the type of composite material. For example, hot melt adhesive omentum composite products, diversion mesh perforated isolation film composite materials, etc.

[0003] Lamination temperature and lamination force are key process parameters that affect the quality of mesh composite products, such as the firmness of mesh composite products, the fact that the substrates of mesh composite products cannot be easily separated or torn by external forces, and product flatness, appearance defects, etc. In the prior art, technicians usually optimize the above key process parameters that are theoretically better based on their production experience or the simulation results of the simulation system, and then introduce the key process parameters into the lamination roller, which conducts trial production according to the better key process parameters to verify the optimization effect of the better key process parameters.

[0004] However, the above existing process optimization methods lack effective strategies for determining the number of trial production and other strategies. A fixed number of trial production is generally adopted. If the number of trial production is set too high, the cost of trial production will be greatly increased. If the number of trial production is set too low, it may be difficult to verify the new key process parameters. Therefore, how to determine the appropriate trial production strategy is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In this regard, the present invention provides a process optimization method, system, electronic equipment, computer storage medium and computer program product for a mesh composite product to solve the above-mentioned technical problems.

[0006] The present invention discloses a process optimization method for a mesh composite product, the method comprising the following steps: receiving key process parameters to be verified, the key process parameters including the lamination temperature and lamination force of a lamination roller; using simulation software to simulate the key process parameters based on a dynamic model of the lamination roller to obtain a number of simulation results, and evaluating a second comprehensive quality evaluation value of each mesh composite product according to each of the simulation results; comparing the second comprehensive quality evaluation value with a first comprehensive quality evaluation value corresponding to the lamination roller, and determining a trial production quantity corresponding to the key process parameter according to the comparison result; wherein the first comprehensive quality evaluation value corresponds to a standard process parameter currently being used by the lamination roller; importing the key process parameters into a control system of the lamination roller so that the control system trial-produces samples of the mesh composite product corresponding to the trial production quantity according to the key process parameters; evaluating each of the samples according to preset rules to obtain a third comprehensive quality evaluation value, if the third comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value, then the key process parameter is adopted, otherwise the key process parameter is not adopted.

[0007] In some embodiments, if the first quantity of the key process parameters is higher than the quantity threshold; then before using the simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, it also includes: obtaining the key process parameters and their corresponding hot melt adhesive web information and substrate information, and using a convolutional network to extract the first process feature from the key process parameters, the hot melt adhesive web information, and the substrate information; similarly matching the first process feature with the second process features of other verified key process parameters recorded in the database one by one to obtain the second quantity corresponding to each of the key process parameters; wherein the second quantity refers to the number of other key process parameters whose similarity to the key process parameter is higher than the similarity threshold; determining the verification serial number of each of the key process parameters in descending order according to the second quantity; wherein the smaller the second number, the smaller the verification serial number corresponding to the key process parameter, and vice versa.

[0008] In some embodiments, the quantity threshold is determined in the following manner: determining the scheduled formal production time of the mesh composite product corresponding to each of the key process parameters to be verified, and determining the quantity threshold based on the time difference between the current time and the formal production time; wherein the quantity threshold is positively correlated with the time difference.

[0009] In some embodiments, the trial production quantity corresponding to the key process parameter is determined based on the comparison result, including: if the second comprehensive quality assessment value is higher than the first comprehensive quality assessment value corresponding to the lamination roller, and the degree of increase reaches a preset degree, then the trial production quantity is set to a first value; if the second comprehensive quality assessment value is not higher than the first comprehensive quality assessment value corresponding to the lamination roller, then the trial production quantity is set to a second value; wherein, the first value is lower than the second value.

[0010] In some embodiments, the preset degree is determined by calculating a third quantity of the simulation results used to evaluate the second comprehensive quality evaluation value, and matching the third quantity to obtain the preset degree; wherein the preset degree is negatively correlated with the third quantity.

[0011] The present invention also discloses a process optimization system for mesh composite products, characterized in that the system includes at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program, when executed by the processor, executes the following steps: receiving key process parameters to be verified, wherein the key process parameters include the lamination temperature and lamination force of the lamination roller; using simulation software to simulate the key process parameters based on the dynamic model of the lamination roller to obtain a number of simulation results, and evaluating the second comprehensive quality evaluation value of each mesh composite product according to each of the simulation results; and using the second comprehensive quality evaluation value as the evaluation result. The value is compared with the first comprehensive quality evaluation value corresponding to the lamination roller, and the trial production quantity corresponding to the key process parameters is determined according to the comparison result; wherein the first comprehensive quality evaluation value corresponds to the standard process parameters currently being used by the lamination roller; the key process parameters are introduced into the control system of the lamination roller, so that the control system trial-produces samples of the mesh composite product corresponding to the trial production quantity according to the key process parameters; each of the samples is evaluated separately according to preset rules to obtain a third comprehensive quality evaluation value, and if the third comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value, the key process parameters are adopted, otherwise the key process parameters are not adopted.

[0012] In some embodiments, if the first quantity of the key process parameters is higher than the quantity threshold; then before using the simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, it also includes: obtaining the key process parameters and their corresponding hot melt adhesive web information and substrate information, and using a convolutional network to extract the first process feature from the key process parameters, the hot melt adhesive web information, and the substrate information; similarly matching the first process feature with the second process features of other verified key process parameters recorded in the database one by one to obtain the second quantity corresponding to each of the key process parameters; wherein the second quantity refers to the number of other key process parameters whose similarity to the key process parameter is higher than the similarity threshold; determining the verification serial number of each of the key process parameters in descending order according to the second quantity; wherein the smaller the second number, the smaller the verification serial number corresponding to the key process parameter, and vice versa.

[0013] The present invention also discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method described in any of the preceding items when executed.

[0014] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the method described in any of the preceding items is implemented.

[0015] The present invention also discloses a computer program product. The computer program product is prepackaged with computer program codes. When the computer program codes are executed, the method described in any of the above items is implemented.

[0016] The beneficial effect of the present invention is that the scheme of the present invention can dynamically determine a more reasonable and effective trial production quantity based on the simulation results of key process parameters, so as to minimize the number of trial production samples while ensuring that each trial production sample is sufficient to evaluate the effectiveness of the key process parameters, thereby reducing the cost of trial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a structural schematic diagram of a process optimization method for a mesh composite product disclosed in an embodiment of the present invention.

[0019] Figure 2 It is a structural schematic diagram of a process optimization system for a mesh composite product disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0021] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a process optimization method for a mesh composite product, the method comprising the following steps: S1, receiving key process parameters to be verified, the key process parameters including the lamination temperature and lamination force of the lamination roller.

[0023] In this step, the process designer can formulate one or more key process parameters including the lamination temperature and lamination force of the lamination roller based on past production experience and industry general standards. All the data of these key process parameters can be entered into the production management system in a structured table format, waiting for subsequent calls.

[0024] It should be noted that the process designers should also submit the key process parameters to another team of experts for review to check whether the parameter values ​​meet the equipment operating limits to avoid the situation where the lamination temperature is too high and exceeds the maximum power of the heating device, or the lamination force is too large and may damage the mechanical structure of the lamination roller. After the review is correct, the key process parameters will be entered into the production management system.

[0025] S2, using simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, to obtain a number of simulation results, and to evaluate the second comprehensive quality evaluation value of each mesh composite product according to each of the simulation results.

[0026] In this step, an accurate dynamic model of the lamination roller is first established. Considering the material properties of the lamination roller, such as density, elastic modulus, and Poisson's ratio, combined with its geometric dimensions, such as roller diameter and length, a three-dimensional solid model is constructed in the finite element analysis software. Then, constraints are added to the three-dimensional solid model to simulate the rotation and translation restrictions of the lamination roller in actual production; the contact relationship is defined to describe the interaction between the lamination roller and the hot melt adhesive web and substrate, and the force and deformation of the material during the lamination process are accurately simulated.

[0027] Input the key process parameters to be verified, such as the lamination temperature and lamination force, into the constructed dynamic model, and set the calculation parameters such as the simulation time step and convergence criterion. The simulation software starts large-scale numerical calculations based on the principles of thermodynamics and material mechanics, simulating the melting and flow state of the hot melt adhesive under the above key process parameters of the lamination roller, as well as the degree of fit and stress distribution during the composite process of the mesh and substrate. Finally, multiple simulation results of the mesh composite "product" are output (each simulation corresponds to a simulation result), and then the simulation results are integrated (for example, the average value of the evaluation values ​​corresponding to each simulation result is obtained) to obtain the second comprehensive quality evaluation value. This evaluation value comprehensively considers multiple factors such as the firmness, product flatness, and appearance defects of the "product" obtained by simulation, and is presented in the form of a quantitative score.

[0028] S3, comparing the second comprehensive quality assessment value with the first comprehensive quality assessment value corresponding to the lamination roller, and determining the trial production quantity corresponding to the key process parameters based on the comparison result; wherein the first comprehensive quality assessment value corresponds to the standard process parameters currently being used by the lamination roller.

[0029] In this step, the standard process parameters currently used by the lamination roller (i.e., process parameters that have been recognized as effective and are also applicable to the same type of web composite products) are extracted from the production database, and the quality of the web composite products produced using the standard process parameters is evaluated to obtain a first comprehensive quality evaluation value. The first comprehensive quality evaluation value is based on the quality inspection data of a large number of actual production products, and reflects the comprehensive quality status of the product under the current production process level.

[0030] The second comprehensive quality assessment value obtained by simulation is compared with the first comprehensive quality assessment value, and the comparison results include two situations, namely: 1) the second comprehensive quality assessment value is significantly higher than the first comprehensive quality assessment value, and the value used to judge the above "significant" can be, for example, 15%; 2) the second comprehensive quality assessment value is significantly lower than the first comprehensive quality assessment value, or is basically the same as the first comprehensive quality assessment value, and the value used to judge the above "significant" can be, for example, 5%.

[0031] Among them, the first case above shows that the new key process parameters have great potential, and the second case above shows that the new key process parameters do not significantly improve product quality compared to the standard process parameters currently in use, and even cause product quality to decline, and need to be treated with caution. On this basis, the present invention is set to determine the trial production quantity corresponding to the key process parameters in a targeted manner based on the above two comparison results.

[0032] S4, importing the key process parameters into the control system of the lamination roller, so that the control system can trial-produce samples of the web composite product corresponding to the trial-production quantity according to the key process parameters.

[0033] In this step, the above key process parameters to be verified are accurately entered into the control system of the lamination roller. The control system is, for example, based on a programmable logic controller (PLC), which can adjust the power output of the heating module according to the above key process parameters, calibrate the zero point and range of the lamination force sensor, and ensure that the lamination temperature and lamination force can accurately reach the set values, that is, the lamination temperature and lamination force in the above key process parameters. At the same time, it is also necessary to control the operating speed of auxiliary equipment such as unwinding and winding, and the tension control to adapt to the new key process parameters to ensure the coordinated operation of the entire production line. Since this step does not belong to the key content of the present invention, it will not be repeated here.

[0034] Start the production line and transport the substrate, hot melt adhesive web and other raw materials to the lamination roller area in turn according to the set trial production quantity. The lamination roller presses the substrate and the hot melt adhesive web with the corresponding force at the corresponding temperature to complete the composite processing and produce samples of the web composite product.

[0035] S5, evaluating each of the samples according to preset rules to obtain a third comprehensive quality evaluation value. If the third comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value, the key process parameters are adopted; otherwise, the key process parameters are not adopted.

[0036] In this step, the evaluation rules are constructed around core indicators such as firmness, product flatness, and appearance defects. Firmness can be quantitatively tested using a tensile testing machine, and the minimum peeling force standard is set to obtain a firmness evaluation score, wherein the higher the actual peeling force, the higher the firmness evaluation score. Automatic measuring equipment is used to accurately measure the dimensions of the mesh composite product to obtain product flatness information, and the product flatness evaluation score is derived based on this evaluation, wherein the flatter the product, the higher the corresponding product flatness evaluation score. The appearance of the mesh composite product is automatically or manually inspected, mainly to check for defects such as wrinkles, bubbles, and glue spots, and detailed deduction rules are formulated to obtain an appearance defect evaluation score, wherein the milder the appearance defect, the higher the corresponding appearance defect evaluation score.

[0037] By integrating the above-mentioned firmness evaluation scores, product flatness evaluation scores, and appearance defect evaluation scores, a third comprehensive quality evaluation value for evaluating the overall quality of the trial production samples can be obtained. If the third comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value corresponding to the standard process parameters currently in use, it means that the new key process parameters are feasible and can be formally included in the production process file; otherwise, the parameters are rejected and other optimization solutions entered by the process designer are verified.

[0038] Therefore, the solution of the present invention can dynamically determine a more reasonable and effective trial production quantity based on the simulation results of key process parameters, thereby minimizing the number of trial production samples while ensuring that each trial production sample is sufficient to evaluate the effectiveness of key process parameters, thereby reducing the cost of trial production.

[0039] In some embodiments, if the first quantity of the key process parameters is higher than the quantity threshold; then before using the simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, it also includes: obtaining the key process parameters and their corresponding hot melt adhesive web information and substrate information, and using a convolutional network to extract the first process feature from the key process parameters, the hot melt adhesive web information, and the substrate information; similarly matching the first process feature with the second process features of other verified key process parameters recorded in the database one by one to obtain the second quantity corresponding to each of the key process parameters; wherein the second quantity refers to the number of other key process parameters whose similarity to the key process parameter is higher than the similarity threshold; determining the verification serial number of each of the key process parameters in descending order according to the second quantity; wherein the smaller the second number, the smaller the verification serial number corresponding to the key process parameter, and vice versa.

[0040] In an embodiment of the present invention, the designer may develop multiple sets of key process parameters for the lamination process of a type of mesh composite product. Since the simulation process of the above-mentioned simulation software usually takes several hours or even one or two days, when the number of key process parameters to be verified is large, the total duration of the simulation process is difficult to accept. Therefore, the present invention first determines the ranking of each key process parameter, verifies each key process parameter according to the ranking, and when the verification time reaches the maximum time, even if there are key process parameters that have not been verified, the verification is terminated or suspended. At this time, the key process parameters that have been verified are screened based on the above-mentioned third comprehensive quality evaluation value, that is, the key process parameters corresponding to the largest third comprehensive quality evaluation value are screened out, and they are formally included in the production process file and put into actual production. In addition, for other key process parameters that have not been verified, they can be re-verified at other time periods.

[0041] Specifically, each key process parameter corresponds to hot melt adhesive mesh information and substrate information. The hot melt adhesive mesh information includes the type of hot melt adhesive mesh (EVA, PA, PES, etc.), mesh structure (average area of ​​mesh pores, porosity, adhesive layer thickness, etc.), and the substrate information includes the type of substrate (textile fabric, plastic film, etc.), substrate thickness and substrate composite method (N-layer superposition composite, sandwich composite, etc.). The convolutional network is used to extract the first process feature from the key process parameters, hot melt adhesive mesh information, and substrate information. Then, the first process feature is similarly matched with the second process features of other key process parameters recorded in the database that have been verified (which may be verification of key process parameters other than this round) one by one, and the second number of other key process parameters with similarity higher than the similarity threshold is obtained. Next, the verification serial number of each key process parameter is determined. The smaller the above second number is, the smaller the verification serial number corresponding to the corresponding key process parameter is set, that is, the key process parameter is verified first, and vice versa, the key process parameter is verified later.

[0042] The second number represents the number of similar key process parameters that have been verified. When the number of similar key process parameters is larger, it indicates that the key process parameter is also likely to have a process performance similar to that of similar key process parameters. In this case, it is set not to be verified first. When the number of similar key process parameters is smaller, it indicates that the key process parameter is closer to being "new" and its process performance is unknown. In this case, it is set to be verified first, which is conducive to screening out "new" key process parameters with higher process performance.

[0043] In some embodiments, the quantity threshold is determined in the following manner: determining the scheduled formal production time of the mesh composite product corresponding to each of the key process parameters to be verified, and determining the quantity threshold based on the time difference between the current time and the formal production time; wherein the quantity threshold is positively correlated with the time difference.

[0044] In an embodiment of the present invention, the above-mentioned key process parameters are formulated by process designers for a mesh composite product to be produced. Since the process efficiency verification of the above-mentioned key process parameters requires a lot of time, it is necessary to reasonably arrange which key process parameters to be verified first to ensure that when the formal production time arrives, the key process parameters that meet the process efficiency standards can be verified to ensure the smooth progress of formal production.

[0045] In view of the above technical problems, the present invention sets the above quantity threshold value to be determined based on the time difference between the current time and the formal production time. That is, when the time difference is larger, it indicates that the remaining time is more sufficient, and the above quantity threshold value should be set larger at this time, and a larger number of key process parameters are screened in one stage (i.e., within the time between the current time and the formal production time); when the time difference is smaller, it indicates that the remaining time is more tense, and the above quantity threshold value should be set smaller at this time, and a smaller number of key process parameters are screened in one stage (i.e., within the time between the current time and the formal production time) in advance.

[0046] In some embodiments, the trial production quantity corresponding to the key process parameter is determined based on the comparison result, including: if the second comprehensive quality assessment value is higher than the first comprehensive quality assessment value corresponding to the lamination roller, and the degree of increase reaches a preset degree, then the trial production quantity is set to a first value; if the second comprehensive quality assessment value is not higher than the first comprehensive quality assessment value corresponding to the lamination roller, then the trial production quantity is set to a second value; wherein, the first value is lower than the second value.

[0047] In an embodiment of the present invention, when the second comprehensive quality evaluation value obtained by the simulation is higher than the first comprehensive quality evaluation value corresponding to the lamination roller and the degree of the higher degree reaches a preset degree, it means that the process efficiency of the key process parameter is higher from the perspective of theoretical simulation, and a smaller number of trial productions are set at this time, so that the cost of trial production can be appropriately reduced. When the second comprehensive quality evaluation value is not higher than the first comprehensive quality evaluation value corresponding to the lamination roller, it means that the process efficiency of the key process parameter may be insufficient from the perspective of theoretical simulation, and a larger number of trial productions should be set at this time, so that a more realistic process efficiency of the key process parameter can be evaluated based on the evaluation of the quality of more trial production products.

[0048] In some embodiments, the preset degree is determined by calculating a third quantity of the simulation results used to evaluate the second comprehensive quality evaluation value, and matching the third quantity to obtain the preset degree; wherein the preset degree is negatively correlated with the third quantity.

[0049] In an embodiment of the present invention, the aforementioned second comprehensive quality evaluation value is obtained based on a comprehensive evaluation of multiple simulation results of the simulation software. When the number of simulation results used for comprehensive evaluation is greater, the confidence of the obtained second comprehensive quality evaluation value is higher, and correspondingly, the above-mentioned preset degree is set lower. On the contrary, when the number of simulation results used for comprehensive evaluation is smaller, the confidence of the obtained second comprehensive quality evaluation value is lower, and correspondingly, the above-mentioned preset degree is set higher. With such a setting, it is possible to avoid erroneous trial production of a small number of units.

[0050] Among them, a comparison table between the preset degree and the third quantity can be established in advance according to statistical conditions, and the preset degree can be obtained by looking up the table.

[0051] like Figure 2 As shown, the present invention also discloses a process optimization system for mesh composite products, the system comprising at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the computer program executing the following steps when being executed by the processor: receiving key process parameters to be verified, the key process parameters comprising the lamination temperature and lamination force of the lamination roller; using simulation software to simulate the key process parameters based on a dynamic model of the lamination roller to obtain a number of simulation results, and evaluating the second comprehensive quality evaluation value of each mesh composite product according to each of the simulation results; and converting the second comprehensive quality evaluation value into a value. The first comprehensive quality evaluation value corresponding to the lamination roller is compared with the first comprehensive quality evaluation value corresponding to the lamination roller, and the trial production quantity corresponding to the key process parameters is determined according to the comparison result; wherein the first comprehensive quality evaluation value corresponds to the standard process parameters currently being used by the lamination roller; the key process parameters are introduced into the control system of the lamination roller, so that the control system trial-produces samples of the mesh composite product corresponding to the trial production quantity according to the key process parameters; each of the samples is evaluated separately according to preset rules to obtain a third comprehensive quality evaluation value, if the third comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value, the key process parameters are adopted, otherwise the key process parameters are not adopted.

[0052] In some embodiments, if the first quantity of the key process parameters is higher than the quantity threshold; then before using the simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, it also includes: obtaining the key process parameters and their corresponding hot melt adhesive web information and substrate information, and using a convolutional network to extract the first process feature from the key process parameters, the hot melt adhesive web information, and the substrate information; similarly matching the first process feature with the second process features of other verified key process parameters recorded in the database one by one to obtain the second quantity corresponding to each of the key process parameters; wherein the second quantity refers to the number of other key process parameters whose similarity to the key process parameter is higher than the similarity threshold; determining the verification serial number of each of the key process parameters in descending order according to the second quantity; wherein the smaller the second number, the smaller the verification serial number corresponding to the key process parameter, and vice versa.

[0053] In some embodiments, the quantity threshold is determined in the following manner: determining the scheduled formal production time of the mesh composite product corresponding to each of the key process parameters to be verified, and determining the quantity threshold based on the time difference between the current time and the formal production time; wherein the quantity threshold is positively correlated with the time difference.

[0054] In some embodiments, the trial production quantity corresponding to the key process parameter is determined based on the comparison result, including: if the second comprehensive quality assessment value is higher than the first comprehensive quality assessment value corresponding to the lamination roller, and the degree of increase reaches a preset degree, then the trial production quantity is set to a first value; if the second comprehensive quality assessment value is not higher than the first comprehensive quality assessment value corresponding to the lamination roller, then the trial production quantity is set to a second value; wherein, the first value is lower than the second value.

[0055] In some embodiments, the preset degree is determined by calculating a third quantity of the simulation results used to evaluate the second comprehensive quality evaluation value, and matching the third quantity to obtain the preset degree; wherein the preset degree is negatively correlated with the third quantity.

[0056] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method described in any of the preceding items when executed.

[0057] An embodiment of the present invention further discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the method described in any of the preceding items is implemented.

[0058] The embodiment of the present invention further discloses a computer program product. The computer program product is prepackaged with computer program code. When the computer program code is executed, the method described in any of the above items is implemented.

[0059] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0060] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0061] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0062] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0063] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0064] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0065] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0066] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A process optimization method for omentum composite products, characterized in that: The method comprises the following steps: receiving key process parameters to be verified, wherein the key process parameters include the lamination temperature and lamination force of the lamination roller; using simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, obtaining a plurality of simulation results, and evaluating and obtaining a second comprehensive quality evaluation value of each mesh composite product according to each of the simulation results; The second comprehensive quality assessment value is compared with the first comprehensive quality assessment value corresponding to the lamination roller, and the trial production quantity corresponding to the key process parameters is determined based on the comparison result; wherein the first comprehensive quality assessment value corresponds to the standard process parameters currently being used by the lamination roller; the key process parameters are imported into the control system of the lamination roller so that the control system trial-produces samples of the mesh composite product corresponding to the trial production quantity according to the key process parameters; each of the samples is evaluated separately according to preset rules to obtain a third comprehensive quality assessment value, and if the third comprehensive quality assessment value is higher than the first comprehensive quality assessment value, the key process parameters are adopted, otherwise the key process parameters are not adopted.

2. The process optimization method for a mesh composite product according to claim 1, characterized in that: If the first quantity of the key process parameters is higher than the quantity threshold; then before using the simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, it also includes: obtaining the key process parameters and their corresponding hot melt adhesive web information and substrate information, and using a convolutional network to extract the first process feature from the key process parameters, the hot melt adhesive web information, and the substrate information; performing similarity matching on the first process feature and the second process features of other verified key process parameters recorded in the database one by one to obtain the second quantity corresponding to each of the key process parameters; wherein the second quantity refers to the number of other key process parameters whose similarity to the key process parameters is higher than the similarity threshold; determining the verification serial number of each of the key process parameters in descending order according to the second quantity; wherein the smaller the second number, the smaller the verification serial number corresponding to the key process parameter, and vice versa.

3. The process optimization method for a mesh composite product according to claim 2, characterized in that: The quantity threshold is determined in the following manner: determining the scheduled formal production time of the mesh composite product corresponding to each of the key process parameters to be verified, and determining the quantity threshold based on the time difference between the current time and the formal production time; wherein the quantity threshold is positively correlated with the time difference.

4. The process optimization method for a mesh composite product according to claim 1, characterized in that: The method of determining the trial production quantity corresponding to the key process parameters based on the comparison result includes: if the second comprehensive quality evaluation value is higher than the first comprehensive quality evaluation value corresponding to the lamination roller, and the degree of increase reaches a preset degree, then the trial production quantity is set to a first value; if the second comprehensive quality evaluation value is not higher than the first comprehensive quality evaluation value corresponding to the lamination roller, then the trial production quantity is set to a second value; wherein the first value is lower than the second value.

5. The process optimization method for a mesh composite product according to claim 4, characterized in that: The preset degree is determined by calculating a third quantity of the simulation results used to evaluate the second comprehensive quality evaluation value, and matching the third quantity to obtain the preset degree; wherein the preset degree is negatively correlated with the third quantity.

6. A process optimization system for omentum composite products, characterized in that: The system includes at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the computer program is executed by the processor, the following steps are performed: receiving key process parameters to be verified, wherein the key process parameters include the lamination temperature and lamination force of the lamination roller; using simulation software to simulate the key process parameters based on the dynamic model of the lamination roller to obtain a plurality of simulation results, and evaluating and deriving a second comprehensive quality evaluation value of each mesh composite product according to each of the simulation results; The second comprehensive quality assessment value is compared with the first comprehensive quality assessment value corresponding to the lamination roller, and the trial production quantity corresponding to the key process parameters is determined based on the comparison result; wherein the first comprehensive quality assessment value corresponds to the standard process parameters currently being used by the lamination roller; the key process parameters are imported into the control system of the lamination roller so that the control system trial-produces samples of the mesh composite product corresponding to the trial production quantity according to the key process parameters; each of the samples is evaluated separately according to preset rules to obtain a third comprehensive quality assessment value, and if the third comprehensive quality assessment value is higher than the first comprehensive quality assessment value, the key process parameters are adopted, otherwise the key process parameters are not adopted.

7. A process optimization system for omentum composite products according to claim 6, characterized in that: If the first quantity of the key process parameters is higher than the quantity threshold; then before using the simulation software to simulate the key process parameters based on the dynamic model of the lamination roller, it also includes: obtaining the key process parameters and their corresponding hot melt adhesive web information and substrate information, and using a convolutional network to extract the first process feature from the key process parameters, the hot melt adhesive web information, and the substrate information; performing similarity matching on the first process feature and the second process features of other verified key process parameters recorded in the database one by one to obtain the second quantity corresponding to each of the key process parameters; wherein the second quantity refers to the number of other key process parameters whose similarity to the key process parameters is higher than the similarity threshold; determining the verification serial number of each of the key process parameters in descending order according to the second quantity; wherein the smaller the second number, the smaller the verification serial number corresponding to the key process parameter, and vice versa.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method according to any one of claims 1 to 5 when executed.

9. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 5 is implemented.

10. A computer program product, characterized in that: The computer program product is prepackaged with computer program codes, and when the computer program codes are executed, the method according to any one of claims 1 to 5 is implemented.

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