An optimization method and system for stamping process of automotive parts

Abstract

The present invention provides a method and system for optimizing the stamping process of automotive parts, which relates to the technical field of data processing. By extracting curve parameters from a digital model, a set of model reduction parameters is obtained. The first reduction parameter group is input into the model simulation reduction module to obtain the first simulation reduction model, and with the reduction parameter optimization rule as a constraint, the reduction parameters of the parts are optimized to obtain the optimized result of the part parameters. The stamping positioning optimization result is determined by analyzing the raw material size parameters and the optimized result of the part parameters. It solves the technical problems in the prior art that the optimization of the stamping process of automotive parts depends on multiple rounds of repeated tests by technicians, resulting in time-consuming process optimization, and at the same time, there is a lack of adaptability between the actual performance of automotive parts produced based on the process optimization results and the production requirements. It achieves the technical effects of improving the efficiency and effectiveness of the stamping process optimization of automotive parts, and improving the adaptability between the actual performance of automotive parts and the automotive function requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for optimizing the stamping process of automotive parts. Background Art

[0002] There are several obvious defects in the current optimization of the stamping process for automotive parts. First, the optimization process of the stamping process for these automotive parts highly depends on technicians to conduct multiple rounds of repeated tests, making the whole process cumbersome and time-consuming. Second, although the optimized parts may meet the design requirements in some aspects, there are still certain deficiencies in the adaptability between the actual performance and production requirements, resulting in the production efficiency and quality not being able to fully meet the requirements. In addition, this traditional optimization process lacks systematicness and intelligence, and cannot quickly find the global optimal solution, causing waste of resources and time.

[0003] In summary, the prior art has the defects that the optimization of the stamping process for automotive parts depends on multiple rounds of repeated tests by technicians, resulting in a relatively long time-consuming process for process optimization, and at the same time, there is a deficiency in the adaptability between the actual performance of automotive parts produced based on the process optimization results and the production requirements. Summary of the Invention

[0004] This application provides a method and system for optimizing the stamping process of automotive parts, which are used to solve the technical problems existing in the prior art that the optimization of the stamping process for automotive parts depends on multiple rounds of repeated tests by technicians, resulting in a relatively long time-consuming process for process optimization, and at the same time, there is a deficiency in the adaptability between the actual performance of automotive parts produced based on the process optimization results and the production requirements.

[0005] In view of the above problems, this application provides a method and system for optimizing the stamping process of automotive parts.

[0006] In the first aspect of the present application, a method for optimizing the stamping process of automotive parts is provided. The method includes: constructing a target digital model, where the target digital model is constructed and generated according to the design information of the target parts; obtaining a model reduction parameter set, where the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators; obtaining a first reduction parameter group and inputting the first reduction parameter group into the model simulation reduction module to obtain a first simulation reduction model; predefining a reduction parameter optimization rule, and starting from the first simulation reduction model and constrained by the reduction parameter optimization rule, performing reduction parameter optimization of the target parts to obtain a component parameter optimization result; interactively obtaining the raw material size parameters of the stamping process raw material; obtaining a stamping positioning optimization result, where the stamping positioning optimization result is determined by superposition simulation analysis with the raw material size parameters and the component parameter optimization result as constraints; generating a component stamping optimization result, where the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result.

[0007] In the second aspect of the present application, a system for optimizing the stamping process of automotive parts is provided. The system includes: a digital model construction module for constructing a target digital model, where the target digital model is constructed and generated according to the design information of the target parts; a model parameter generation module for obtaining a model reduction parameter set, where the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators; a reduction model generation module for obtaining a first reduction parameter group and inputting the first reduction parameter group into the model simulation reduction module to obtain a first simulation reduction model; a component parameter optimization module for predefining a reduction parameter optimization rule, and starting from the first simulation reduction model and constrained by the reduction parameter optimization rule, performing reduction parameter optimization of the target parts to obtain a component parameter optimization result; a size parameter interaction module for interactively obtaining the raw material size parameters of the stamping process raw material; a superposition simulation analysis module for obtaining a stamping positioning optimization result, where the stamping positioning optimization result is determined by superposition simulation analysis with the raw material size parameters and the component parameter optimization result as constraints; a stamping parameter output module for generating a component stamping optimization result, where the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The method provided by the embodiment of the present application constructs a target digital model, wherein the target digital model is constructed and generated according to the design information of the target component; obtains a model reduction parameter set, wherein the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators; obtains a first reduction parameter group, and inputs the first reduction parameter group into the model simulation reduction module to obtain a first simulation reduction model; determines a reduction parameter optimization rule, and takes the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint to perform the reduction parameter optimization of the target component to obtain a component parameter optimization result; interactively obtains the raw material size parameters of the stamping process raw material; obtains a stamping positioning optimization result, wherein the stamping positioning optimization result is determined by superimposing and simulating with the raw material size parameters and the component parameter optimization result as constraints; generates a component stamping optimization result, wherein the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result. It achieves the technical effects of improving the optimization efficiency and effectiveness of the stamping process of automotive components, and improving the matching degree between the actual performance of automotive components and the automotive function requirements. Description of the Drawings

[0010] Figure 1 It is a schematic flow chart of a method for optimizing the stamping process of automotive components provided by the present application;

[0011] Figure 2 It is a schematic flow chart of obtaining a model reduction parameter set in a method for optimizing the stamping process of automotive components provided by the present application;

[0012] Figure 3 It is a schematic flow chart of obtaining a first simulation reduction model in a method for optimizing the stamping process of automotive components provided by the present application;

[0013] Figure 4 It is a schematic structural diagram of a system for optimizing the stamping process of automotive components provided by the present application.

[0014] Description of the reference numerals: Digital model construction module 1, model parameter generation module 2, reduction model generation module 3, component parameter optimization module 4, dimension parameter interaction module 5, superposition simulation analysis module 6, stamping parameter output module 7. Detailed Embodiments

[0015] The present application provides a method and system for optimizing the stamping process of automotive parts, which are used to solve the technical problems existing in the prior art that the optimization of the stamping process of automotive parts relies on multiple rounds of repeated tests by technicians, resulting in relatively long time-consuming for process optimization, and there is also insufficient adaptation between the actual performance of automotive parts produced based on the process optimization results and the production requirements. The technical effect of improving the efficiency and effectiveness of the stamping process optimization of automotive parts and improving the adaptation between the actual performance of automotive parts and the functional requirements of the vehicle is achieved.

[0016] In the technical solution of the present invention, the acquisition, storage, use, processing, etc. of data all comply with relevant regulations.

[0017] Next, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention are shown in the drawings rather than all of them.

[0018] Embodiment 1

[0019] As Figure 1 shown, the present application provides a method for optimizing the stamping process of automotive parts. The method is applied to a system for optimizing the stamping process of automotive parts. The system includes a model simulation reduction module and a reduced model testing module. The method includes:

[0020] A100: Construct a target digital model, where the target digital model is constructed and generated according to the design information of the target part.

[0021] Specifically, it should be understood that automotive parts are the individual independent components that make up an entire vehicle, and they are the basic building blocks of a vehicle. Different automotive parts perform different functions and tasks. By combining and connecting different types and quantities of automotive parts, a complete vehicle system is formed to achieve the normal operation of the vehicle.

[0022] In this embodiment, the target part is an automotive part with unspecified functions and tasks obtained by stamping process.

[0023] According to the model or name of the target component, directly interact with the manufacturer to obtain the design information of the target component, and further perform 3D digital modeling based on the design information to obtain the target digital model, which highly accurately restores the appearance and spatial structure of the target component. The target digital model obtained in this embodiment provides a reference basis for reducing the number of spatial structure parameters of the target component in the follow-up.

[0024] A200: Obtain a model reduction parameter set, where the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators;

[0025] In one embodiment, as Figure 2 shown, obtain a model reduction parameter set, where the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators. The method step A200 provided in this application further includes:

[0026] A210: Preset a surface division threshold, and based on the surface division threshold, divide the surface area of the target digital model to obtain M regional division results, where each regional division result has a regional position identifier;

[0027] A220: Identify the curvature parameters of the M regional division results to obtain M groups of curvature parameters;

[0028] A230: Based on the M groups of curvature parameters, perform adjacent merging on the M regional division results to obtain K model surface division results, where the K model surface division results are mapped to the K reduction parameter indicators;

[0029] A240: The K reduction parameter indicators constitute the model reduction parameter set.

[0030] Specifically, it should be understood that a spatial surface can be represented by different types of data index combinations. Some data index combinations have fewer parameter indicators, and some data index combinations have more parameter indicators. The purpose of digitizing the target component in this embodiment is to perform model construction data analysis based on the target digital model to obtain the model reduction parameter set that can accurately restore the target digital model and has fewer parameter indicators.

[0031] Specifically, in this embodiment, the method for obtaining a smaller amount of parameter indicators for constructing the target digital model is as follows. Based on the complexity of the spatial structure of the target digital model, in this embodiment, the target digital model is surface-divided, and the overall surface parameters of the target digital model are extracted by means of extracting local surface curve parameters.

[0032] The surface division threshold is the grid size constraint preset for surface meshing of the target digital model. For example, the surface division threshold is a square of 2*2 cm. Based on the surface division threshold, the surface area of the target digital model is meshed to obtain M regional division results. Each regional division result has a regional position identifier, and the regional position identifier can be achieved by pre-constructing a spatial coordinate system for the target digital model to ensure the accuracy of the surface area position identifier.

[0033] Based on the M regional division results, a first regional division result is randomly extracted, and a curvature calculation algorithm including but not limited to Gaussian curvature is used for the first regional division result to obtain multiple curvature parameters representing the surface of the first regional division result at different points.

[0034] The minimum and maximum principal curvatures are used as the main features to extract the corresponding curvature parameters from the above multiple curvature parameters, and information such as the corresponding principal curvature direction and curvature radius is recorded to generate the first characteristic curvature parameters.

[0035] Multiple sample parts of the same model as the target part are collected, and the principal curvature parameters of each area on the surface of the sample parts are extracted to obtain several sets of sample curvature parameter sets. Each set of sample curvature parameter sets has minimum and maximum principal curvature identifiers.

[0036] The first characteristic curvature parameters are used to traverse the several sets of sample curvature parameter sets to obtain a set of sample curvature parameter sets corresponding to the minimum and maximum principal curvature identifiers with the same meaning as the first characteristic curvature parameters. This set of sample curvature parameter sets is used as the first curvature parameters of the first regional division result.

[0037] And so on, the curvature parameters of the M regional division results are identified to obtain M sets of curvature parameters.

[0038] Based on whether the curvature parameters of two adjacent regional division results are consistent, the M regional division results are merged adjacent to each other according to the M sets of curvature parameters. Finally, all the curvature parameters on the surface of the target digital model are obtained, a total of K model surface division results. The K model surface division results are the K reduction parameter indicators that can effectively restore the target digital model, and the K reduction parameter indicators constitute the model reduction parameter set.

[0039] In this embodiment, by digitally modeling the target part and further performing data analysis based on the target digital model, the technical effect of obtaining the model reduction parameter set that can accurately restore the target digital model and has a small number of parameter indicators is achieved.

[0040] A300: Obtain the first set of reduction parameters, and input the first set of reduction parameters into the model simulation reduction module to obtain the first simulation reduction model;

[0041] In one embodiment, as Figure 3 shown, to obtain the first set of reduction parameters, and input the first set of reduction parameters into the model simulation reduction module to obtain the first simulation reduction model, the method step A300 provided by this application further includes:

[0042] A310: Input the target digital model into the reduction model testing module, perform the anti-deformation performance test of the target digital model based on the reduction model testing module, and generate the first structural test result;

[0043] A320: Preset an index adjustment threshold, and adjust the K reduction parameter indexes based on the index adjustment threshold to obtain K sets of adjusted parameter index sets, where only one parameter index in each adjusted parameter index set is adjusted;

[0044] A330: Input the K sets of adjusted parameter index sets into the model simulation reduction module to obtain K adjusted simulation models;

[0045] A340: Input the K adjusted simulation models into the reduction model testing module to obtain K adjusted structural test results;

[0046] A350: Generate K reduction parameter adjustment step sizes according to the first structural test result and the K adjusted structural test results;

[0047] A360: Interactively obtain the K parameter index value constraints of the K reduction parameter indexes, and construct a parameter optimization search space based on the K parameter index value constraints;

[0048] A370: Select K random reduction parameters in the parameter optimization search space to generate the first set of reduction parameters.

[0049] Specifically, in this embodiment, the reduction model testing module is used to perform the anti-compression and anti-deformation performance test of the device based on the three-dimensional model of the device. The reduction model testing module defaults to input the three-dimensional model as a standardized material to exclude the interference of the material on the anti-compression and anti-deformation ability of the three-dimensional structure.

[0050] Input the target digital model into the reduction model test module, perform the anti-deformation performance test of the target digital model based on the reduction model test module, generate the compressive performance test result and the anti-deformation ability test result of the target digital model. Further, set the first weight and the second weight according to the requirements for the compressive performance and anti-deformation ability of the target component, and perform weighted calculation based on the first weight, the second weight, the compressive performance test result and the anti-deformation ability test result of the target digital model to obtain the first structure test result.

[0051] Preset an index adjustment threshold, where the index adjustment threshold is a constraint on the adjustment degree of the K reduction parameter indexes. For example, increase the parameter value of each reduction parameter index among the K reduction parameter indexes by 5%. Adjust the K reduction parameter indexes one by one based on the index adjustment threshold, and based on the control variable method, only one parameter index in each set of adjusted parameter indexes is adjusted to obtain K sets of adjusted parameter indexes.

[0052] Based on the K sets of adjusted parameter indexes, the model simulation reduction module performs the reduction construction of the 3D model to obtain K adjusted simulation models. Input the K adjusted simulation models into the reduction model test module to perform the device compressive and anti-deformation performance test, and use the same method as obtaining the first structure test result to obtain K adjusted structure test results.

[0053] Calculate the difference between the first structure test result and the K adjusted structure test results to obtain K test result deviations. Each test result deviation corresponds to a set of adjusted parameter indexes. Use the sum of the K test result deviations as the denominator and each of the K test result deviations as the numerator to calculate the K index change influence indexes of the K reduction parameter indexes for the compressive and anti-deformation performance of the target component.

[0054] Divide the index adjustment threshold by the K index change influence indexes one by one to obtain the K reduction parameter adjustment steps. The reduction parameter adjustment step is the numerical change amount for each optimization when performing the optimization of the reduction parameter indexes.

[0055] The parameter index value constraint is the digital selectable range of the reduction parameter index. In this embodiment, the K parameter index value constraints of the K reduction parameter indexes are obtained through interaction, and a parameter optimization search space is constructed based on the K parameter index value constraints, which realizes reducing the optimization search range of the reduction parameter indexes and improving the efficiency of obtaining the K parameter index values that meet the requirements. Randomly select the K random reduction parameters in the parameter optimization search space to generate the first reduction parameter group, and the first reduction parameter group is the optimization starting point.

[0056] In this embodiment, by constructing a parameter optimization search space, setting the starting point for optimizing the taste of the first reduction parameter and setting K adjustment steps for the reduction parameter, the technical effect of providing an effective constraint for quickly determining the optimized result of the component parameters subsequently is achieved.

[0057] A400: Prescribe an optimization rule for the reduction parameter, and taking the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint, perform the reduction parameter optimization of the target component to obtain the optimized result of the component parameters;

[0058] In one embodiment, prescribing an optimization rule for the reduction parameter, and taking the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint, perform the reduction parameter optimization of the target component to obtain the optimized result of the component parameters. The method step A400 provided in this application further includes:

[0059] A410: Input the first simulation reduction model into the reduction model test module to obtain the first simulation test result;

[0060] A420: Construct the reduction parameter optimization rule based on the K adjustment steps of the reduction parameter;

[0061] A430: Taking the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint, obtain the second reduction parameter group;

[0062] A440: Input the second reduction parameter group into the model simulation reduction module to obtain the second simulation reduction model;

[0063] A450: Input the second simulation reduction model into the reduction model test module to obtain the second simulation test result;

[0064] A460: Compare the first simulation test result and the second simulation test result. If the first simulation test result is inferior to the second simulation test result, temporarily store the second simulation test result and the second reduction parameter group in the stamping process parameter library;

[0065] A470: Taking the second reduction parameter group as the optimization starting point and the reduction parameter optimization rule as the constraint, obtain the third reduction parameter group;

[0066] A480: And so on, perform the reduction parameter optimization of the target component to obtain the optimized result of the component parameters.

[0067] In one embodiment, the method steps provided in this application further include:

[0068] A481: Preset a qualified constraint for the test result and a stop constraint for the optimization frequency;

[0069] A482: If the simulation test result meets the qualified test result constraint, or the actual optimization frequency meets the optimization frequency stop constraint, an optimization stop instruction is generated.

[0070] A483: The optimal restoration parameters corresponding to the optimal simulation restoration model corresponding to the optimization stop instruction are used as the component parameter optimization result.

[0071] Specifically, in this embodiment, the first restoration parameter group is input into the model simulation restoration module to obtain a first simulation restoration model, and the first simulation restoration model is input into the restoration model test module to obtain a first simulation test result, where the first simulation test result is a comprehensive evaluation result of the compressive and deformation resistance performance of the first simulation restoration model.

[0072] The K restoration parameter adjustment steps form the restoration parameter optimization rule, which is used to limit the parameter change amount when each of the K restoration parameter indicators changes. Starting from the first simulation restoration model as the optimization starting point and subject to the restoration parameter optimization rule, a second restoration parameter group is obtained.

[0073] The second restoration parameter group is input into the model simulation restoration module to obtain a second simulation restoration model; the second simulation restoration model is input into the restoration model test module to obtain a second simulation test result. The first simulation test result and the second simulation test result are compared. If the first simulation test result is inferior to the second simulation test result, the second simulation test result and the second restoration parameter group are temporarily stored in the stamping process parameter library, and starting from the second restoration parameter group as the optimization starting point and subject to the restoration parameter optimization rule, a third restoration parameter group is obtained, and so on. One of the two restoration parameter groups is selected and retained according to the simulation test result.

[0074] To avoid the restoration parameter optimization of the target component from falling into an infinite loop, this embodiment presets stop optimization conditions, specifically including a qualified test result constraint and an optimization frequency stop constraint. The qualified test result constraint includes a compressive performance qualified index and a deformation resistance performance qualified index, and the optimization frequency stop constraint is to assume how many times of optimization iteration is considered that the obtained restoration parameter group is optimal.

[0075] If the simulation test result meets the qualified test result constraint, or the actual optimization frequency meets the optimization frequency stop constraint, an optimization stop instruction is generated; the optimal restoration parameters corresponding to the optimal simulation restoration model corresponding to the optimization stop instruction are used as the component parameter optimization result.

[0076] In this embodiment, by adaptively setting the optimization step size and setting the optimization stop rule, the technical effect of quickly and efficiently obtaining the component parameter data for improving the performance of the target component is achieved.

[0077] A500: Interactively obtain the raw material size parameters of the stamping process raw material;

[0078] Specifically, in this embodiment, the stamping process raw material is the raw material for processing and producing the target component, and the stamping process raw material includes but is not limited to cold-rolled steel strips, stainless steel, and titanium alloys. The raw material size parameter is the raw material size of a single piece of the stamping process raw material. For example, the raw material size parameter is the length and width data of the cold-rolled steel strip.

[0079] A600: Obtain the stamping positioning optimization result, where the stamping positioning optimization result is determined by superposition simulation analysis with the raw material size parameter and the component parameter optimization result as constraints;

[0080] In one embodiment, to obtain the stamping positioning optimization result, where the stamping positioning optimization result is determined by superposition simulation analysis with the raw material size parameter and the component parameter optimization result as constraints, the method step A600 provided in this application further includes:

[0081] A610: Obtain the stamping raw material consumption parameter according to the component parameter optimization result;

[0082] A620: Generate the minimum stamping interval constraint according to the stamping raw material consumption parameter;

[0083] A630: Generate a falling vertical interlayer by fitting based on the raw material size parameter;

[0084] A640: Perform falling superposition simulation on the falling vertical interlayer based on the minimum stamping interval constraint to generate the stamping positioning optimization result.

[0085] Specifically, in this embodiment, the stamping raw material consumption parameter is the shape and size parameter of the punched hole generated on the stamping raw material after the stamping equipment is debugged with reference to the component parameter optimization interface and the optimized target component entity in actual production.

[0086] Due to the interval distance between the punched holes, and in this embodiment, the preset interval constraint is used to expand the contour of the stamping raw material consumption parameter to generate the minimum stamping interval constraint. The real-time minimum stamping interval constraint is to realize the size constraint of each punched hole when performing multiple stampings on the stamping process raw material to obtain multiple target component entities.

[0087] A falling vertical interlayer is generated by fitting based on the raw material size parameters. The falling vertical interlayer is a rectangular interlayer structure formed by assuming a vertical arrangement with the length and width of the raw material size parameters.

[0088] Using the minimum stamping interval constraint, several planar images are generated and poured into the falling vertical interlayer for a falling superposition simulation similar to the volume counting method. When several planar images are under the action of "gravity" and reach the maximum accommodation capacity in the falling vertical interlayer, the dispersion position of several planar images in the falling vertical interlayer is obtained, which constitutes the stamping position layout when the stamping equipment stamps the stamping process raw material, forming the stamping positioning optimization result.

[0089] In this embodiment, by adopting the falling superposition model, the technical effects of efficiently obtaining the maximum number of target components that can be stamped on the surface of the stamping process raw material and the stamping positions of the stamping equipment on the surface of the stamping process raw material when achieving the maximum number of stamped target components are realized.

[0090] The stamping operation equipment uses the stamping die produced according to the component parameter optimization result and takes the stamping positioning optimization result as the stamping positioning reference on the surface of the stamping process raw material, so as to realize the stamping production of the maximum number of target components with the minimum raw material consumption on the surface of the stamping process raw material.

[0091] A700: Generate the component stamping optimization result, where the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result.

[0092] In one embodiment, the method steps provided by this application further include:

[0093] A710: Preset a stamping process monitoring window;

[0094] A720: Using the stamping process monitoring window as a constraint, adopt the component stamping optimization result to perform batch stamping processing of the target components to obtain a sample component set;

[0095] A730: Restore and generate optimized component design information according to the component parameter optimization result;

[0096] A740: Perform sampling quality inspection on the sample component set according to the optimized component design information to obtain a sample sampling quality inspection result;

[0097] A750: Identify the hardware faults of the stamping equipment according to the sample sampling quality inspection result.

[0098] Specifically, in this embodiment, the stamping equipment uses a stamping die produced based on the optimized results of the component parameters, and uses the optimized stamping positioning results as the stamping positioning reference on the surface of the stamping process raw material, so as to achieve the technical effect of improving the utilization rate of the stamping process raw material while stamping and producing automotive parts that meet the automotive adaptability requirements.

[0099] Specifically, it should be understood that theoretically, the stamping equipment uses a stamping die produced based on the optimized results of the component parameters, and uses the optimized stamping positioning results as the stamping positioning reference on the surface of the stamping process raw material, so as to achieve continuous and stable production of target parts that meet the requirements. However, this situation does not consider the production deviation caused by the equipment failure defects of the stamping equipment.

[0100] Based on this, this embodiment presets a stamping process monitoring window, and the stamping process monitoring window is based on the production cycle of the target parts set manually. Exemplarily, the stamping process monitoring window is 15 consecutive minutes.

[0101] Constrained by the stamping process monitoring window, the stamping equipment uses the stamping die produced by the optimized results of the component stamping to perform batch stamping processing of the target parts, and obtains a set of sample parts.

[0102] Construct a digital model of the optimized parts according to the optimized results of the component parameters to obtain an optimized digital model. Both the optimized digital model and the target digital model are 3D models.

[0103] Adopt the method of constructing and generating a target digital model according to the design information of the target parts, and inversely deduce the optimized component design information based on the optimized digital model. Use the optimized component design information as the qualified size constraint for the production of parts to conduct sampling quality inspection on the set of sample parts, and obtain the sample sampling quality inspection results. The sample sampling quality inspection results include two types: qualified quality inspection and unqualified quality inspection.

[0104] If the sample sampling quality inspection result is qualified, continue to conduct production monitoring based on the stamping process monitoring window. Conversely, if the sample sampling quality inspection result is unqualified, suspend the stamping part production, and send a stamping equipment detection instruction to the operation and maintenance personnel, so that the operation and maintenance personnel can identify and perform operation and maintenance on the hardware faults of the stamping equipment according to the sample sampling quality inspection results.

[0105] This embodiment realizes the technical effects of avoiding waste of stamping raw materials caused by stamping equipment failures and reducing the production cost of stamping parts by setting a stamping process monitoring window for automotive parts production monitoring.

[0106] Embodiment 2

[0107] Based on the same inventive concept as an optimization method for an automotive component stamping process in the foregoing embodiments, as Figure 4 shown, the present application provides an optimization system for an automotive component stamping process, wherein the system includes:

[0108] A digital model construction module 1 for constructing a target digital model, wherein the target digital model is constructed and generated according to the design information of the target component;

[0109] A model parameter generation module 2 for obtaining a model reduction parameter set, wherein the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators;

[0110] A reduction model generation module 3 for obtaining a first reduction parameter group and inputting the first reduction parameter group into a model simulation reduction module to obtain a first simulation reduction model;

[0111] A component parameter optimization module 4 for predetermining a reduction parameter optimization rule and, starting from the first simulation reduction model as an optimization starting point and with the reduction parameter optimization rule as a constraint, performing reduction parameter optimization of the target component to obtain a component parameter optimization result;

[0112] A dimension parameter interaction module 5 for interactively obtaining raw material dimension parameters of the stamping process;

[0113] A superposition simulation analysis module 6 for obtaining a stamping positioning optimization result, wherein the stamping positioning optimization result is determined by superposition simulation analysis with the raw material dimension parameters and the component parameter optimization result as constraints;

[0114] A stamping parameter output module 7 for generating a component stamping optimization result, wherein the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result.

[0115] In one embodiment, the superposition simulation analysis module 6 further includes:

[0116] Obtaining stamping raw material consumption parameters according to the component parameter optimization result;

[0117] Generating a minimum stamping interval constraint according to the stamping raw material consumption parameters;

[0118] Fitting a falling vertical interlayer based on the raw material dimension parameters;

[0119] Performing a falling superposition simulation on the falling vertical interlayer based on the minimum stamping interval constraint to generate the stamping positioning optimization result.

[0120] In one embodiment, the model parameter generation module 2 further includes:

[0121] Preset a surface division threshold, and perform surface area division on the target digital model based on the surface division threshold to obtain M regional division results, where each regional division result has a regional position identifier;

[0122] Perform curvature parameter identification on the M regional division results to obtain M groups of curvature parameters;

[0123] Perform adjacent merging on the M regional division results based on the M groups of curvature parameters to obtain K model surface division results, where the K model surface division results are mapped to the K reduction parameter indicators;

[0124] The K reduction parameter indicators constitute the model reduction parameter set.

[0125] In one embodiment, the reduction model generation module 3 further includes:

[0126] Input the target digital model into the reduction model test module, perform anti-deformation performance testing on the target digital model based on the reduction model test module, and generate a first structure test result;

[0127] Preset an index adjustment threshold, and adjust the K reduction parameter indicators based on the index adjustment threshold to obtain K sets of adjusted parameter indicator sets, where only one parameter indicator in each set of adjusted parameter indicator sets is adjusted;

[0128] Input the K sets of adjusted parameter indicator sets into the model simulation reduction module to obtain K adjusted simulation models;

[0129] Input the K adjusted simulation models into the reduction model test module to obtain K adjusted structure test results;

[0130] Generate K reduction parameter adjustment steps according to the first structure test result and the K adjusted structure test results;

[0131] Interactively obtain K parameter indicator value constraints of the K reduction parameter indicators, and construct a parameter optimization search space based on the K parameter indicator value constraints;

[0132] Select K random reduction parameters in the parameter optimization search space to generate the first reduction parameter group.

[0133] In one embodiment, the component parameter optimization module 4 further includes:

[0134] Input the first simulation reduction model into the reduction model test module to obtain a first simulation test result;

[0135] Construct the reduction parameter optimization rule based on the K reduction parameter adjustment step sizes;

[0136] Taking the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint, obtain the second set of reduction parameters;

[0137] Input the second set of reduction parameters into the model simulation reduction module to obtain the second simulation reduction model;

[0138] Input the second simulation reduction model into the reduction model test module to obtain the second simulation test result;

[0139] Compare the first simulation test result and the second simulation test result. If the first simulation test result is inferior to the second simulation test result, temporarily store the second simulation test result and the second set of reduction parameters in the stamping process parameter library;

[0140] Taking the second set of reduction parameters as the optimization starting point and the reduction parameter optimization rule as the constraint, obtain the third set of reduction parameters;

[0141] And so on, perform the reduction parameter optimization of the target component to obtain the component parameter optimization result.

[0142] In one embodiment, the component parameter optimization module 4 further includes:

[0143] Preset the qualified test result constraint and the optimization frequency stop constraint;

[0144] If the simulation test result meets the qualified test result constraint, or the actual optimization frequency meets the optimization frequency stop constraint, generate an optimization stop instruction;

[0145] Take the optimal reduction parameters corresponding to the optimal simulation reduction model corresponding to the optimization stop instruction as the component parameter optimization result.

[0146] In one embodiment, the stamping parameter output module 7 further includes:

[0147] Preset the stamping process monitoring window;

[0148] Taking the stamping process monitoring window as the constraint, use the component stamping optimization result to perform batch stamping processing of the target component to obtain a set of sample components;

[0149] Restore and generate optimized component design information according to the component parameter optimization result;

[0150] Perform sampling quality inspection on the set of sample components according to the optimized component design information to obtain the sample sampling quality inspection result;

[0151] Identify the hardware faults of the stamping equipment according to the quality inspection results of the sample sampling.

[0152] Any of the methods or steps described above can be stored as computer instructions or programs in various types of computer memories, and the computer instructions or programs can be recognized by various types of computer processors, thereby implementing any of the above methods or steps.

[0153] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principle of the present invention shall fall within the scope of patent protection of the present invention.

Claims

1. An optimization method for the stamping process of automotive parts, characterized in that The method is applied to an optimization system for an automotive parts stamping process. The system includes a model simulation restoration module and a restored model testing module. The method includes: Construct a target digital model, where the target digital model is constructed based on the design information of the target part; Obtain a model restoration parameter set, where the model restoration parameter set is obtained by extracting curve parameters from the target digital model, and the model restoration parameter set includes K restoration parameter indicators; Obtain a first set of restoration parameters and input the first set of restoration parameters into the model simulation restoration module to obtain a first simulation restoration model; Preset a restoration parameter optimization rule, and starting from the first simulation restoration model and constrained by the restoration parameter optimization rule, optimize the restoration parameters of the target part to obtain a component parameter optimization result; Interactively obtain the raw material size parameters of the stamping process raw materials; Obtain a stamping positioning optimization result, where the stamping positioning optimization result is determined by superimposed simulation analysis with the raw material size parameters and the component parameter optimization result as constraints; Generate a component stamping optimization result, where the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result; Obtain a model restoration parameter set, where the model restoration parameter set is obtained by extracting curve parameters from the target digital model, and the model restoration parameter set includes K restoration parameter indicators. The method further includes: Preset a surface division threshold and perform surface area division on the target digital model based on the surface division threshold to obtain M area division results, where each area division result has an area position identifier; Identify the curvature parameters of the M area division results to obtain M sets of curvature parameters; Based on the M sets of curvature parameters, perform adjacent merging on the M area division results to obtain K model surface division results, where the K model surface division results are mapped to the K restoration parameter indicators; The K restoration parameter indicators constitute the model restoration parameter set; Obtain a first set of restoration parameters and input the first set of restoration parameters into the model simulation restoration module to obtain a first simulation restoration model. The method further includes: Input the target digital model into the restored model testing module, perform an anti-deformation performance test on the target digital model based on the restored model testing module, and generate a first structure test result; Preset an index adjustment threshold and adjust the K restoration parameter indicators based on the index adjustment threshold to obtain K sets of adjusted parameter indicator sets, where only one parameter indicator in each adjusted parameter indicator set is adjusted; Input the K sets of adjusted parameter indicator sets into the model simulation restoration module to obtain K adjusted simulation models; Input the K adjusted simulation models into the restored model testing module to obtain K adjusted structure test results; Generate K restoration parameter adjustment steps according to the first structure test result and the K adjusted structure test results; Interactively obtain the K parameter index value constraints of the K reduction parameters, and construct a parameter optimization search space based on the K parameter index value constraints; Select K random reduction parameters in the parameter optimization search space to generate the first reduction parameter group; Preset a reduction parameter optimization rule, and use the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint to perform the reduction parameter optimization of the target component to obtain the component parameter optimization result. The method further includes: Input the first simulation reduction model into the reduction model test module to obtain the first simulation test result; Construct the reduction parameter optimization rule based on the K reduction parameter adjustment steps; 2. The method according to claim 1, wherein Obtain the stamping positioning optimization result, where the stamping positioning optimization result is determined by superimposing and simulating based on the raw material size parameters and the component parameter optimization result. The method further includes: Obtain the stamping raw material consumption parameter according to the component parameter optimization result; Generate the minimum stamping interval constraint according to the stamping raw material consumption parameter; Generate a falling vertical sandwich by fitting the raw material size parameters; Perform a falling superposition simulation on the falling vertical sandwich based on the minimum stamping interval constraint to generate the stamping positioning optimization result.

3. The method according to claim 1, wherein Preset a reduction parameter optimization rule, and use the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint to perform the reduction parameter optimization of the target component to obtain the component parameter optimization result. The method further includes: Use the first simulation reduction model as the optimization starting point and the reduction parameter optimization rule as the constraint to obtain the second reduction parameter group; Input the second reduction parameter group into the model simulation reduction module to obtain the second simulation reduction model; Input the second simulation reduction model into the reduction model test module to obtain the second simulation test result; Compare the first simulation test result and the second simulation test result. If the first simulation test result is inferior to the second simulation test result, temporarily store the second simulation test result and the second reduction parameter group in the stamping process parameter library; Use the second reduction parameter group as the optimization starting point and the reduction parameter optimization rule as the constraint to obtain the third reduction parameter group; And so on, perform the reduction parameter optimization of the target component to obtain the component parameter optimization result.

4. The method according to claim 3, wherein The method further includes: Preset a qualified test result constraint and an optimization frequency stop constraint; If the simulation test result meets the qualified test result constraint or the actual optimization frequency meets the optimization frequency stop constraint, generate an optimization stop instruction; Use the optimal reduction parameters corresponding to the optimal simulation reduction model corresponding to the optimization stop instruction as the component parameter optimization result.

5. The method according to claim 2, characterized in that, The method further includes: Preset a stamping process monitoring window; Using the component stamping optimization result as the constraint, perform batch stamping processing of the target component with the stamping process monitoring window to obtain a set of sample components; Restore and generate optimized component design information according to the component parameter optimization result; Perform sampling quality inspection on the sample component set according to the optimized component design information to obtain the sample sampling quality inspection result; Perform hardware fault identification of the stamping equipment according to the sample sampling quality inspection result.

6. An optimization system for the stamping process of automotive parts, characterized in that, The system includes: A digital model construction module for constructing a target digital model, where the target digital model is constructed and generated according to the design information of the target component; A model parameter generation module for obtaining a model reduction parameter set, where the model reduction parameter set is obtained by extracting curve parameters from the target digital model, and the model reduction parameter set includes K reduction parameter indicators; A reduced model generation module for obtaining a first set of reduced parameters and inputting the first set of reduced parameters into a model simulation reduction module to obtain a first simulation reduced model; A component parameter optimization module for presetting a reduced parameter optimization rule and starting from the first simulation reduced model as the optimization starting point and using the reduced parameter optimization rule as a constraint to optimize the reduced parameters of the target component to obtain a component parameter optimization result; A dimension parameter interaction module for interactively obtaining the raw material dimension parameters of the stamping process raw materials; A superposition simulation analysis module for obtaining a stamping positioning optimization result, where the stamping positioning optimization result is determined by performing superposition simulation analysis with the raw material dimension parameters and the component parameter optimization result as constraints; A stamping parameter output module for generating a component stamping optimization result, where the component stamping optimization result is composed of the component parameter optimization result and the stamping positioning optimization result; A reduced model testing module for testing the compressive and deformation resistance performance of the device based on the three-dimensional model of the device; The component parameter optimization module further includes: inputting the first simulation reduced model into the reduced model testing module to obtain a first simulation test result; constructing the reduced parameter optimization rule based on the K reduction parameter adjustment steps.

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