Stamping process parameter processing methods, devices, electronic equipment and storage media

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for processing stamping process parameters, relating to the field of computer technology, particularly artificial intelligence, industrial big data, and deep learning. The specific implementation involves: acquiring the target dimension information and the first raw material information of the stamped part; and generating optimized stamping process parameters for the stamped part using a genetic algorithm based on the target dimension information, the first raw material information, and a preset simulation model. The simulation model has learned to predict the dimensions of the stamped part based on the process parameters and raw material information. This solution can optimize process parameters during the stamping process, reducing the time and labor costs of on-site debugging in the stamping production line.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to the fields of artificial intelligence, industrial big data and deep learning, and particularly to a method, apparatus, electronic device and storage medium for processing stamping process parameters. Background Technology

[0002] In the steel industry, stamping is a downstream process closely related to specific business operations and is a type of metal processing method. For example, in the automotive industry, stamping mainly involves plasticizing or deforming upstream metals and applying pressure to the raw material metals using molds and stamping equipment, causing the raw material metals to deform and separate, thereby obtaining stamped parts with specific shapes, sizes, and properties.

[0003] In the stamping industry, process parameters are core technical indicators of the factory and are closely related to the quality of stamped parts. However, there are numerous process parameters in the stamping process, and different process parameters affect each other, resulting in long on-site debugging times and room for improvement in product quality. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for processing stamping process parameters.

[0005] According to a first aspect of this disclosure, a method for processing stamping process parameters is provided, the method comprising:

[0006] Obtain the target dimension information and the first raw material information of the stamped part;

[0007] Based on the target size information, the first raw material information, and the preset simulation model, the stamping process optimization parameters of the stamped parts are generated using a genetic algorithm;

[0008] The simulation model has learned to predict the dimensions of stamped parts based on process parameters and raw material information.

[0009] According to a second aspect of this disclosure, a stamping process parameter processing apparatus is provided, the apparatus comprising:

[0010] The first acquisition module is used to acquire the target size information and the first raw material information of the stamped part;

[0011] The generation module is used to generate stamping process optimization parameters for the stamped parts using a genetic algorithm based on the target size information, the first raw material information, and the preset simulation model.

[0012] The simulation model has learned to predict the dimensions of stamped parts based on process parameters and raw material information.

[0013] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0017] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0018] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0021] Figure 1 This is a schematic diagram based on the first embodiment of the present disclosure;

[0022] Figure 2 This is a schematic diagram according to the second embodiment of the present disclosure;

[0023] Figure 3 This is a schematic diagram according to the third embodiment of the present disclosure;

[0024] Figure 4 This is a schematic diagram according to the fourth embodiment of the present disclosure;

[0025] Figure 5 This is a schematic diagram according to the fifth embodiment of the present disclosure;

[0026] Figure 6 This is a schematic diagram according to the sixth embodiment of the present disclosure;

[0027] Figure 7 This is a block diagram of an electronic device used to implement the stamping process parameter processing method of the embodiments of this disclosure. Detailed Implementation

[0028] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0029] The acquisition, storage, and application of user personal information in this disclosed technical solution comply with relevant laws and regulations and do not violate public order and good morals. The user personal information is acquired, stored, and used with the user's consent.

[0030] It should be noted that in the steel industry, stamping is a downstream process closely related to specific business operations and is a type of metal processing method. For example, in the automotive industry, stamping mainly involves plasticizing or deforming upstream metals and applying pressure to the raw material metals using molds and stamping equipment, causing the raw material metals to deform and separate, thereby obtaining stamped parts with specific shapes, dimensions, and properties.

[0031] In the stamping industry, process parameters are core technical indicators of the factory and are closely related to the quality of stamped parts. However, there are numerous process parameters in the stamping process, and different process parameters affect each other, resulting in long on-site debugging times and room for improvement in product quality.

[0032] To address the aforementioned issues, this disclosure provides a method, apparatus, electronic device, and storage medium for processing stamping process parameters. The method predicts the dimensions of stamped parts using a simulation model and optimizes the parameters using a genetic algorithm, thereby improving the dimensional quality of stamped parts.

[0033] Figure 1 This is a flowchart illustrating a stamping process parameter processing method provided in an embodiment of this disclosure. Figure 1 As shown, the method includes the following steps:

[0034] Step 101: Obtain the target dimension information and the first raw material information of the stamped part.

[0035] In this embodiment of the disclosure, the target dimension information of the stamped part refers to the required dimension information of the stamped part, which can be understood as the dimension information of the stamped part expected to be obtained through the stamping process in actual industrial operations. For example, it may include: the end dimension of the U-shaped bend of the slide rail, the root dimension of the U-shaped bend of the slide rail, and the ball track profile. The first raw material information of the stamped part refers to the raw material composition information of the upstream metal corresponding to the process of producing the stamped part. For example, it may include: carbon content, silicon content, manganese content, phosphorus content, sulfur content, aluminum content, chromium content, copper content, nitrogen content, nickel content, titanium content, niobium content, molybdenum content, boron content, vanadium content, yield strength, tensile strength, etc.

[0036] As an example, the method for obtaining the target size information and the first raw material information of the stamped part can be as follows: using a terminal device that can input information, the user inputs the target size information and the first raw material information of the stamped part through an interactive interface and submits it, that is, the target size information and the first raw material information of the stamped part are obtained by receiving the data information submitted by the user.

[0037] As another example, the target size information and first raw material information can also be obtained by requesting a database used to store target size information and raw material information, based on the identification information of the stamped part.

[0038] As another example, the first raw material information of the stamped part can also be obtained from the training samples used in the training process of the preset simulation model. The training samples of the preset simulation model can be based on the process parameters, raw material information and the size information of the stamped part obtained through the stamping process in the actual stamping process. Therefore, the raw material information of the stamped part in the training sample can be obtained according to the identifier of the stamped part.

[0039] Step 102: Based on the target size information, the first raw material information, and the preset simulation model, the genetic algorithm is used to generate stamping process optimization parameters for the stamped parts; wherein, the simulation model has learned to predict the size of the stamped parts based on the process parameters and raw material information.

[0040] It should be noted that genetic algorithms are computational models that simulate the biological evolutionary process based on Darwin's theory of evolution, specifically natural selection and genetic mechanisms. They are methods for searching for optimal solutions by simulating natural evolution. This algorithm uses mathematical methods and computer simulations to transform the problem-solving process into processes similar to the crossover and mutation of chromosomes and genes in biological evolution. It draws on mechanisms of genetic evolution in biology, such as mutation, crossover, natural selection, and survival of the fittest, to optimize a set of iterative candidate solutions to the objective.

[0041] In this embodiment of the disclosure, the method of generating stamping process optimization parameters for stamped parts using a genetic algorithm can be as follows: a population is obtained through a genetic algorithm, and each individual in the population represents a process parameter; based on the first raw material information and the process parameter represented by each individual, a simulation model is used to predict the corresponding stamped part size information; based on the stamped part size information and target size information corresponding to each individual, the suitability of the individual is calculated, and individuals in the population are screened based on the suitability to continuously optimize the stamping process parameters and generate stamping process optimization parameters for stamped parts.

[0042] It should be noted that, in some embodiments of this disclosure, the stamping process parameters and the stamping process optimization parameters can both include multi-dimensional parameters, and the dimensions of the process parameters corresponding to the simulation model training, the process parameters represented by the individuals in the population obtained using the genetic algorithm, and the dimensions of the stamping process optimization parameters generated using the genetic algorithm are the same. The stamping process parameters may include parameters such as die closing height and upper die main teeth.

[0043] The simulation model can be trained using production line data from the actual stamping process, and the simulation model can be a linear model, a tree model, a deep learning model, etc. It can be a model in the prior art or a model constructed by a person skilled in the art based on the actual scenario. This disclosure does not limit it.

[0044] As an example, the training process of the simulation model can be as follows: Training samples for the simulation model are obtained based on data from the actual stamping process. Each set of data in the training samples includes process parameters, raw material information, and the size information of the stamped part obtained by the metal from the actual stamping process using the specified process parameters. The process parameters and raw material information from the training samples are input into the simulation model to obtain the predicted size information of the stamped part. The loss value is calculated based on the predicted size information of the stamped part and the corresponding stamped part information in the training samples. The simulation model is then trained based on the loss value.

[0045] As another example, the training process of the simulation model may also include: obtaining training samples and test samples of the simulation model based on data from the actual stamping process, wherein the data composition structure of the training samples and test samples is consistent; inputting the process parameters and raw material information in the test samples into the trained simulation model to obtain the predicted stamped part size information; comparing the predicted stamped part size information with the corresponding stamped part information in the test samples to determine whether the predicted stamped part size information meets the preset deviation range; calculating the accuracy of the trained simulation model in predicting part size, if the accuracy is greater than the preset threshold, it indicates that the simulation model has completed training, if the accuracy is less than or equal to the preset threshold, it indicates that the simulation model has not completed training and needs to continue to adjust the model parameters for further training.

[0046] According to the stamping process parameter processing method of this disclosure, based on the target size information of the stamped part, the raw material information, and the learned simulation model that can predict the size of the stamped part based on the process parameters and raw material information, the genetic algorithm is used to generate the stamping process optimization parameters of the stamped part. This not only optimizes the process parameters during the stamping process and improves the dimensional quality of the stamped parts, but also significantly reduces the time and labor costs of on-site debugging of the stamping process production line, and plays a guiding role in the industrial production process.

[0047] Based on the above embodiments, the following section will introduce the optimization parameters for generating stamping process parameters for stamped parts using a genetic algorithm.

[0048] Figure 2 This is a flowchart illustrating an embodiment of the present disclosure of generating stamping process optimization parameters for stamped parts using a genetic algorithm. For example... Figure 2 As shown, based on the above embodiments, the process includes the following steps:

[0049] Step 201: Determine the process parameters represented by each individual in the initial population within a preset parameter range in a random manner.

[0050] In this embodiment of the disclosure, to ensure that the process parameters represented by each individual in the initial population do not deviate from the actual application scenario, the process parameters represented by each individual can be determined within a preset parameter range. This preset parameter range can be a range determined based on expert experience or a range obtained based on historical stamping data. Furthermore, each individual in the population has its own set of chromosomes, and since chromosomes are composed of genes, the phenotype corresponding to each individual's own chromosome represents the process parameters it embodies.

[0051] Step 202: Calculate the applicability of each individual based on the process parameters, first raw material information, target size information, and simulation model represented by each individual.

[0052] In other words, each individual needs to be evaluated based on the process parameters, primary raw material information, target size information, and simulation model to determine whether the process parameters represented by each individual are suitable for the stamping process of the stamped part.

[0053] As an example, the implementation process may include: inputting the process parameters and first raw material information represented by each individual into the simulation model to obtain the predicted size information of each individual; and calculating the applicability of each individual based on the target size information and the predicted size information of each individual.

[0054] As another example, the first raw material information may include multiple sets of raw material information. That is, there are multiple predicted size information based on the process parameters represented by each individual. Therefore, the applicability of each individual can be calculated based on the pass rate of the predicted size of each individual.

[0055] Step 203: Based on the suitability of each individual, the individuals in the population are screened to obtain the target individuals.

[0056] In other words, we can determine whether the applicability of each individual meets the preset conditions based on the applicability of each individual, and then select the individuals that meet the preset conditions as the target individuals.

[0057] As an example, if the pre-set condition for individual selection in each round of population is the two individuals with the highest suitability, then the individuals in the population can be sorted from high to low suitability, and the top two individuals in the sorting results can be selected as target individuals.

[0058] As another example, to improve the diversity of individuals in a population, a roulette wheel selection method can be used to determine target individuals. For instance, the area occupied by each individual in the roulette wheel can be set according to its suitability; individuals with higher suitability occupy a larger area. The roulette wheel with the area set is then rotated. When the roulette wheel stops, the individual represented by the area pointed to by the pointer is the first target individual. This rotation is repeated, and the individual represented by the area pointed to by the pointer becomes the second target individual.

[0059] Step 204: Use the target individuals for crossbreeding and mutation to obtain the next round of population.

[0060] In this embodiment, crossbreeding refers to the method of exchanging chromosomes of a target individual to generate a new individual. Crossbreeding can typically include single-point crossbreeding or multi-point crossbreeding. Single-point crossbreeding involves randomly selecting a crossbreeding point based on the target individual's chromosomes and swapping the chromosome portions before and after the crossbreeding point to generate a new individual. Multi-point crossbreeding involves selecting two crossbreeding points based on the target individual's chromosomes and swapping the chromosome portions between the crossbreeding points to generate a new individual.

[0061] In biology, during the growth of offspring, the genes within their bodies undergo changes that make them different from those of their parents; this process is called mutation. In this embodiment of the disclosure, an individual can be randomly selected from those obtained through crossbreeding, and the genes on the chromosome of the selected individual can be randomly altered with a preset probability to obtain a new individual.

[0062] In this embodiment of the disclosure, the new individuals generated during the crossbreeding process and the new individuals obtained through the mutation process are combined to form the next round of population.

[0063] Step 205: Determine whether the termination condition of the genetic algorithm is met.

[0064] In this embodiment, the termination condition of the genetic algorithm is typically a preset number of rounds in which the population is generated. If the termination condition is met, step 206 is executed; otherwise, step 202 is executed. For example, if the termination condition of the genetic algorithm is to generate a population for 30 rounds, then after each round of population generation, it is necessary to determine whether the current population has reached 30 rounds. If the generated population is in the 30th round, it indicates that the genetic algorithm has reached the termination condition and will no longer generate new populations. Step 206 is then executed to obtain the stamping process optimization parameters for the stamped parts. If the termination condition of the genetic algorithm is not met, step 202 is then executed.

[0065] Step 206: The process parameters represented by individuals in the population obtained when the termination condition of the genetic algorithm is met are determined as the stamping process optimization parameters for the stamped parts.

[0066] In other words, if the termination condition of the genetic algorithm is met, the process parameters represented by the individuals in the newly obtained population can be determined as the stamping process optimization parameters for the stamped parts. If the termination condition of the genetic algorithm is to generate 30 rounds of population, then after generating the 30th round of population, the process parameters represented by the individuals in the 30th round of population will be determined as the stamping process optimization parameters for the stamped parts.

[0067] In some embodiments of this disclosure, the number of individuals in the population obtained when the termination condition of the genetic algorithm is met can be multiple, and the corresponding process parameters can also be multiple. Therefore, they can be screened to make the obtained stamping process optimization parameters more suitable for the process of the stamped part. As an example, a preset applicability threshold can be used to select the process parameters represented by individuals with an applicability greater than the preset applicability threshold as the stamping process parameters of the stamped part. As another example, the number of stamping process optimization parameters can also be preset. The individuals in the population obtained when the termination condition of the genetic algorithm is met can be arranged from high to low applicability, and a preset number of individuals can be selected according to the arrangement. The process parameters represented by the selected individuals can be used as the stamping process optimization parameters of the stamped part.

[0068] According to the stamping process parameter processing method of this disclosure, in the process of generating stamping process optimization parameters for stamped parts using a genetic algorithm, the applicability of each individual is calculated based on the process parameters represented by each individual, the first raw material information, the target size information, and the simulation model. Based on the applicability of each individual, individuals in the population can be screened to obtain the next round of the population. In other words, the applicability of each individual can be calculated based on the simulation model to achieve the purpose of optimizing stamping process parameters, generating stamping process optimization parameters for stamped parts, thereby improving the dimensional quality of stamped parts and reducing the time and labor costs of on-site debugging of the stamping process production line.

[0069] Since the initial raw material information of stamped parts can contain multiple sets of raw material data in actual stamping scenarios, the following section will introduce the implementation method for calculating the applicability of each individual part in this case.

[0070] Figure 3 This is a flowchart illustrating the calculation of individual suitability in an embodiment of this disclosure. Figure 3 As shown, based on the above embodiments, the implementation process may include the following steps:

[0071] Step 301: Input the process parameters and multiple sets of raw material data represented by each individual into the simulation model to obtain multiple predicted size information for each individual.

[0072] In actual stamping processes, the composition of upstream metal raw materials corresponding to the production of stamped parts may vary due to different batches or manufacturers. Therefore, the content of each raw material may fall within a range, meaning the initial raw material information for stamped parts may contain multiple sets of raw material data. To determine the matching between the process parameters represented by each individual component and each set of raw material information, in this embodiment, the process parameters represented by each individual component and multiple sets of raw material data can be input into a simulation model to obtain multiple predicted dimensional information corresponding to the process parameters represented by each individual component.

[0073] Step 302: Determine the acceptable size range based on the target size information.

[0074] It is understandable that, depending on the actual application scenario, the dimensional information of stamped parts can be within a relatively small range. This range can be the dimensional deviation range obtained through multiple rounds of testing that does not affect the performance of the stamped parts.

[0075] In this embodiment of the disclosure, the acceptable size range of the stamped part can be determined based on the target size information and the allowable deviation range of the stamped part. The acceptable size range means that if the size information of the stamped part falls within this range, then the size of the stamped part is considered acceptable. For example, if the allowable size deviation range of the stamped part, determined based on multiple tests, is ±0.02, then the size range corresponding to the target size information ±0.02 is considered an acceptable size range.

[0076] Step 303: Compare each predicted size information with the qualified size range to calculate the qualified rate of each individual predicted size.

[0077] In other words, each predicted size is compared with the acceptable size range to determine whether each predicted size is acceptable, and the acceptable rate of each individual's predicted size is calculated based on the acceptable status of multiple predicted sizes for each individual.

[0078] As an example, if the first raw material information includes 30 sets of raw material data, then in step 301 each individual can obtain 30 predicted size information; each predicted size information is compared with the qualified size range. If 21 of the predicted size information of an individual is within the qualified size range, while the other predicted size information of the individual is not within the qualified size range, then the qualified rate of the predicted size of the individual can be calculated as 21 / 30 × 100% = 70%.

[0079] Step 304: Determine the pass rate of the predicted size for each individual as the applicability of the corresponding individual.

[0080] According to the stamping process parameter processing method of this disclosure, when the first raw material information includes multiple sets of raw material data, multiple sets of raw material data and process parameters represented by individuals can be input into the simulation model, and the obtained predicted size information can be compared with the qualified size range to obtain the qualified rate of the predicted size of each individual, thereby determining the applicability of the corresponding individual. In other words, the matching situation between each set of raw material data and the process parameters represented by each individual is considered in the applicability evaluation process, so that the stamping process optimization parameters generated by the genetic algorithm can meet the actual stamping process scenario. This can not only further improve the accuracy of the stamping process optimization parameters and ensure the dimensional quality of the stamped parts, but also improve the applicability of this solution.

[0081] To further confirm the applicability of the stamping process optimization parameters to actual stamping process scenarios, this disclosure proposes yet another embodiment.

[0082] Figure 4 A flowchart illustrating another method for processing stamping process parameters provided in this disclosure. (See attached flowchart.) Figure 4As shown, the method includes:

[0083] Step 401: Obtain the target dimension information and the first raw material information of the stamped part.

[0084] Step 402: Based on the target size information, the first raw material information, and the preset simulation model, the genetic algorithm is used to generate the stamping process optimization parameters for the stamped parts; wherein, the simulation model has learned to predict the size of the stamped parts based on the process parameters and raw material information.

[0085] Step 403: Obtain the second raw material information for the stamped part.

[0086] In this embodiment of the disclosure, the second raw material information of the stamped part also refers to the raw material composition information of the upstream metal corresponding to the process of producing the stamped part. It is consistent with the data composition of the first raw material information, but the content of each raw material in the second raw material information may differ from that in the first raw material information due to different product batches or other reasons.

[0087] As an example, the second raw material information for stamped parts can be obtained as follows: The user uses an information input terminal device to input and submit the second raw material information for the stamped parts through an interactive interface; that is, the second raw material information is obtained by receiving the data submitted by the user. Alternatively, the user can submit the raw material information simultaneously with the input. After receiving the submitted raw material information, the system then divides the raw material information into first raw material information and second raw material information according to preset rules.

[0088] As another example, the corresponding second raw material information can also be obtained by requesting a database used to store raw material information, based on the identification information of the stamped part.

[0089] As another example, the second raw material information for stamped parts can also be obtained from test samples used in the testing process of a pre-defined simulation model. For instance, production line data from the actual stamping process can be obtained and segmented to obtain training samples and test samples. Both training and test samples include process parameters, raw material information, and the dimensional information of the stamped parts obtained through the stamping process. In this way, based on the stamped part identifier, the raw material information of the stamped part in the test sample can be used as the second raw material information, and the raw material information of the stamped part in the training sample can be used as the first raw material information.

[0090] Step 404: Evaluate the stamping process optimization parameters based on the second raw material information, target size information, and simulation model.

[0091] It is understandable that, in order to ensure the applicability of the stamping process optimization parameters, the obtained stamping process optimization parameters can be evaluated using the second raw material information. In other words, if the raw material information of the stamped part is the second raw material information, and the process parameters of the stamping process are configured as the stamping process optimization parameters, the applicability of the stamping process optimization parameters to the case where the raw material information of the stamped part is the second raw material information can be determined by comparing the difference between the dimensional information of the stamped part obtained after the stamping process and the target dimensional information.

[0092] As an example, the implementation process may include: inputting the obtained stamping process optimization parameters and the second raw material information into the simulation model to obtain the predicted size information corresponding to each stamping process optimization parameter; comparing the predicted size information corresponding to each stamping process optimization parameter with the target size information; if the predicted size information corresponding to a certain stamping process optimization parameter meets the preset range, then the stamping process optimization parameter can be produced and applied to the actual stamping production line; if the predicted size information corresponding to a certain stamping process optimization parameter does not meet the preset range, then the stamping process optimization parameter can continue to be optimized using a genetic algorithm.

[0093] As another example, due to differences in batches or manufacturers, the content of each raw material may vary. Therefore, the values ​​of each indicator in the raw material composition information may be within a range. In other words, the second raw material information for stamped parts may also contain multiple sets of raw material data. For cases where the second raw material information includes multiple sets of raw material data, the evaluation of stamping process optimization parameters can include: inputting each stamping process optimization parameter and multiple sets of raw material data into a simulation model to obtain multiple predicted dimension information corresponding to each stamping process optimization parameter; determining the acceptable dimension range based on the target dimension information; comparing each predicted dimension information with the acceptable dimension range to calculate the predicted dimension pass rate corresponding to each stamping process optimization parameter; and determining the evaluation result of each stamping process optimization parameter based on the predicted dimension pass rate corresponding to each stamping process optimization parameter. If the pass rate is greater than the preset threshold, the evaluation result of the stamping process optimization parameter is considered passed, indicating that the stamping process optimization parameter can also be used for stamped parts based on the second raw material information. In other words, the stamping process optimization parameter is applicable to the actual stamping scenario of the corresponding stamped parts, so the stamping process optimization parameter can be generated and applied to the actual stamping process. If the predicted dimension pass rate corresponding to a certain stamping process optimization parameter is less than or equal to the preset threshold, the evaluation result of the stamping process optimization parameter is considered failed, and the applicability of the stamping process optimization parameter needs to be improved. It is necessary to continue to optimize the stamping process optimization parameter using a genetic algorithm.

[0094] According to the stamping process parameter processing method of this disclosure, the stamping process optimization parameters generated by the genetic algorithm are evaluated using the second raw material information, target size information, and simulation model. This can further determine the applicability of the stamping process optimization parameters, thereby ensuring the applicability of the produced stamping process optimization parameters in the actual stamping process. This further reduces the time and labor costs of on-site debugging of the stamping process production line, and thus improves the dimensional quality of the stamped parts and the efficiency of the stamping process.

[0095] To achieve the above embodiments, this disclosure provides a stamping process parameter processing device.

[0096] Figure 5 This is a structural block diagram of a stamping process parameter processing device provided in an embodiment of this disclosure. Figure 5 As shown, the device includes:

[0097] The first acquisition module 510 is used to acquire the target size information and the first raw material information of the stamped part;

[0098] The generation module 520 is used to generate stamping process optimization parameters for stamped parts using a genetic algorithm based on the target size information, the first raw material information and the preset simulation model.

[0099] The simulation model has learned to predict the dimensions of stamped parts based on process parameters and raw material information.

[0100] In some embodiments of this disclosure, the generation module 520 includes:

[0101] The first determining unit 521 is used to determine the process parameters represented by each individual in the initial population in a random manner within a preset parameter range.

[0102] The calculation unit 522 is used to calculate the applicability of each individual based on the process parameters, first raw material information, target size information and simulation model represented by each individual.

[0103] The screening unit 523 is used to screen individuals in the population based on the suitability of each individual to obtain the target individual;

[0104] Acquisition unit 524 is used to perform crossbreeding and mutation using the target individuals to obtain the next round of the population;

[0105] The computing unit 522 is also used to calculate the applicability of each individual based on the process parameters, first raw material information, target size information and simulation model represented by each individual in response to the failure to meet the termination condition of the genetic algorithm.

[0106] The second determining unit 525 is used to determine the process parameters represented by individuals in the population obtained when the termination condition of the genetic algorithm is met as the stamping process optimization parameters of the stamping parts in response to the termination condition of the genetic algorithm being met.

[0107] In some embodiments of this disclosure, the computing unit 522 is specifically used for:

[0108] The process parameters and first raw material information represented by each individual are input into the simulation model to obtain the predicted size information of each individual.

[0109] The suitability of each individual is calculated based on the target size information and the predicted size information for each individual.

[0110] In some embodiments of this disclosure, the first raw material information includes multiple sets of raw material data; the calculation unit 522 is specifically used for:

[0111] The process parameters and multiple sets of raw material data represented by each individual are input into the simulation model to obtain multiple predicted size information for each individual.

[0112] Based on the target size information, determine the acceptable size range;

[0113] Each predicted size is compared with the acceptable size range to calculate the pass rate of each individual predicted size.

[0114] The pass rate of the predicted size for each individual is determined as the suitability of that individual.

[0115] According to the stamping process parameter processing apparatus of this disclosure, based on the target size information of the stamped part, the raw material information, and the learned simulation model that can predict the size of the stamped part based on the process parameters and the raw material information, the genetic algorithm is used to generate stamping process optimization parameters for the stamped part. This not only optimizes the process parameters during the stamping process and improves the dimensional quality of the stamped parts, but also significantly reduces the time and labor costs of on-site debugging of the stamping process production line, thus playing a guiding role in the industrial production process.

[0116] Figure 6 This is a structural block diagram of another stamping process parameter processing device provided in an embodiment of this disclosure. (See diagram below.) Figure 6 As shown, based on the above embodiments, the device may further include:

[0117] The second acquisition module 630 is used to acquire the second raw material information of the stamped part;

[0118] Evaluation module 640 is used to evaluate the stamping process optimization parameters based on the second raw material information, target size information, and simulation model.

[0119] in, Figure 6 Modules 610-620 and Figure 5 Modules 510-520 in the text have the same functional structure, which will not be described in detail here.

[0120] According to the stamping process parameter processing apparatus of this disclosure, the stamping process optimization parameters generated by the genetic algorithm are evaluated by using second raw material information, target size information and simulation model. This can further determine the applicability of the stamping process optimization parameters, thereby ensuring the applicability of the produced stamping process optimization parameters in the actual stamping process. This further reduces the time and labor costs of on-site debugging of the stamping process production line, and thus improves the dimensional quality of the stamped parts and the efficiency of the stamping process.

[0121] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0122] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0123] like Figure 7 As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 702 or a computer program loaded from storage unit 708 into random access memory (RAM) 703. RAM 703 may also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0124] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0125] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the stamping process parameter processing method. For example, in some embodiments, the stamping process parameter processing method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the stamping process parameter processing method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the stamping process parameter processing method by any other suitable means (e.g., by means of firmware).

[0126] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0128] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0129] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0130] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0131] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0132] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for processing stamping process parameters, comprising: Obtain the target dimension information of the stamped part and the first raw material information of the stamped part, wherein the first raw material information refers to the raw material composition information of the upstream metal corresponding to the process of producing the stamped part; Determine the process parameters represented by each individual in the initial population; The applicability of each individual is calculated based on the process parameters represented by each individual, the first raw material information, the target size information, and the simulation model. Based on the suitability of each individual, individuals in the population are screened to obtain target individuals; The target individuals are then used for crossbreeding and mutation to obtain the next round of the population; In response to the failure to meet the termination condition of the genetic algorithm, the process returns to the step of calculating the suitability of each individual based on the process parameters represented by each individual, the first raw material information, the target size information, and the simulation model. In response to the satisfaction of the termination condition of the genetic algorithm, the process parameters represented by individuals in the population obtained when the termination condition of the genetic algorithm is satisfied are determined as the stamping process optimization parameters of the stamped part. The simulation model has learned to predict the dimensions of stamped parts based on process parameters and raw material information. The first raw material information includes multiple sets of raw material data; the step of calculating the applicability of each individual based on the process parameters represented by each individual, the first raw material information, the target size information, and the simulation model includes: The process parameters represented by each individual and the multiple sets of raw material data are input into the simulation model to obtain multiple predicted size information for each individual. Based on the target size information, determine the acceptable size range; Each predicted size is compared with the qualified size range to calculate the qualified rate of each individual predicted size. The pass rate of the predicted size for each individual is determined as the suitability of the corresponding individual; The method further includes: Obtain the second raw material information of the stamped part; The stamping process optimization parameters are evaluated based on the second raw material information, the target size information, and the simulation model.

2. The method according to claim 1, wherein, The determination of the process parameters represented by each individual in the initial population includes: Within a preset parameter range, the process parameters represented by each individual in the initial population are determined in a random manner.

3. A stamping process parameter processing device, comprising: The first acquisition module is used to acquire the target size information and the first raw material information of the stamped part. The first raw material information refers to the raw material composition information of the upstream metal corresponding to the process of producing the stamped part. The first raw material information contains multiple sets of raw material data. The generation module is used to generate stamping process optimization parameters for the stamped parts using a genetic algorithm based on the target size information, the first raw material information, and the preset simulation model. The simulation model has learned to predict the dimensions of stamped parts based on process parameters and raw material information. The generation module includes: The first determining unit is used to determine the process parameters represented by each individual in the initial population; The calculation unit is used to calculate the applicability of each individual based on the process parameters represented by each individual, the first raw material information, the target size information, and the simulation model. A screening unit is used to screen individuals in the population based on the suitability of each individual to obtain target individuals; The acquisition unit is used to perform crossbreeding and mutation using the target individuals to obtain the next round of the population; The computing unit is further configured to, in response to the failure to meet the termination condition of the genetic algorithm, calculate the applicability of each individual based on the process parameters represented by each individual, the first raw material information, the target size information, and the simulation model. The second determining unit is used to determine the process parameters represented by individuals in the population obtained when the termination condition of the genetic algorithm is met as the stamping process optimization parameters of the stamped part in response to the meeting of the termination condition of the genetic algorithm. The first raw material information includes multiple sets of raw material data; the calculation unit is specifically used for: The process parameters represented by each individual and the multiple sets of raw material data are input into the simulation model to obtain multiple predicted size information for each individual. Based on the target size information, determine the acceptable size range; Each predicted size is compared with the qualified size range to calculate the qualified rate of each individual predicted size. The pass rate of the predicted size for each individual is determined as the suitability of the corresponding individual; The device further includes: The second acquisition module is used to acquire the second raw material information of the stamped part; The evaluation module is used to evaluate the stamping process optimization parameters based on the second raw material information, the target size information, and the simulation model.

4. The apparatus according to claim 3, wherein, The first determining unit is specifically used to determine the process parameters represented by each individual in the initial population in a random manner within a preset parameter range.

5. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-2.

6. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-2.

7. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-2.

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