Production process optimization method, system, device and storage medium for automobile parts

Abstract

The present invention relates to a production process optimization method, system, device, and storage medium for automotive parts. The method comprises obtaining design parameters of an automotive brake disc, performing process flow analysis based on the design parameters to obtain an initial process plan; simulating the initial process plan to obtain simulation optimization parameters; obtaining measured process parameters during the production process, performing matrix comparison analysis between the measured process parameters and the simulation optimization parameters to obtain a deviation matrix, performing link analysis based on the deviation matrix to obtain a list of links to be optimized; iteratively adjusting the simulation optimization parameters based on the list of links to be optimized to obtain optimized process parameters; constructing a three-dimensional sample model based on the optimized process parameters, performing quality analysis on the three-dimensional sample model to obtain a quality assessment result; and performing solution analysis based on the quality assessment result and the optimized process parameters to obtain a corresponding production process optimization plan. The present invention can effectively improve production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile parts production, and in particular to a production process optimization method, system, device and storage medium for automobile parts. Background Art

[0002] As an important part of the automotive manufacturing industry, optimizing the production process of auto parts is of great significance to improving the overall performance and reliability of automobiles. With the rapid development of the automotive industry and the continuous improvement of consumers' requirements for automobile quality, how to effectively optimize the production process of auto parts to achieve high-quality and low-cost production goals has become one of the focuses of industry attention. However, existing methods for optimizing the production process of auto parts often have some limitations. These methods usually only focus on certain specific links in the production process, such as material selection or processing steps, while ignoring the systematic nature of the entire production process and the mutual influence between various links. In addition, traditional optimization methods often rely on empirical adjustments, lack scientific data support and precise quantitative analysis, and cannot meet the high requirements of modern auto parts production for precision and efficiency. Summary of the Invention

[0003] The main purpose of the present invention is to provide a production process optimization method, system, device and storage medium for automotive parts, which can effectively balance multiple factors such as production efficiency, product quality and cost control.

[0004] To achieve the above objectives, the present invention provides a method for optimizing the production process of automotive parts, comprising:

[0005] Obtaining design parameters of the automobile brake disc, performing process analysis based on the design parameters, and obtaining an initial process plan;

[0006] simulating the initial process plan to obtain simulation optimization parameters;

[0007] Obtaining the measured process parameters during the production process, performing matrix comparison analysis on the measured process parameters and the simulation optimization parameters to obtain a deviation matrix, performing link analysis based on the deviation matrix to obtain a list of links to be optimized;

[0008] Iteratively simulate and adjust the simulation optimization parameters according to the list of links to be optimized to obtain optimized process parameters;

[0009] constructing a three-dimensional sample model based on the optimized process parameters, and performing quality analysis on the three-dimensional sample model to obtain a quality assessment result;

[0010] Based on the quality assessment results and the optimized process parameters, a solution analysis is performed to obtain a corresponding production process optimization solution.

[0011] Furthermore, the obtaining of design parameters of the automobile brake disc, performing process analysis based on the design parameters, and obtaining an initial process solution include:

[0012] Acquiring three-dimensional data of the automobile brake disc, performing geometric feature extraction on the three-dimensional data, and obtaining geometric feature parameters;

[0013] Performing material property analysis on the automobile brake disc according to the geometric characteristic parameters to obtain material parameters;

[0014] Based on the geometric characteristic parameters and the material parameters, a force analysis is performed on the automobile brake disc to obtain mechanical performance parameters;

[0015] Integrating the geometric characteristic parameters, the material parameters and the mechanical property parameters to obtain the design parameters;

[0016] Performing initial process matching on the design parameters through a preset process knowledge base to obtain an initial process flow;

[0017] The processing parameters of the process steps of the preliminary process flow are set based on the design parameters to obtain the initial process plan.

[0018] Furthermore, simulating the initial process plan to obtain simulation optimization parameters includes:

[0019] Performing parameterized description on the process flow in the initial process plan to obtain a process flow parameter set;

[0020] Performing simulation construction according to the process flow parameter set to obtain a corresponding process simulation scenario, setting process parameters for the process simulation scenario to obtain an initial simulation configuration;

[0021] Performing iterative calculations on the initial simulation configuration to obtain multiple sets of simulation result data;

[0022] Constructing an index graph for the multiple sets of simulation result data to obtain a performance index distribution graph;

[0023] updating parameters of the initial simulation configuration according to the performance indicator distribution graph to obtain an optimized simulation configuration;

[0024] Performing a verification simulation on the optimized simulation configuration to obtain a verification result;

[0025] Key process parameters are extracted from the optimized simulation configuration according to the verification results to obtain simulation optimization parameters.

[0026] Furthermore, the measured process parameters in the production process are obtained, the measured process parameters are compared and analyzed with the simulation optimization parameters to obtain error parameters, and the link analysis is performed based on the error parameters to obtain a list of links to be optimized, including:

[0027] Collecting real-time data from sensors arranged on the parts production line to obtain the real-time process parameters;

[0028] The real-time process parameters are matrix-constructed to obtain a first matrix, and the simulation optimization parameters are matrix-constructed to obtain a second matrix, wherein the row and column structure of the first matrix is ​​the same as that of the second matrix;

[0029] Comparing the relative deviations of the corresponding row and column elements in the first matrix and the second matrix one by one to obtain a deviation matrix;

[0030] Performing statistical analysis on each row of the deviation matrix to obtain an average deviation value and a deviation standard deviation;

[0031] Calculating a comprehensive deviation index based on the average deviation value and the deviation standard deviation to obtain a parameter deviation value vector;

[0032] Sorting the parameter deviation value vectors to obtain a deviation degree ranking table;

[0033] Based on the deviation degree ranking table, the process links are screened through multi-level screening and dynamic threshold method to obtain the list of links to be optimized, which specifically includes:

[0034] Setting an initial preset threshold and a secondary threshold, wherein the secondary threshold is smaller than the initial preset threshold;

[0035] Performing a first round of screening on the deviation degree ranking table according to the initial preset threshold value to obtain a first process link exceeding the initial preset threshold value and a second process link between the secondary threshold value and the initial preset threshold value;

[0036] Mark the first process step as a first-level step to be optimized;

[0037] Performing a secondary screening on the second process link, calculating the rate of change of the deviation value of the second process link, and if the rate of change exceeds a predetermined deviation threshold, marking the second process link exceeding the predetermined deviation threshold as a secondary process link to be optimized;

[0038] Performing a correlation matrix analysis on the first-level links to be optimized and the second-level links to be optimized according to a preset correlation threshold to obtain corresponding associated process links, performing deviation value discrimination on the associated process links, and marking the associated process links whose deviation values ​​exceed the secondary threshold as third-level links to be optimized;

[0039] The first-level links to be optimized, the second-level links to be optimized, and the third-level links to be optimized are sorted and integrated according to priority to obtain a list of links to be optimized.

[0040] Furthermore, the iterative simulation adjustment of the simulation optimization parameters according to the list of links to be optimized to obtain the optimized process parameters includes:

[0041] Performing parameter sensitivity analysis on the list of links to be optimized to obtain a list of sensitivity parameters;

[0042] Classifying the simulation optimization parameters according to the sensitivity parameter list to obtain a graded parameter set;

[0043] Performing an orthogonal experimental design on each parameter in the hierarchical parameter set to obtain an orthogonal experimental scheme;

[0044] Perform simulation calculations according to the orthogonal test scheme to obtain a simulation result data set;

[0045] Performing parameter analysis on the hierarchical parameter set according to the simulation result data set to obtain preliminary optimization parameters;

[0046] Performing parameter extraction on the preliminary optimization parameters according to a preset threshold parameter to obtain optimized extraction parameters;

[0047] Performing verification calculations based on the optimized extraction parameters to obtain verification results;

[0048] The optimized extraction parameters are adjusted according to the verification results to obtain the optimized process parameters.

[0049] Furthermore, the three-dimensional sample model is constructed based on the optimized process parameters, and the quality analysis is performed on the three-dimensional sample model to obtain a quality assessment result, including:

[0050] Performing a three-dimensional analysis on the optimized process parameters to obtain corresponding geometric characteristic parameters, and constructing a three-dimensional model based on the geometric characteristic parameters to obtain the three-dimensional sample model;

[0051] Meshing the three-dimensional sample model to obtain a finite element model;

[0052] Performing stress analysis on the finite element model based on preset boundary load conditions to obtain stress distribution data;

[0053] Calculating the maximum stress value and the stress concentration area based on the stress distribution data to obtain a stress assessment result;

[0054] Performing modal analysis on the three-dimensional sample model to obtain natural frequency and mode shape data;

[0055] Calculating the modal participation factor based on the natural frequency and mode shape data to obtain a dynamic characteristics evaluation result;

[0056] Performing thermal analysis on the three-dimensional sample model to obtain temperature distribution and thermal stress data;

[0057] Calculate the maximum thermal deformation and the thermal stress concentration area based on the temperature distribution and thermal stress data to obtain a thermal performance evaluation result;

[0058] The stress evaluation results, dynamic characteristics evaluation results and thermal performance evaluation results are comprehensively analyzed to obtain the quality evaluation results.

[0059] Furthermore, the scheme analysis is performed based on the quality assessment results combined with the optimized process parameters to obtain a corresponding production process optimization scheme.

[0060] Performing indicator conversion on the quality assessment result to obtain an assessment indicator set;

[0061] Allocating weights to the optimized process parameters according to the evaluation index set to obtain weighted process parameters;

[0062] Performing multidimensional analysis on the weighted process parameters to obtain parameter optimization ranges;

[0063] Sampling the parameter optimization range by a preset search method to obtain a potential optimization point set;

[0064] Scoring the potential optimization point set according to a preset multi-objective evaluation standard to obtain a preliminary scoring result;

[0065] Performing a solution analysis on the potential optimization point set based on the preliminary scoring results to obtain an optimization solution set;

[0066] Performing parameter sensitivity analysis on the optimization solution set to obtain influencing factor information;

[0067] Performing scheme optimization screening on the optimization scheme set based on the information degree of the influencing factors to obtain a candidate process scheme set;

[0068] Screening the candidate process solution set according to preset process constraints to obtain a feasible process solution set;

[0069] Performing scheme analysis on each scheme in the set of feasible process schemes to obtain a scheme score;

[0070] The feasible process solution set is sorted according to the solution scores, and the solution with the highest score is selected as the final production process optimization solution.

[0071] The present invention further provides a production process optimization system for automobile parts, which is applied to any of the above-mentioned production process optimization methods for automobile parts, comprising:

[0072] An acquisition module, the acquisition module is used to obtain design parameters of the automotive parts, perform process analysis based on the design parameters, and obtain an initial process plan;

[0073] Analysis module, which is used to simulate the initial process plan and obtain simulation optimization parameters

[0074] An association module is used to obtain measured process parameters in the production process, perform matrix comparison analysis on the measured process parameters and the simulation optimization parameters to obtain a deviation matrix, perform link analysis based on the deviation matrix, and obtain a list of links to be optimized;

[0075] A processing module, the processing module is used to iteratively simulate and adjust the simulation optimization parameters according to the list of links to be optimized to obtain optimized process parameters;

[0076] a control module, wherein the control module constructs a three-dimensional sample model based on the optimized process parameters, performs quality analysis on the three-dimensional sample model, and obtains an evaluation result;

[0077] An execution module is used to perform a solution analysis based on the evaluation results and the optimized process parameters to obtain a corresponding production process optimization solution.

[0078] The present invention also provides a production process optimization device for automobile parts, comprising:

[0079] Memory, used to store programs;

[0080] The processor is used to execute the program to implement each step of the production process optimization method for automobile parts described in any one of the above.

[0081] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0082] The present invention provides a method, system, device, and storage medium for optimizing the production process of automotive parts, which have the following beneficial effects:

[0083] By obtaining the design parameters of automotive parts and conducting process flow analysis, a comprehensive consideration of all aspects of the production process can be developed, laying the foundation for subsequent optimization. Simulating the initial process plan and obtaining optimized simulation parameters, combined with matrix comparison analysis of measured process parameters from actual production, precisely identifies the areas requiring optimization, avoiding the limitations of traditional empirical optimization methods and improving optimization accuracy and efficiency. Iterative simulation adjustments and quality analysis of 3D sample models enable refined adjustment and verification of optimized process parameters, ensuring the reliability and practicality of the optimization plan. This data- and model-based optimization approach significantly enhances the scientific nature and accuracy of automotive parts production process optimization. The resulting production process optimization plan comprehensively considers design parameters, simulation results, actual production data, and quality assessment results, effectively balancing multiple factors such as production efficiency, product quality, and cost control, providing a comprehensive and efficient optimization solution for automotive parts production. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 This is a flow chart of a production process optimization method for automotive parts provided by the present invention;

[0085] Figure 2 This is a structural diagram of a production process optimization system for automotive parts provided by the present invention;

[0086] Figure 3 This is a structural diagram of a production process optimization device for automobile parts provided by the present invention.

[0087] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0089] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0090] Reference Figure 1 As shown, the present invention provides a method for optimizing the production process of automobile parts, comprising:

[0091] Step S1: obtaining design parameters of the automobile brake disc, performing process analysis based on the design parameters, and obtaining an initial process plan;

[0092] Step S2: simulating the initial process plan to obtain simulation optimization parameters;

[0093] Step S3: Obtain the measured process parameters during the production process, perform matrix comparison analysis on the measured process parameters and the simulation optimization parameters to obtain a deviation matrix, perform link analysis based on the deviation matrix, and obtain a list of links to be optimized;

[0094] Step S4: iteratively adjust the simulation optimization parameters according to the list of links to be optimized to obtain optimized process parameters;

[0095] Step S5: constructing a three-dimensional sample model based on the optimized process parameters, and performing quality analysis on the three-dimensional sample model to obtain a quality assessment result;

[0096] Step S6: Based on the quality assessment results and the optimized process parameters, a solution analysis is performed to obtain a corresponding production process optimization solution.

[0097] Based on the above steps, the detailed process is as follows:

[0098] Step S1: Obtain detailed brake disc design parameters from the automaker. These parameters include the disc's diameter, thickness, material composition, surface roughness requirements, and heat treatment specifications. Once these parameters are obtained, analyze their impact on the production process. For example, material composition influences the casting process, while dimensional accuracy requirements determine the machining method and precision level.

[0099] Based on these parameters, a preliminary process flow is developed. This includes multiple steps, including raw material selection, casting, heat treatment, rough machining, finishing, and surface treatment. When developing this initial process plan, engineers consider factors such as existing production equipment capabilities, process technology level, production efficiency, and cost. They may use a computer-aided process planning (CAPP) system to assist in decision-making. The system can quickly generate a feasible process route based on the input design parameters and production conditions. An initial process plan is then generated, including the operating steps for each process step, process parameter settings, and key quality control points.

[0100] Step S2: After obtaining the initial process plan, a virtual simulation is performed on the plan, involving multiple aspects of simulation, including casting process simulation, heat treatment simulation, and machining simulation. Casting simulation simulates the flow and solidification of molten metal to predict potential defects such as shrinkage and pores. Heat treatment simulation predicts the microstructure evolution and performance changes of the material under different heat treatment processes. Machining simulation simulates parameters such as cutting force, cutting temperature, and surface quality.

[0101] By adjusting various parameters in the simulation, such as pouring temperature, cooling rate, cutting parameters, etc., the theoretically optimal combination of process parameters is found. These optimized parameters are called simulation optimization parameters, which represent the best process state that can be achieved under ideal conditions.

[0102] Step S3: Integrate theoretical simulation with actual production to collect actual process parameters for each key step during production, including actual casting temperature, cooling time, machining speed and feed rate, heat treatment temperature curve, etc. Data collection is automatically completed through various sensors and data acquisition systems.

[0103] After obtaining these measured data, we compared them with the optimized parameters from simulation. This was done using a matrix format, creating a comparison matrix that included all key parameters, listing the simulated and measured values. By calculating the deviation for each parameter, we generated a deviation matrix. This deviation matrix clearly demonstrated the gap between theoretical expectations and actual conditions.

[0104] Analyze this deviation matrix to identify parameters with significant deviations. Then, trace the production links corresponding to these parameters to identify which links deviate significantly from the ideal state and obtain the analysis results. Based on the analysis results, a list of links to be optimized is generated and ranked according to urgency and importance.

[0105] Step S4: Based on the identified list of optimization steps, perform parameter adjustments and testing. First, develop a detailed experimental plan and design a series of small-scale simulation experiments, each targeting one or several relevant parameters. During the simulation process, gradually adjust process parameters for each step, such as casting temperature, cooling time, machining speed and feed rate, and heat treatment temperature profile.

[0106] After each parameter adjustment, record the various indicators of the production process in detail, including but not limited to product dimensional accuracy, surface quality, material properties, etc. Then compare the production results under different parameter settings, evaluate the impact of each adjustment on product quality and production efficiency, and obtain the evaluation results.

[0107] Based on the evaluation results, further parameter adjustments are made and the above process is repeated until the optimal parameter combination is found. Through this repeated debugging and optimization in actual production, a set of verified optimized process parameters is finally obtained.

[0108] Step S5: Construct an accurate 3D model of the brake disc based on the optimized process parameters. This model not only includes the geometric shape of the brake disc, but also reflects the influence of process factors such as material properties and surface treatment.

[0109] Perform a comprehensive quality analysis on this 3D model, including but not limited to the following aspects:

[0110] Structural strength analysis: simulate the stress distribution and deformation of the brake disc under various working conditions.

[0111] Thermal analysis: Evaluate the performance of brake discs in high temperature working environments.

[0112] Fatigue analysis: Predict the possible fatigue failure locations of brake discs after long-term use.

[0113] Vibration analysis: Checks whether the brake disc resonates at certain frequencies.

[0114] Life test: Perform accelerated life test to evaluate the long-term reliability of the brake disc.

[0115] All test results are integrated and analyzed to obtain quality assessment results.

[0116] Step S6: Compare product quality and production efficiency before and after optimization to quantify the improvements brought about by optimization. Consider the feasibility of the optimized parameters in large-scale production, including equipment capacity, operational difficulty, cost impact, etc. Based on the optimization results, formulate a detailed process improvement plan, including equipment adjustment, process flow modification, quality control standard update, etc. Develop a training plan for operators and quality inspectors to ensure that they can correctly implement the new process flow. Evaluate the investment required to implement the optimization plan and the expected economic benefits. Develop an implementation schedule for the optimization plan, including a phased implementation plan and effect evaluation nodes. Obtain the corresponding production process optimization plan.

[0117] The present invention provides a method for optimizing the production process of automotive parts. By obtaining the design parameters of the automotive parts and performing process flow analysis, it can comprehensively consider all aspects of the production process, thereby formulating a more systematic and comprehensive initial process plan, laying the foundation for subsequent optimization. The initial process plan is simulated to obtain simulation optimization parameters. Matrix comparison analysis is then performed in combination with the measured process parameters during the actual production process to accurately identify the aspects that need optimization, avoiding the limitations of traditional empirical optimization methods and improving the accuracy and efficiency of optimization. Through iterative simulation adjustment and quality analysis of three-dimensional sample models, refined adjustment and verification of the optimized process parameters are achieved, ensuring the reliability and practicality of the optimization plan. This data- and model-based optimization method greatly improves the scientific nature and accuracy of automotive parts production process optimization. The resulting production process optimization plan comprehensively considers design parameters, simulation results, actual production data, and quality assessment results, effectively balancing multiple factors such as production efficiency, product quality, and cost control, providing a comprehensive and efficient optimization solution for automotive parts production.

[0118] In one embodiment, design parameters of a vehicle brake disc are obtained, and a process flow analysis is performed based on the design parameters to obtain an initial process plan, including:

[0119] The three-dimensional data is meshed to obtain a mesh model; edge detection is performed on the mesh model to extract the contour line of the brake disc; and geometric characteristic parameters such as the diameter, thickness, and number of ventilation holes of the brake disc are calculated based on the contour line.

[0120] The preset material database is queried based on the geometric characteristic parameters to obtain a list of candidate materials; the physical properties such as thermal conductivity, density and hardness of the candidate materials are analyzed; based on the results of the physical property analysis, the most suitable material is selected to obtain the material parameters.

[0121] Establish a finite element model of the brake disc; set the boundary conditions and load conditions of the brake disc; perform static analysis to calculate the stress distribution and deformation of the brake disc; perform thermal-mechanical coupling analysis to calculate the temperature distribution and thermal stress of the brake disc; based on the analysis results, obtain the mechanical performance parameters of the brake disc, such as strength, stiffness, and thermal stability.

[0122] Establish parameter association relationships to associate geometric feature parameters, material parameters, and mechanical property parameters; optimize and analyze parameter association relationships to obtain the optimal parameter combination; and integrate the optimal parameter combination into a design parameter set.

[0123] Query the process knowledge base based on design parameters to obtain feasible process routes; evaluate each process route, considering factors such as processing accuracy, production efficiency and cost; select the optimal process route and generate the initial process flow.

[0124] Optimize the processing parameters for each process, including cutting speed, feed rate and cutting depth; calculate the processing time and processing cost of each process; generate process cards containing the processing parameters and operating instructions for each process; integrate the process flow and process cards to form an initial process plan.

[0125] This embodiment accurately obtains the key dimensional parameters of the brake disc by performing refined geometric feature extraction and analysis on the three-dimensional data of the automobile brake disc, providing a reliable basis for subsequent material selection and performance analysis. By analyzing the material properties based on the geometric feature parameters, it is possible to more accurately select suitable materials, thereby optimizing the performance of the brake disc. By establishing a finite element model for static and thermomechanical coupling analysis, the mechanical properties of the brake disc can be comprehensively evaluated to ensure its reliability and safety in actual use. By integrating and optimizing the geometric features, materials, and mechanical performance parameters, the optimal design parameter combination can be obtained, thereby improving the overall performance of the brake disc. By using a preset process knowledge base for process matching and optimization, it is possible to quickly generate a suitable process plan and improve production efficiency.

[0126] In one embodiment, the initial process plan is simulated to obtain simulation optimization parameters, including:

[0127] Quantify each process step, process conditions, equipment parameters, etc. in the process flow to form a series of quantifiable parameters, such as temperature, pressure, time, etc. These parameters constitute the process flow parameter set and provide basic data for subsequent simulation.

[0128] Based on the process parameter set, a simulation is constructed to create a corresponding process simulation scenario. Using simulation software and the data in the process parameter set, a virtual production environment and process are constructed. The process parameters are set for the process simulation scenario to create an initial simulation configuration. During this step, the various parameters in the simulation scenario are initialized based on actual production experience and process requirements to ensure that the simulation environment is as close to actual production conditions as possible.

[0129] Iterate the initial simulation configuration to generate multiple sets of simulation results. By running the simulation program multiple times, slightly adjusting some parameters each time, we obtain a series of simulation results under different conditions. These results include key indicators such as product performance and production efficiency under different parameter combinations.

[0130] Build performance indicator distribution graphs for multiple sets of simulation results. Visualize multiple sets of simulation results to generate distribution graphs for each performance indicator. These graphs intuitively demonstrate the impact of different parameter combinations on product performance and help analyze the optimal parameter range.

[0131] Based on the performance indicator distribution graph, the parameters of the initial simulation configuration are updated to obtain an optimized simulation configuration. By analyzing the performance indicator distribution graph, key parameters affecting product performance and their optimal value ranges are identified. Based on these analysis results, the relevant parameters in the initial simulation configuration are adjusted and optimized to form a new optimized simulation configuration.

[0132] Perform a validation simulation on the optimized simulation configuration to obtain verification results. Simulate again using the optimized parameter configuration to verify the optimization effect. The verification results include key data such as product performance indicators and production efficiency, which are used to evaluate the effectiveness of the optimization.

[0133] Based on the validation results, key process parameters are extracted from the optimized simulation configuration to obtain the simulation optimization parameters. Analyze the validation results to identify the key parameters that most significantly impact product performance and production efficiency. These key parameters are then referred to as simulation optimization parameters and serve as an important basis for optimizing the process plan.

[0134] This embodiment simulates the initial process plan and can optimize the process parameters before actual production, greatly improving the efficiency and accuracy of process optimization. Through parametric description and simulation scenario construction, accurate simulation of complex process flows is achieved, providing a reliable data basis for subsequent optimization. The acquisition of multiple sets of simulation result data and the construction of indicator graphs make the relationship between process parameters and product performance more intuitive and clear, which helps to quickly identify key parameters. Through iterative optimization and verification simulation, the reliability and practicality of the optimization results are ensured. The simulation optimization parameters finally extracted provide a scientific basis for the adjustment of the actual production process, effectively improving the production quality and efficiency of automotive parts, while reducing trial and error costs and resource waste. This simulation-based optimization method is characterized by high efficiency, accuracy, and low cost, and is of great significance to improving the overall level of automotive parts production technology.

[0135] In one embodiment, the measured process parameters in the production process are obtained, and the measured process parameters are compared and analyzed with the simulation optimization parameters to obtain error parameters. Based on the error parameters, a link analysis is performed to obtain a list of links to be optimized, including:

[0136] Multiple sensors are deployed throughout the automotive parts production line to collect real-time process parameter data. These sensors, including temperature sensors, pressure sensors, and displacement sensors, can collect key process parameters such as mold temperature, injection pressure, and cooling time. The real-time data collected by the sensors is transmitted to the central processing unit via a data acquisition system.

[0137] The collected real-time process parameter data is constructed into a matrix to form the first matrix. The ideal process parameters obtained through simulation optimization are also constructed into a second matrix with the same structure. The rows of the two matrices represent different process parameters, and the columns represent different time points or production batches.

[0138] Compare the corresponding elements in the first and second matrices one by one, calculate the relative deviation, and generate a deviation matrix. Each element in the deviation matrix represents the degree of deviation between the actual parameter and the ideal parameter. Statistical analysis is performed on each row of the deviation matrix (i.e., each process parameter), calculating the mean deviation and standard deviation. The mean deviation reflects the overall deviation of the parameter, while the standard deviation reflects the fluctuation.

[0139] Based on the mean deviation and standard deviation, a comprehensive deviation index is calculated for each process parameter. The formula for calculating the comprehensive deviation index is: Comprehensive Deviation Index = a Mean Deviation + b Standard Deviation, where a and b are weight coefficients. The comprehensive deviation indices of all process parameters are combined into a parameter deviation vector and sorted from highest to lowest deviation to create a deviation ranking table.

[0140] Perform multi-level screening on the deviation ranking table to identify process links requiring optimization. Set an initial preset threshold α and a secondary threshold β (β < α). The specific values ​​of α and β can be determined based on production requirements, such as α = 5% and β = 3%. Process links with a comprehensive deviation index greater than α are marked as first-level links for optimization; process links with a comprehensive deviation index between β and α are designated as second-level links for further analysis.

[0141] For the second process step, calculate the rate of change of the deviation value, that is, the magnitude of the change in the deviation value between adjacent time points or batches. Set a predetermined deviation threshold γ (e.g., γ = 2%) and mark the second process step with a rate of change exceeding γ as a secondary optimization step. This step aims to identify process steps with large fluctuations despite a small overall deviation.

[0142] Conduct a correlation matrix analysis on the primary and secondary links to be optimized. The correlation matrix reflects the degree of mutual influence between different process links. Set a correlation threshold δ (e.g., δ = 0.7) to identify process links with a correlation greater than δ with the link to be optimized. Deviance values ​​are determined for these linked process links, and links with deviation values ​​exceeding a secondary threshold β are marked as tertiary links to be optimized.

[0143] The first, second, and third level optimization links are prioritized and combined to create a final list of links to be optimized. The principle of prioritization is: first level > second level > third level. Within the same level, links are sorted by the size of the comprehensive deviation index.

[0144] This embodiment arranges multiple sensors to collect process parameters in real time, and performs matrix comparison analysis between the measured data and the simulation optimization parameters. This can comprehensively and accurately identify the deviation links in the production process, providing a reliable data basis for process optimization. The multi-level screening and dynamic threshold method are used to analyze the process links, which not only considers the deviation degree of a single parameter, but also takes into account the correlation and fluctuation between parameters, thereby achieving comprehensive identification and precise positioning of the links to be optimized. By setting different levels of links to be optimized and sorting them by priority, subsequent optimization work can be targeted and the optimization efficiency is improved. In addition, by introducing correlation matrix analysis, this method takes into account the mutual influence between process links, avoids the one-sidedness that may result from viewing a single parameter in isolation, and thus ensures the systematic and comprehensiveness of the optimization plan. This data-driven optimization method can adapt to different types of automotive parts production lines and has strong versatility and flexibility.

[0145] In one embodiment, the simulation optimization parameters are iteratively adjusted based on the list of links to be optimized to obtain optimized process parameters, including:

[0146] Perform parameter sensitivity analysis on the list of optimization steps. By varying the range of each parameter and observing its impact on the results, a sensitivity parameter list is generated. Based on this sensitivity parameter list, the simulation optimization parameters are graded into three levels: high, medium, and low, forming a graded parameter set.

[0147] An orthogonal experimental design is performed for each parameter in the hierarchical parameter set. An appropriate orthogonal table is selected and the levels of each parameter are determined to obtain an orthogonal experimental plan. Simulation calculations are performed based on this plan, and a simulation result data set is obtained through multiple simulations. Based on this data, parameter analysis of the hierarchical parameter set is performed using methods such as analysis of variance or regression analysis to obtain preliminary optimized parameters.

[0148] Parameter extraction is performed on the preliminary optimization parameters based on the pre-set threshold parameters, and the parameters that meet the threshold requirements are screened out to obtain the optimized extracted parameters. To verify the validity of these parameters, verification calculations are performed to obtain verification results.

[0149] According to the verification results, the optimized extraction parameters are fine-tuned and optimized, and multiple iterations are performed to obtain the optimized process parameters that meet the requirements.

[0150] This embodiment, by performing parameter sensitivity analysis on the optimization steps, can quickly identify key parameters that significantly impact the production process, effectively improving optimization efficiency. The use of parameter grading and orthogonal experimental design significantly reduces the number of simulation calculations, saving time and computing resources. Multiple iterations and verification calculations ensure the accuracy and reliability of the optimization results. The introduction of preset threshold parameters and verification calculations effectively avoids the risk of optimization results deviating from actual production requirements.

[0151] In one embodiment, a three-dimensional sample model is constructed based on the optimized process parameters, and a quality analysis is performed on the three-dimensional sample model to obtain a quality assessment result, including:

[0152] The optimized process parameters are analyzed in three dimensions to extract key geometric feature parameters, such as size, shape, surface features, etc. Based on these geometric feature parameters, a three-dimensional sample model of the automotive parts is constructed using three-dimensional modeling software.

[0153] The constructed 3D sample model then undergoes meshing. Meshing is the process of discretizing a continuous geometric model into a finite number of elements, using elements such as tetrahedrons or hexahedrons. The density and quality of the mesh directly impact the accuracy of subsequent analysis. Once meshing is complete, the finite element model of the component is obtained.

[0154] When performing stress analysis on a finite element model, boundary load conditions must be set. Boundary conditions include constraints and loads. Constraints simulate the installation state of the accessory, while loads simulate the external forces the accessory will experience during use. Using finite element analysis software, stress distribution data for each component can be obtained. Based on this stress distribution data, the maximum stress value and location are calculated, and areas of stress concentration are identified, resulting in a stress assessment result.

[0155] Modal analysis is an important method for studying the dynamic characteristics of structures. Performing modal analysis on a three-dimensional sample model can reveal the natural frequencies and mode shapes of components. Natural frequencies reflect the stiffness characteristics of a structure, while mode shapes describe its deformation patterns at each natural frequency. Based on these natural frequency and mode shape data, modal participation factors are calculated to assess the contribution of each mode to the structure's dynamic response, ultimately yielding dynamic characteristics assessment results.

[0156] Thermal analysis primarily studies the thermal stress and thermal deformation of components under temperature fluctuations. By setting temperature boundary conditions and material thermophysical parameters, finite element analysis software is used to generate temperature distribution and thermal stress data. Based on this data, the maximum thermal deformation and its location are calculated, areas of thermal stress concentration are identified, and thermal performance evaluation results are obtained.

[0157] A comprehensive analysis of the stress, dynamic characteristics, and thermal performance evaluation results is performed. This comprehensive analysis considers the functional requirements and operating environment of the accessory, weighing the importance of various performance indicators. By employing a multi-objective decision-making approach, an overall quality assessment of the accessory is obtained.

[0158] This embodiment achieves a comprehensive evaluation of accessory performance by performing a three-dimensional analysis of the optimized process parameters and constructing a sample model, avoiding the deviations that may result from traditional methods that rely solely on experience or simplified models. The use of finite element analysis technology for stress, modal, and thermal analysis not only improves the accuracy of the evaluation, but also provides in-depth insights into the performance of accessories under various working conditions. In particular, by calculating key indicators such as maximum stress value, stress concentration area, modal participation factor, and thermal deformation, a clear direction and basis are provided for process optimization. The method of comprehensively analyzing multiple evaluation results ensures the comprehensiveness and objectivity of the quality assessment.

[0159] In one embodiment, a solution analysis is performed based on the quality assessment results combined with the optimized process parameters to obtain a corresponding production process optimization solution.

[0160] Based on the quality assessment results and combined with the optimization process parameters, a solution analysis is conducted to obtain the corresponding production process optimization solution.

[0161] The quality assessment results are converted into indicators to form an evaluation indicator set. The quality assessment results include data on various product performance indicators, production efficiency, costs, and other aspects. Through data standardization and normalization, these results are converted into comparable evaluation indicators to form an evaluation indicator set.

[0162] Weighted process parameters are assigned to the optimization process parameters based on the evaluation indicator set. These parameters include key parameters that affect production quality, such as temperature, pressure, and time. Weighted process parameters are then assigned to each process parameter based on its importance.

[0163] Multidimensional analysis of weighted process parameters is performed to obtain the parameter optimization range. Multidimensional statistical methods such as principal component analysis and cluster analysis are used to analyze the correlation and influence between parameters, determine the reasonable optimization interval of each parameter, and form the parameter optimization range.

[0164] The parameter optimization range is sampled using a pre-defined search method to obtain a set of potential optimization points. Pre-defined search methods include grid search, random search, or genetic algorithms. Systematic sampling is performed within the parameter optimization range to generate a series of potential optimization parameter combinations, which constitute the potential optimization point set.

[0165] The potential optimization point set is scored based on pre-set multi-objective evaluation criteria to produce a preliminary scoring result. The multi-objective evaluation criteria include product quality, production efficiency, cost control, and other aspects. Each point in the potential optimization point set is evaluated from multiple dimensions, and a comprehensive score is calculated to form the preliminary scoring result.

[0166] Based on the preliminary scoring results, a solution analysis is performed on the set of potential optimization points to obtain the optimization solution set. Based on the scoring results, the optimization points with higher scores are screened out for in-depth analysis, considering their actual feasibility and potential risks, and forming a series of possible optimization solutions to form the optimization solution set.

[0167] Perform parameter sensitivity analysis on the optimization solution set to obtain influencing factor information. Use the control variable method to analyze the impact of each parameter change on the solution effect, identify key influencing factors, and form influencing factor information.

[0168] Based on the influencing factors, the optimization solution set is optimized and screened to obtain a set of candidate process solutions. Based on the importance of the influencing factors, the optimization solutions are further adjusted and screened to retain the most promising solutions and form a set of candidate process solutions.

[0169] The set of candidate process solutions is screened based on pre-set process constraints to obtain a set of feasible process solutions. These constraints include equipment capacity limitations, raw material property requirements, and safety production regulations. The candidate solutions are checked for compliance, and solutions that do not meet the constraints are eliminated to obtain a set of feasible process solutions.

[0170] Each solution in the set of feasible process solutions is analyzed to obtain a solution score. The solution analysis includes technical feasibility assessment, economic benefit analysis, risk assessment, etc. Taking all factors into consideration, each solution is scored to obtain the final solution score.

[0171] The feasible process solution set is sorted according to the solution score, and the solution with the highest score is selected as the final production process optimization solution. All feasible solutions are sorted by score, and the solution with the highest score is selected as the final optimization solution to guide the improvement and optimization of the actual production process.

[0172] The present invention achieves accurate quantification and priority sorting of production process parameters by converting indicators and assigning weights to quality assessment results, providing a reliable data basis for subsequent optimization. The use of multidimensional analysis and preset search methods to determine the parameter optimization range and perform sampling greatly improves the comprehensiveness and efficiency of the optimization scheme. The introduction of multi-objective evaluation criteria and parameter sensitivity analysis ensures the balance and stability of the optimization scheme in multiple dimensions. The feasibility and optimality of the final optimization scheme are guaranteed by screening process constraints and ranking scheme scores. The entire optimization process is systematic and scientific, which can effectively improve the production quality of automotive parts, reduce costs, improve production efficiency, and bring significant economic benefits and competitive advantages to enterprises.

[0173] Reference Figure 2 As shown, the present invention further provides a production process optimization system for automobile parts, characterized in that the production process optimization method for automobile parts applied to any of the above items includes:

[0174] The acquisition module is used to obtain the design parameters of automotive parts, perform process analysis based on the design parameters, and obtain an initial process plan;

[0175] Analysis module, which is used to simulate the initial process plan and obtain simulation optimization parameters

[0176] The correlation module is used to obtain the measured process parameters in the production process, perform matrix comparison analysis on the measured process parameters and the simulation optimization parameters, obtain the deviation matrix, perform link analysis based on the deviation matrix, and obtain a list of links to be optimized;

[0177] The processing module is used to iteratively adjust the simulation optimization parameters according to the list of links to be optimized to obtain the optimized process parameters;

[0178] A control module, which constructs a three-dimensional sample model based on optimized process parameters, performs quality analysis on the three-dimensional sample model, and obtains an evaluation result;

[0179] The execution module is used to perform solution analysis based on the evaluation results and the optimized process parameters to obtain the corresponding production process optimization solution.

[0180] The present invention provides a production process optimization system for automotive parts. By acquiring the design parameters of the automotive parts and performing process flow analysis, it comprehensively considers all aspects of the production process, thereby formulating a more systematic and comprehensive initial process plan, laying the foundation for subsequent optimization. The initial process plan is simulated to obtain simulation optimization parameters. Matrix comparison analysis is then performed on the measured process parameters during the actual production process to accurately identify the aspects that require optimization, avoiding the limitations of traditional empirical optimization methods and improving the accuracy and efficiency of optimization. Through iterative simulation adjustments and quality analysis of three-dimensional sample models, refined adjustment and verification of the optimized process parameters are achieved, ensuring the reliability and practicality of the optimization plan. This data- and model-based optimization method significantly improves the scientific nature and accuracy of automotive parts production process optimization. The resulting production process optimization plan comprehensively considers design parameters, simulation results, actual production data, and quality assessment results, effectively balancing multiple factors such as production efficiency, product quality, and cost control, providing a comprehensive and efficient optimization solution for automotive parts production.

[0181] Reference Figure 3 As shown, the present invention also provides a production process optimization device for automobile parts, comprising:

[0182] Memory, used to store programs;

[0183] The processor is used to execute the program to implement each step of any one of the above-mentioned methods for optimizing the production process of automobile parts.

[0184] In this embodiment, the processor and memory may be connected via a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0185] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0186] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0187] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the production process of automobile parts, characterized in that: include: Obtaining design parameters of the automobile brake disc, performing process analysis based on the design parameters, and obtaining an initial process plan; simulating the initial process plan to obtain simulation optimization parameters; Obtaining the measured process parameters during the production process, performing matrix comparison analysis on the measured process parameters and the simulation optimization parameters to obtain a deviation matrix, performing link analysis based on the deviation matrix to obtain a list of links to be optimized; Iteratively simulate and adjust the simulation optimization parameters according to the list of links to be optimized to obtain optimized process parameters; constructing a three-dimensional sample model based on the optimized process parameters, and performing quality analysis on the three-dimensional sample model to obtain a quality assessment result; Performing a solution analysis based on the quality assessment results and the optimized process parameters to obtain a corresponding production process optimization solution; The method of obtaining measured process parameters during the production process, comparing and analyzing the measured process parameters with the simulation optimization parameters to obtain error parameters, and performing link analysis based on the error parameters to obtain a list of links to be optimized includes: Collecting real-time data from sensors arranged on the parts production line to obtain the measured process parameters; Constructing a matrix of the measured process parameters to obtain a first matrix, and constructing a matrix of the simulation optimization parameters to obtain a second matrix, wherein the row and column structure of the first matrix is ​​the same as that of the second matrix; Comparing the relative deviations of the corresponding row and column elements in the first matrix and the second matrix one by one to obtain a deviation matrix; Performing statistical analysis on each row of the deviation matrix to obtain an average deviation value and a deviation standard deviation; Calculating a comprehensive deviation index based on the average deviation value and the deviation standard deviation to obtain a parameter deviation value vector; Sorting the parameter deviation value vectors to obtain a deviation degree ranking table; Based on the deviation degree ranking table, the process links are screened through multi-level screening and dynamic threshold method to obtain the list of links to be optimized, which specifically includes: Setting an initial preset threshold and a secondary threshold, wherein the secondary threshold is smaller than the initial preset threshold; Performing a first round of screening on the deviation degree ranking table according to the initial preset threshold value to obtain a first process link exceeding the initial preset threshold value and a second process link between the secondary threshold value and the initial preset threshold value; Mark the first process step as a first-level step to be optimized; Performing a secondary screening on the second process link, calculating the rate of change of the deviation value of the second process link, and if the rate of change exceeds a predetermined deviation threshold, marking the second process link exceeding the predetermined deviation threshold as a secondary process link to be optimized; Performing a correlation matrix analysis on the first-level links to be optimized and the second-level links to be optimized according to a preset correlation threshold to obtain corresponding associated process links, performing deviation value discrimination on the associated process links, and marking the associated process links whose deviation values ​​exceed the secondary threshold as third-level links to be optimized; The first-level links to be optimized, the second-level links to be optimized, and the third-level links to be optimized are sorted and integrated according to priority to obtain a list of links to be optimized.

2. The method for optimizing the production process of automobile parts according to claim 1, characterized in that: The step of obtaining design parameters of the automobile brake disc and performing process analysis based on the design parameters to obtain an initial process solution includes: Acquiring three-dimensional data of the automobile brake disc, performing geometric feature extraction on the three-dimensional data, and obtaining geometric feature parameters; Performing material property analysis on the automobile brake disc according to the geometric characteristic parameters to obtain material parameters; Based on the geometric characteristic parameters and the material parameters, a force analysis is performed on the automobile brake disc to obtain mechanical performance parameters; Integrating the geometric characteristic parameters, the material parameters and the mechanical property parameters to obtain the design parameters; Performing initial process matching on the design parameters through a preset process knowledge base to obtain an initial process flow; The processing parameters of the process steps of the initial process flow are set based on the design parameters to obtain the initial process plan.

3. The method for optimizing the production process of automobile parts according to claim 1, characterized in that: The simulating the initial process plan to obtain simulation optimization parameters includes: Performing parameterized description on the process flow in the initial process plan to obtain a process flow parameter set; Performing simulation construction according to the process flow parameter set to obtain a corresponding process simulation scenario, setting process parameters for the process simulation scenario to obtain an initial simulation configuration; Iteratively calculating the initial simulation configuration to obtain multiple sets of simulation result data; Constructing an index graph for the multiple sets of simulation result data to obtain a performance index distribution graph; updating parameters of the initial simulation configuration according to the performance indicator distribution graph to obtain an optimized simulation configuration; Performing a verification simulation on the optimized simulation configuration to obtain a verification result; Key process parameters are extracted from the optimized simulation configuration according to the verification results to obtain simulation optimization parameters.

4. The method for optimizing the production process of automobile parts according to claim 1, characterized in that: The iterative simulation adjustment of the simulation optimization parameters according to the list of links to be optimized to obtain the optimized process parameters includes: Performing parameter sensitivity analysis on the list of links to be optimized to obtain a list of sensitivity parameters; Classifying the simulation optimization parameters according to the sensitivity parameter list to obtain a graded parameter set; Performing an orthogonal experimental design on each parameter in the hierarchical parameter set to obtain an orthogonal experimental scheme; Perform simulation calculations according to the orthogonal test scheme to obtain a simulation result data set; Performing parameter analysis on the hierarchical parameter set according to the simulation result data set to obtain preliminary optimization parameters; Performing parameter extraction on the preliminary optimization parameters according to a preset threshold parameter to obtain optimized extraction parameters; Performing verification calculations based on the optimized extraction parameters to obtain verification results; The optimized extraction parameters are adjusted according to the verification results to obtain the optimized process parameters.

5. The method for optimizing the production process of automobile parts according to claim 1, characterized in that: The step of constructing a three-dimensional sample model based on the optimized process parameters and performing quality analysis on the three-dimensional sample model to obtain a quality assessment result includes: Performing a three-dimensional analysis on the optimized process parameters to obtain corresponding geometric characteristic parameters, and constructing a three-dimensional model based on the geometric characteristic parameters to obtain the three-dimensional sample model; Meshing the three-dimensional sample model to obtain a finite element model; Performing stress analysis on the finite element model based on preset boundary load conditions to obtain stress distribution data; Calculating the maximum stress value and the stress concentration area based on the stress distribution data to obtain a stress assessment result; Performing modal analysis on the three-dimensional sample model to obtain natural frequency and mode shape data; Calculating the modal participation factor based on the natural frequency and mode shape data to obtain a dynamic characteristics evaluation result; Performing thermal analysis on the three-dimensional sample model to obtain temperature distribution and thermal stress data; Calculate the maximum thermal deformation and the thermal stress concentration area based on the temperature distribution and thermal stress data to obtain a thermal performance evaluation result; The stress evaluation results, dynamic characteristics evaluation results and thermal performance evaluation results are comprehensively analyzed to obtain the quality evaluation results.

6. The method for optimizing the production process of automobile parts according to claim 1, characterized in that: The scheme analysis is performed based on the quality assessment results combined with the optimized process parameters to obtain the corresponding production process optimization scheme, Performing indicator conversion on the quality assessment result to obtain an assessment indicator set; Allocating weights to the optimized process parameters according to the evaluation index set to obtain weighted process parameters; Performing multidimensional analysis on the weighted process parameters to obtain parameter optimization ranges; Sampling the parameter optimization range by a preset search method to obtain a potential optimization point set; Scoring the potential optimization point set according to a preset multi-objective evaluation standard to obtain a preliminary scoring result; Performing a solution analysis on the potential optimization point set based on the preliminary scoring results to obtain an optimization solution set; Performing parameter sensitivity analysis on the optimization solution set to obtain influencing factor information; Performing scheme optimization screening on the optimization scheme set based on the information degree of the influencing factors to obtain a candidate process scheme set; Screening the candidate process solution set according to preset process constraints to obtain a feasible process solution set; Performing scheme analysis on each scheme in the set of feasible process schemes to obtain a scheme score; The feasible process solution set is sorted according to the solution scores, and the solution with the highest score is selected as the final production process optimization solution.

7. A production process optimization system for automobile parts, characterized in that: The production process optimization method for automobile parts applied to any one of claims 1 to 6 above comprises: An acquisition module, the acquisition module is used to obtain design parameters of the automotive parts, perform process analysis based on the design parameters, and obtain an initial process plan; Analysis module, which is used to simulate the initial process plan and obtain simulation optimization parameters An association module is used to obtain measured process parameters in the production process, perform matrix comparison analysis on the measured process parameters and the simulation optimization parameters to obtain a deviation matrix, perform link analysis based on the deviation matrix, and obtain a list of links to be optimized; A processing module, the processing module is used to iteratively simulate and adjust the simulation optimization parameters according to the list of links to be optimized to obtain optimized process parameters; a control module, wherein the control module constructs a three-dimensional sample model based on the optimized process parameters, performs quality analysis on the three-dimensional sample model, and obtains an evaluation result; An execution module, configured to perform a solution analysis based on the evaluation result and the optimized process parameters to obtain a corresponding production process optimization solution; The method of obtaining measured process parameters during the production process, comparing and analyzing the measured process parameters with the simulation optimization parameters to obtain error parameters, and performing link analysis based on the error parameters to obtain a list of links to be optimized includes: Collecting real-time data from sensors arranged on the parts production line to obtain the measured process parameters; Constructing a matrix of the measured process parameters to obtain a first matrix, and constructing a matrix of the simulation optimization parameters to obtain a second matrix, wherein the row and column structure of the first matrix is ​​the same as that of the second matrix; Comparing the relative deviations of the corresponding row and column elements in the first matrix and the second matrix one by one to obtain a deviation matrix; Performing statistical analysis on each row of the deviation matrix to obtain an average deviation value and a deviation standard deviation; Calculating a comprehensive deviation index based on the average deviation value and the deviation standard deviation to obtain a parameter deviation value vector; Sorting the parameter deviation value vectors to obtain a deviation degree ranking table; Based on the deviation degree ranking table, the process links are screened through multi-level screening and dynamic threshold method to obtain the list of links to be optimized, which specifically includes: Setting an initial preset threshold and a secondary threshold, wherein the secondary threshold is smaller than the initial preset threshold; Performing a first round of screening on the deviation degree ranking table according to the initial preset threshold value to obtain a first process link exceeding the initial preset threshold value and a second process link between the secondary threshold value and the initial preset threshold value; Mark the first process step as a first-level step to be optimized; Performing a secondary screening on the second process link, calculating the rate of change of the deviation value of the second process link, and if the rate of change exceeds a predetermined deviation threshold, marking the second process link exceeding the predetermined deviation threshold as a secondary process link to be optimized; Performing a correlation matrix analysis on the first-level links to be optimized and the second-level links to be optimized according to a preset correlation threshold to obtain corresponding associated process links, performing deviation value discrimination on the associated process links, and marking the associated process links whose deviation values ​​exceed the secondary threshold as third-level links to be optimized; The first-level links to be optimized, the second-level links to be optimized, and the third-level links to be optimized are sorted and integrated according to priority to obtain a list of links to be optimized.

8. A production process optimization device for automobile parts, characterized in that: include: Memory, used to store programs; A processor is used to execute the program to implement each step of the production process optimization method for automobile parts according to any one of claims 1 to 6.

9. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 6.

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