Stamping process optimization method for seat backrest connecting bracket

Abstract

The invention discloses a stamping process optimization method for a seat backrest connecting bracket, and relates to the technical field of stamping process optimization, and the method comprises the steps: collecting real-time heat treatment process data in a bracket heat treatment process, combining with environment condition data, and predicting a corresponding microstructure by using a trained phase change and grain growth coupling model; obtaining structure prediction data; the method comprises the following steps: predicting performance data of a stent according to heat treatment process parameters and microstructure characteristics of a material, generating a performance value according to the performance data, if the performance value does not exceed a performance threshold value, optimizing the current heat treatment process parameters by adopting a pre-trained genetic algorithm, and executing by an automatic control module of heat treatment equipment. Microstructure characteristics are predicted in real time, and heat treatment process parameters are iteratively optimized when the deviation degree of the microstructure characteristics exceeds the expectation; historical optimization schemes and experience are called at any time, the response speed of production decision making is increased, and it is ensured that the heat treatment process is always in the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of stamping process optimization, and specifically to a method for optimizing the stamping process of a seat back connection bracket. Background Art

[0002] With the rapid development of the automotive industry, higher requirements have been put forward for the design and manufacturing of seat components, especially seat back connection brackets. As a key load-bearing component in the seat system, the performance of the seat connection bracket directly affects the comfort, safety of passengers and the durability of the seat. Therefore, how to ensure the high strength, good toughness and reliable long-term use performance of the bracket through precise processing technology has become an urgent problem to be solved in the manufacturing process.

[0003] Traditional stamping processes often rely on experience and usually do not monitor and intervene in environmental factors during the processing in real time, which makes the product quality greatly affected by environmental changes. Especially during the stamping process, factors such as environmental temperature and humidity, and dust in the air may pose potential risks to the forming accuracy and product quality. In the heat treatment process, the changes in the microstructure and properties of materials are also significantly affected by process parameters such as temperature, heating time and cooling rate. Therefore, combining real-time environmental monitoring, microstructure prediction and process optimization has become a necessary means to improve the manufacturing quality of seat connection brackets.

[0004] In the Chinese invention patent with the authorization announcement number of CN109332482B, an optimized stamping forming process method for accumulator diaphragm is disclosed, including the following steps: optimization of stamping blanks to improve the flatness of blanks; optimization of stamping processes, forming in two steps; determination of reasonable die springback values. Before machining parts, reasonably allocate the bending angles of parts in two stages of blanking - preforming and final forming. Before forming, a finite element analysis tool can be used for simulation calculation to obtain the allocation value. Then, conduct die tryout verification, evaluate the test results, and appropriately correct the die size. Through a series of iterative analyses, the structural parameters of the die can be finally determined, mainly the springback angle value of bending. By correcting the springback, the final dimensions of the diaphragm parts can be ensured.

[0005] During the heat treatment process of the bracket, especially in key process stages such as quenching and tempering, different heat treatment parameters, such as heating temperature, heating time, cooling rate, etc., will significantly affect the microstructural changes of the material. Specifically, during the quenching process, the phase transformation, grain growth, and formation of martensite in the material at different temperatures will directly affect the strength and hardness of the material, while the tempering process optimizes the toughness and internal stress distribution of the material by controlling the combination of temperature and time. However, due to the complex changes in the microstructure of the material caused by the changes in these parameters, it often leads to large fluctuations in the mechanical properties of the final product. The existing technical methods cannot effectively achieve precise control of these heat treatment processes, especially cannot adjust the heat treatment parameters in real time in combination with environmental conditions, resulting in the mechanical properties of the product unable to stably reach the expected value.

[0006] To this end, the present invention provides an optimization method for the stamping process of the seat back connection bracket. Summary of the Invention

[0007] (1) Technical problems to be solved

[0008] Aiming at the deficiencies of the prior art, the present invention provides an optimization method for the stamping process of the seat back connection bracket. By collecting real-time heat treatment process data during the heat treatment process of the bracket and combining it with environmental condition data, using the trained coupling model of phase transformation and grain growth to predict the corresponding microstructure, and obtaining structure prediction data; predicting the performance data of the bracket based on the heat treatment process parameters and the microstructural characteristics of the material, generating a performance value from the performance data. If the performance value does not exceed the performance threshold, a pre-trained genetic algorithm is used to optimize the current heat treatment process parameters, and the automatic control module of the heat treatment equipment is used to execute, predicting the microstructural characteristics in real time, and iteratively optimizing the heat treatment process parameters when its deviation degree exceeds the expectation; retrieving historical optimization plans and experiences at any time to improve the response speed of production decisions and ensure that the heat treatment process is always in the optimal state; thus solving the technical problems recorded in the background art.

[0009] (2) Technical solutions

[0010] To achieve the above objectives, the present invention is realized through the following technical solutions: An optimization method for the stamping process of the seat back connection bracket, including, after real-time monitoring of the environmental condition data in the processing area, performing anomaly detection, constructing an environmental anomaly degree Aop from the obtained anomaly detection data. If the environmental anomaly degree Aop exceeds the expectation, a monitoring instruction is sent to the outside;

[0011] After collecting the real-time heat treatment process data during the heat treatment process of the bracket and combining it with the environmental condition data, using the trained coupling model of phase transformation and grain growth to predict the corresponding microstructure, and obtaining structure prediction data;

[0012] Predict the performance data of the bracket based on the heat treatment process parameters and the microstructural characteristics of the material, generate the performance value Pot from the performance data, and send a control optimization instruction to the outside if the performance value Pot does not exceed the performance threshold.

[0013] Optimize the current heat treatment process parameters using a pre-trained genetic algorithm, and execute it by the automatic control module of the heat treatment equipment. Real-time predict the microstructural characteristics, and iteratively optimize the heat treatment process parameters when their deviation degree exceeds the expectation.

[0014] Conduct performance tests on the processed bracket, and use the test data as feedback to output process optimization suggestions from the bracket heat treatment process optimization knowledge graph.

[0015] Furthermore, the sensor network monitors the changes in environmental conditions in the processing area in real time and obtains the corresponding environmental condition data; using the environmental condition data as input, perform anomaly detection using the trained anomaly detection model and obtain the corresponding anomaly detection data.

[0016] Furthermore, construct the environmental anomaly degree Aop from the anomaly detection data, and the method is as follows:

[0017]

[0018] In the formula: Ω is the integration region, φ(x, y, t) is the anomaly distribution function, is the partial derivative of the anomaly distribution function with respect to time; α is the diffusion coefficient.

[0019] Furthermore, monitor the temperature change data during the heating and cooling processes in real time. After receiving the monitoring instruction, use the online X-ray diffraction equipment to monitor and collect the grain size and phase transformation microstructure changes of the material during the heat treatment process in real time, and obtain the real-time heat treatment process data.

[0020] Furthermore, collect the microstructural characteristic data of the material under different heat treatment parameters, combine it with the real-time heat treatment process data and the environmental condition data in the processing area, and obtain the labeled sample data after preprocessing and labeling.

[0021] Furthermore, using the heat treatment process parameters and the microstructural characteristics of the material as input, predict the mechanical properties of the material using the trained mechanical property prediction model, obtain the corresponding mechanical property data, summarize them to generate a performance characteristic data set, and generate the performance value Pot from the performance characteristic data in the performance characteristic data set.

[0022] Furthermore, after receiving the control optimization instruction, taking improving the performance value as the optimization goal, optimize the current heat treatment process parameters using a pre-trained genetic algorithm to obtain the optimized heat treatment process parameters.

[0023] Use finite element analysis to simulate and test the optimized heat treatment process parameters, and obtain the corresponding simulation test data.

[0024] Furthermore, compare the simulation prediction results with the design target values. If they are inconsistent with the expectations, re-optimize; if they are consistent with the expectations, the automatic control module of the heat treatment equipment will execute the optimized heat treatment process parameters.

[0025] Furthermore, compare the microstructural features predicted in real time with the preset target values, obtain the deviation degree of each feature from the target value, and generate the deviation degree Pok based on the deviation degree. If the obtained deviation degree Pok exceeds the deviation threshold, re-call the genetic algorithm for optimization based on the deviation degree and the current heat treatment parameters, generate new process parameters and execute them, or send an alarm instruction to the outside.

[0026] Furthermore, conduct performance testing on the connecting bracket after heat treatment, and summarize the obtained test data as sample test data; integrate the sample test data, environmental condition data, heat treatment process monitoring data, process parameters, etc., and perform feature extraction to obtain the corresponding optimization features; take the optimization of bracket heat treatment as the target word to obtain the knowledge graph for the optimization of the bracket heat treatment process.

[0027] (III) Beneficial Effects

[0028] The present invention provides a method for optimizing the stamping process of a seat back connecting bracket, having the following beneficial effects:

[0029] 1. Continuously monitoring environmental changes can detect potential environmental problems, effectively prevent adverse effects of environmental anomalies on the processing process, and improve the stability of the production process; ensuring that the processing process is always within the predetermined environmental condition range can improve the consistency of product quality and also avoid quality fluctuations caused by abnormal environmental fluctuations.

[0030] 2. Use machine learning algorithms to predict the microstructure during the heat treatment process. The accurate prediction of the microstructure helps to optimize the heat treatment plan, ensure that factors such as grain size and phase composition are within the optimal range, and thus optimize the mechanical properties of the product.

[0031] 3. The prediction results provide a reliable basis for process optimization, ensuring that the material reaches the ideal mechanical properties under different heat treatment conditions; through real-time prediction of microstructural features, personalized heat treatment plans can be customized according to specific material and product requirements.

[0032] 4. When the predicted performance value Pot does not meet the preset standard, the dynamic adjustment of the heat treatment process enables the process parameters to flexibly adapt to environmental changes and reduce performance fluctuations caused by external changes; through continuous monitoring of the performance value Pot and timely adjustment of the heat treatment process, it can be ensured that the various performance indicators of the product are always within the ideal range, thereby reducing the production of unqualified products and improving production efficiency and product qualification rate.

[0033] 5. The process parameters are optimized through genetic algorithms to determine the optimal parameter combination in the heat treatment process (such as temperature, time, cooling rate, etc.), which improves the optimization efficiency of the heat treatment process, ensures that the optimized parameters can achieve the expected results in actual production, and reduces production risks.

[0034] 6. By comparing the microstructure characteristics with the preset target values, any deviations in the process can be monitored in real time and automatically adjusted to ensure that the production process always remains on the predetermined optimal path, thereby improving the adaptability and flexibility of the process; the process parameters are continuously optimized according to the results of each deviation adjustment to form a closed-loop improvement mechanism, which can improve the stability and consistency of the production process, reduce the production of defective products caused by production fluctuations, and ultimately improve overall production efficiency.

[0035] 7. Automatically generate process optimization suggestions based on optimization features, call up historical optimization plans and experiences at any time, and improve the response speed of production decisions; through automatically generated process optimization suggestions, process optimization can be quickly executed to ensure that the heat treatment process is always in the optimal state, thereby improving the flexibility and adaptability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the process flow of the stamping process optimization method of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 The present invention provides a method for optimizing the stamping process of a seat back connecting bracket, comprising:

[0039] Step 1: Real-time monitoring of environmental condition data in the processing area is performed to detect anomalies, and the environmental anomaly degree Aop is constructed from the acquired anomaly detection data. If the environmental anomaly degree Aop exceeds expectations, a monitoring instruction is issued to the outside;

[0040] Step 1 includes the following contents:

[0041] Step 101: After determining the stamping processing area of the bracket, arrange a sensor network in the processing area, such as temperature sensors, humidity sensors, dust sensors, etc. The sensor network monitors the changes in environmental conditions in real time in the processing area and obtains corresponding environmental condition data, such as temperature, humidity, and the amount of dust in the air, etc.;

[0042] Train a machine learning algorithm with the labeled sample data to obtain a trained anomaly detection model; use the trained anomaly detection model for anomaly detection with the environmental condition data as input, and obtain corresponding anomaly detection data, such as anomaly areas, anomaly times, and anomaly degrees, etc.;

[0043] In use, by arranging devices such as temperature sensors, humidity sensors, and dust sensors, comprehensive and real-time environmental monitoring can be achieved. Continuously monitoring environmental changes can discover potential environmental problems, effectively prevent adverse effects of environmental anomalies on the processing process, improve the stability of the production process, help identify the impact of environmental anomalies on processing, and can also effectively prevent damage to production equipment caused by these anomalies, especially in the precision machining process;

[0044] Step 102: Under dimensionless conditions, construct the environmental anomaly degree Aop from the anomaly detection data in the following way:

[0045]

[0046] In the formula: Ω is the integration region, φ(x,y,t) is the anomaly distribution function, which describes the environmental anomaly degree at the processing area (x,y) and time t, is the partial derivative of the anomaly distribution function with respect to time; α is the diffusion coefficient, with a value between 0 and 1, and are the second-order partial derivatives of the anomaly distribution function with respect to the spatial coordinates x and y respectively, which describe the spatial dispersion behavior of the anomaly degree; is the Laplace operator, which is used to describe the diffusion characteristics of the anomaly degree over the entire space;

[0047] According to historical data and management expectations for environmental conditions, preset an anomaly threshold; if the obtained environmental anomaly degree Aop exceeds the anomaly threshold, it indicates that the environmental anomaly degree in the processing area is relatively high, which may affect the subsequent processing process and targeted actions are required. At this time, send a monitoring instruction to the outside;

[0048] In use, combine the contents in Steps 101 and 102:

[0049] Real-time monitoring of environmental data changes can promptly detect environmental anomalies, avoid the negative impact of environmental anomalies on processing quality, improve product stability and consistency; ensuring that the processing process is always within the predetermined environmental condition range can improve the consistency of product quality and also avoid quality fluctuations caused by abnormal environmental fluctuations.

[0050] Step 2: After collecting the real-time heat treatment process data during the heat treatment of the bracket and combining it with the environmental condition data, use the trained coupled model of phase transformation and grain growth to predict the corresponding microstructures and obtain structure prediction data;

[0051] The said Step 2 includes the following contents:

[0052] Step 201: Install temperature sensors at key positions within the tempering and quenching areas of the bracket, such as in the heating zone and the cooling zone, to real-time monitor the temperature change data during the heating and cooling processes and record the time of each stage of heating, holding, and cooling;

[0053] After receiving the monitoring instruction, use an on-line X-ray diffraction device to real-time monitor and collect the phase changes of the material during the heat treatment process, such as the process of austenite transforming into ferrite and pearlite, monitor the microstructural changes of the material such as grain size and phase transformation, and obtain the real-time heat treatment process data;

[0054] During use, by real-time monitoring the heating and cooling temperature changes during the heat treatment process, ensure the precise control of the temperature in each stage of heating, holding, and cooling, ensure that the heat treatment process of the material is strictly carried out in accordance with the process requirements, and avoid the non-uniformity of the microstructure caused by temperature fluctuations during the heat treatment process. It can also achieve rapid adjustment during the heating and cooling processes, improve the adaptability of the heat treatment process, and thus enhance the quality and performance of the final product.

[0055] Step 202: Collect the microstructural characteristic data of the material under different heat treatment parameters (temperature, time, cooling rate), including grain size distribution, phase composition ratio, and martensite content, etc., combine it with the real-time heat treatment process data and the environmental condition data in the processing area, and obtain the labeled sample data after preprocessing and labeling;

[0056] Based on the phase transformation model (such as the ferrite-pearlite transformation model and the martensite transformation model) and the grain growth model, combine machine learning algorithms; after training with the labeled sample data, obtain the trained coupled model of phase transformation and grain growth; use the heat treatment process data as the input, and use the trained coupled model of phase transformation and grain growth to predict the corresponding microstructures and obtain structure prediction data;

[0057] For example, grain size: According to the Hall-Petch relationship, the grain size directly affects the yield strength of the material; phase composition: Different phases (such as pearlite, martensite, bainite, etc.) have different effects on hardness, strength, and toughness; martensite content: In steel, an increase in the martensite content usually increases hardness and strength; carbon content, distribution of alloying elements: The type and distribution of alloying elements also affect the mechanical properties of the material, which is particularly important in high-alloy steels;

[0058] During use, combine the content in steps 201 and 202:

[0059] Using machine learning algorithms to predict the microstructure during the heat treatment process and understanding in advance the impact of heat treatment on the microstructure of materials such as grain size and phase composition can effectively reduce the time and cost required for a large number of experimental verifications in traditional experiments; the accurate prediction of the microstructure helps to optimize the heat treatment plan, ensuring that factors such as grain size and phase composition are within the optimal range, and then optimizing the mechanical properties of the product, such as hardness, strength, and toughness, etc.

[0060] Step 3: Predict the performance data of the stent based on the heat treatment process parameters and the microstructural characteristics of the material, generate a performance value Pot from the performance data. If the performance value Pot does not exceed the performance threshold, send a control optimization instruction to the outside;

[0061] The said step 3 includes the following content:

[0062] Step 301: After combining theoretical relationship models such as the Hall-Petch relationship and the Lüders formula with a deep neural network, use the labeled experimental data and other relevant data for training and iterative optimization to obtain a trained mechanical property prediction model;

[0063] Using the heat treatment process parameters (temperature, time, cooling rate, etc.) and the microstructural characteristics of the material (grain size, phase composition, etc.) as inputs, use the trained mechanical property prediction model to predict the mechanical properties of the material, obtain the corresponding mechanical property data, such as strength, hardness, and toughness, etc., and generate a performance characteristic data set after summarization;

[0064] By combining classic theoretical models such as the Hall-Petch relationship and the Lüders formula with a deep neural network, high-precision prediction of the material properties (such as strength, hardness, toughness, etc.) under different heat treatment parameters is achieved. The prediction results provide a reliable basis for process optimization, ensuring that the material reaches the ideal mechanical properties under different heat treatment conditions; through the real-time prediction of microstructural characteristics (such as grain size, phase composition, etc.), personalized heat treatment plans can be customized according to the specific material and product requirements; maximize the mechanical properties of the product and ensure that the product meets strict quality and strength requirements;

[0065] Among them, according to the influence of grain refinement on the strength of materials, the Hall-Petch formula is used to describe the relationship between grain size and material strength:

[0066] σ y = σ 0 + k y d -1 / 2

[0067] Among them, σ y is the yield strength, σ 0 is the intrinsic shear strength of the material, k y is the Hall-Petch constant, and d is the grain diameter; through this relationship, the grain size can be related to the strength of the material;

[0068] For martensitic transformation, empirical formulas or theoretical models are usually used to describe the relationship between its hardness and phase transformation. The common Lüders formula can be used to predict martensite hardness:

[0069] H = H 0 + k M ·(V M - V 0 )

[0070] Among them, H is the hardness, H 0 is the matrix hardness, V M is the martensite content, k M is a constant, and V 0 is the initial martensite content. Through this formula, the hardness of the material after heat treatment can be predicted.

[0071] During the heat treatment process, the grain growth process has a significant impact on the mechanical properties of the material (such as strength and toughness). According to the classical JMAK (Johnson-Mehl-Avrami-Kolmogorov) model, the grain growth process can be described by the following equation:

[0072]

[0073] Among them, X is the degree of phase transformation, t is the time, τ is the characteristic time, and n is a constant representing the grain growth rate; this equation can help us quantitatively predict the grain size under different time and temperature conditions;

[0074] To establish the relationship between phase composition and toughness, an empirical formula in the following form is usually used:

[0075] K I C = f(C, α, T)

[0076] Among them, K IC is the fracture toughness of the material, C is the carbon content, α is the phase composition parameter (such as martensite volume fraction, pearlite volume fraction), and T is the temperature;

[0077] Step 302: Under dimensionless conditions, generate the performance value Pot from the performance characteristic data in the performance characteristic data set as follows:

[0078]

[0079] In the formula: T is the time range of performance evaluation, F(t) is the performance characteristic vector, which contains multiple performance characteristic data at time t and is defined as: f s (t) is the strength data at time t, f h (t) is the hardness data at time t, f v (t) is the toughness data at time t; W is the weight vector, where w s , w h , w v are the weights of strength, hardness, and toughness respectively, and w s + w h + w v = 1; K is the characteristic contribution matrix, where k ij represents the contribution degree of characteristic i to characteristic j, controlling the interaction effect between performance characteristics; exp(K·F(t)) is the non - linear transformation function, representing the interaction of performance characteristics. For a specific time t r this term will capture the non - linear coupling relationship between strength, hardness, and toughness;

[0080] According to historical data and the management expectation of the stent processing effect, preset the performance threshold in advance; if the obtained performance value Pot does not exceed the preset performance threshold, it indicates that if the current process parameters are continuously executed, the stent performance may be difficult to achieve the expected effect. Therefore, it is necessary to adjust the process parameters in real - time. At this time, send a control optimization instruction to the outside;

[0081] When in use, combine the content in steps 301 and 302:

[0082] Real - time feedback the relationship between process parameters and performance values. When the predicted performance value Pot does not reach the preset standard, dynamically adjust the heat treatment process. The real - time performance feedback and adjustment mechanism can cope with the uncertainties in complex production environments, enabling the process parameters to flexibly adapt to environmental changes and reducing performance fluctuations caused by external changes; through continuous monitoring of the performance value Pot and timely adjustment of the heat treatment process, it can ensure that all performance indicators of the product are always within the ideal range, thereby reducing the production of unqualified products and improving production efficiency and product qualification rate.

[0083] Step 4: Optimize the current heat treatment process parameters using a pre-trained genetic algorithm, and execute them by the automatic control module of the heat treatment equipment to predict the microstructure characteristics in real time. When the deviation degree exceeds the expectation, iterate and optimize the heat treatment process parameters;

[0084] The above Step 4 includes the following contents:

[0085] Step 401: After receiving the control optimization instruction, taking improving the performance value as the optimization goal, optimize the current heat treatment process parameters (temperature, time, cooling rate, etc.) using a pre-trained genetic algorithm to obtain the optimized heat treatment process parameters; use finite element analysis to conduct simulation tests on the optimized heat treatment process parameters, verify the influence of the optimized parameters on the material microstructure and mechanical properties, and obtain the corresponding simulation test data;

[0086] Optimizing the process parameters through the genetic algorithm to determine the optimal parameter combination (such as temperature, time, cooling rate, etc.) in the heat treatment process improves the optimization efficiency of the heat treatment process, realizes the optimal performance in a short time, and avoids repeated tests and time waste during manual adjustment; using finite element analysis to simulate the optimized process parameters can verify the influence of the optimization scheme on the microstructure and mechanical properties in advance, ensure that the optimized parameters can achieve the expected effect in actual production, and reduce the production risk.

[0087] Step 402: Compare the simulation prediction result with the design target value. If it is inconsistent with the expectation, re-optimize;

[0088] If it is consistent with the expectation, the automatic control module of the heat treatment equipment executes the optimized heat treatment process parameters, and adjusts the heating rate, holding time, cooling rate, etc. in real time to achieve real-time feedback control of the heat treatment process;

[0089] During use, it can avoid performance deviation caused by optimization errors, improve the reliability of process execution and the qualified rate of products. The optimized process parameters are adjusted in real time through the automatic control system, which speeds up the response speed of process parameter adjustment.

[0090] Step 403: Compare the microstructural characteristics predicted in real time (such as grain size, phase composition, etc.) with the preset target values, obtain the deviation degree of each characteristic from the target value, and generate the deviation degree Pok from the deviation degree, in the following manner:

[0091] Pok = ||W(t)·∑ -1 / 2 ·(F - T)|| p

[0092] Where: p = 2, F is the microstructure feature vector, which contains all the feature values ​​predicted in real time (such as grain size, phase composition ratio, martensite content, etc.); The target feature vector contains the preset target value of each feature, and ∑ is the feature covariance matrix, which captures the correlation between features and is defined as: ∑ ij = Cov(f i ,f j ); where Cov(f i ,f j ) represents the feature f i and f j The covariance of -1 / 2 is the inverse square root of the covariance matrix, which is used to normalize the correlation between features and eliminate the influence of feature dimensions; W(t)=diag(w 1 (t),w 2 (t),…,w n (t)) is a dynamic weight matrix, which represents the influence weight of each feature on the deviation degree, and the weight is dynamically adjusted over time;

[0093] Based on historical data and expectations of the processing performance of the bracket, a deviation threshold is pre-set; if the obtained deviation Pok exceeds the deviation threshold, based on the deviation and current heat treatment parameters, the genetic algorithm is re-called for optimization, and new process parameters are generated and executed, or an alarm instruction is issued to the outside;

[0094] When using, combine the contents in steps 401 to 403:

[0095] By comparing microstructural characteristics (such as grain size, phase composition, etc.) with preset target values, any deviations in the process can be monitored in real time and automatically adjusted to ensure that the production process always remains on the predetermined optimal path, thus improving the adaptability and flexibility of the process;

[0096] Continuously optimizing process parameters based on the results of each deviation adjustment to form a closed-loop improvement mechanism can improve the stability and consistency of the production process, reduce the production of defective products due to production fluctuations, and ultimately improve overall production efficiency.

[0097] Step 5: Perform a performance test on the processed stent, use the test data as feedback, and output process optimization suggestions from the stent heat treatment process optimization knowledge graph;

[0098] The step five includes the following contents:

[0099] Step 501: After completing the heat treatment of the bracket in the current stage, perform performance tests on the heat-treated connecting bracket, including tensile test, hardness test, impact toughness test, microstructure detection, surface quality detection, etc. Aggregate the obtained test data as sample test data; integrate the sample test data, environmental condition data, heat treatment process monitoring data, process parameters, etc., and then perform feature extraction to obtain corresponding optimization features.

[0100] During use, by integrating and extracting features from the test data, heat treatment process monitoring data, environmental data, etc., reference data is provided. Data integration can not only improve the accuracy of process optimization but also quickly identify the key factors affecting performance.

[0101] Step 502: Using the optimization of bracket heat treatment as the target word, after in-depth retrieval and entity relationship construction, obtain the knowledge graph for the optimization of the bracket heat treatment process; use the optimization features as input, and based on the correspondence between the optimization features and process optimization suggestions, output process optimization suggestions from the knowledge graph for the optimization of the bracket heat treatment process, and execute the process optimization suggestions to optimize the current heat treatment process to obtain the optimized process.

[0102] During use, combine the content in Steps 501 and 502:

[0103] Automatically generate process optimization suggestions based on the optimization features, retrieve historical optimization plans and experiences at any time, and improve the response speed of production decisions; through the automatically generated process optimization suggestions, the process optimization can be quickly executed to ensure that the heat treatment process is always in the optimal state, improving the flexibility and adaptability of the production process.

[0104] It should be noted that: The construction method of the knowledge graph for the optimization of the bracket heat treatment process takes data-driven and knowledge fusion as the core, and realizes the systematic modeling and semantic expression of the heat treatment process by integrating multi-source heterogeneous data (such as experimental data, literature materials, industry specifications, and production history records) and the process experience of domain experts.

[0105] First, use natural language processing (NLP) technology to extract key entities (such as quenching temperature, cooling rate, holding time, grain size, phase composition, etc.) and their associated relationships (such as causal relationships, parameter dependencies) from literature and technical documents. Secondly, perform feature extraction and association mining on experimental data, real-time monitoring data, and historical records through multivariate data analysis to supplement the potential connections between data. Then, use a knowledge graph construction tool (such as RDF or Neo4j) to model entities and relationships as nodes and edges, and assign semantic labels in the heat treatment process to form a structured graph database. Finally, combine graph algorithms (such as path search, centrality analysis, and graph embedding) and machine learning models to support intelligent reasoning, dynamic update, and precise generation of process optimization solutions, thereby forming an extensible and interactive knowledge system that provides a scientific basis and intelligent support for the design, optimization, and quality improvement of the stent heat treatment process.

[0106] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0107] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0108] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only for some logical function divisions, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

Claims

1. A method for optimizing the stamping process of a seat back connecting bracket, characterized in that: include, Real-time monitoring of environmental condition data in the processing area is performed to detect anomalies. The environmental anomaly degree Aop is constructed based on the acquired anomaly detection data. If the environmental anomaly degree Aop exceeds expectations, a monitoring instruction is issued to the outside. After collecting the real-time heat treatment process data of the bracket during heat treatment, combined with the environmental condition data, the trained phase change and grain growth coupling model is used to predict the corresponding microstructure to obtain the structure prediction data; The performance data of the stent is predicted based on the heat treatment process parameters and the microstructure characteristics of the material, and a performance value Pot is generated from the performance data. If the performance value Pot does not exceed the performance threshold, a control optimization instruction is issued to the outside; The current heat treatment process parameters are optimized using a pre-trained genetic algorithm, which is executed by the automatic control module of the heat treatment equipment to predict the microstructure characteristics in real time and iteratively optimize the heat treatment process parameters when the deviation exceeds expectations; The processed bracket is subjected to performance testing, and the test data is used as feedback, and the bracket heat treatment process optimization knowledge graph outputs process optimization suggestions.

2. The method for optimizing the stamping process of the seat back connecting bracket according to claim 1, characterized in that: The sensor network monitors the changes of environmental conditions in the processing area in real time and obtains the corresponding environmental condition data; Taking environmental condition data as input, the trained anomaly detection model is used to perform anomaly detection and obtain the corresponding anomaly detection data.

3. The method for optimizing the stamping process of the seat back connecting bracket according to claim 2, characterized in that: The environment anomaly degree Aop is constructed from anomaly detection data in the following way: Where: Ω is the integration area, φ(x,y,t) is the abnormal distribution function, is the partial derivative of the abnormal distribution function with respect to time; α is the diffusion coefficient.

4. The method for optimizing the stamping process of the seat back connecting bracket according to claim 3, characterized in that: Real-time monitoring of temperature change data during heating and cooling. After receiving the monitoring instructions, use online X-ray diffraction equipment to monitor and collect the grain size, phase change and microstructure changes of the material during the heat treatment process in real time to obtain real-time heat treatment process data.

5. The method for optimizing the stamping process of the seat back connecting bracket according to claim 4, characterized in that: The microstructure characteristic data of the material under different heat treatment parameters are collected, combined with the real-time heat treatment process data and the environmental condition data in the processing area, and the labeled sample data are obtained after preprocessing and labeling.

6. The method for optimizing the stamping process of the seat back connecting bracket according to claim 5, characterized in that: Taking heat treatment process parameters and material microstructure characteristics as input, the trained mechanical properties prediction model is used to predict the mechanical properties of the material. The corresponding mechanical properties data are obtained and summarized to generate a performance characteristic data set, and the performance value Pot is generated from the performance characteristic data in the performance characteristic data set.

7. The method for optimizing the stamping process of the seat back connecting bracket according to claim 6, characterized in that: After receiving the control optimization instruction, the pre-trained genetic algorithm is used to optimize the current heat treatment process parameters with the improvement of the performance value as the optimization target, and the optimized heat treatment process parameters are obtained; Finite element analysis is used to simulate and test the optimized heat treatment process parameters to obtain corresponding simulation test data.

8. The method for optimizing the stamping process of the seat back connecting bracket according to claim 7, characterized in that: The simulation prediction results are compared with the design target values. If they are inconsistent with the expectations, they are re-optimized; if they are consistent with the expectations, the automatic control module of the heat treatment equipment will execute the optimized heat treatment process parameters.

9. The method for optimizing the stamping process of the seat back connecting bracket according to claim 8, characterized in that: Compare the real-time predicted microstructure characteristics with the preset target values, obtain the degree of deviation between each characteristic and the target value, and generate the deviation degree Pok from the deviation degree; If the obtained deviation degree Pok exceeds the deviation threshold, based on the deviation degree and the current heat treatment parameters, the genetic algorithm is re-called for optimization, new process parameters are generated and executed, or an alarm instruction is issued to the outside.

10. The method for optimizing the stamping process of the seat back connecting bracket according to claim 9, characterized in that: Performing performance testing on the connection bracket after heat treatment, and summarizing the acquired test data as sample test data; Integrate sample test data, environmental condition data, heat treatment process monitoring data, process parameters, etc., and then perform feature extraction to obtain corresponding optimization features; Taking bracket heat treatment optimization as the target word, the knowledge graph of bracket heat treatment process optimization is obtained.

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