A method and system for controlling a process of manufacturing a chassis support

Abstract

The application relates to the technical field of manufacturing process control, and discloses a chassis support manufacturing process control method and system. Through the introduction of real-time sensing, representation and feedforward control mechanism of physical property data of workpiece individuals, and the feedback adjustment of geometric characteristics after machining, the problems of information islands and deviation accumulation in traditional manufacturing processes are effectively solved, and the quality stability, efficiency and yield of chassis support manufacturing are significantly improved.

Description

Technical Field

[0001] This application relates to the field of manufacturing process control technology, and more specifically, to a method and system for controlling the manufacturing process of a chassis bracket. Background Technology

[0002] In the field of high-end equipment manufacturing, especially for the production of critical components such as chassis supports, ensuring product quality and manufacturing efficiency is crucial. Traditional manufacturing processes often face challenges such as low equipment coordination efficiency, untimely adjustments to process parameters, and unstable product quality. These problems are particularly prominent on complex, multi-process production lines, where even minor initial deviations can accumulate and amplify in subsequent stages, ultimately leading to serious quality issues or even product scrap. Existing control systems often manage each process as an independent unit, lacking a mechanism to effectively transmit and interpret information about the actual state of the workpiece. This prevents downstream processes from adaptively adjusting to changes upstream, thus affecting the stability and yield of the entire manufacturing process.

[0003] In multi-stage manufacturing processes such as those for heavy engineering machinery chassis supports, deviations in the individual workpiece state (e.g., minor geometric deformations or uneven internal stresses) caused by slight fluctuations in raw materials or process disturbances in upstream processes can be transmitted as initial conditions and affect the processing results of downstream processes. This leads to the deviations being amplified step by step in the process chain, ultimately resulting in product scrap. For example, in the stamping process, slight fluctuations in the physical properties of the raw material, such as local thickness, local hardness, and internal microstructure uniformity, can cause minor geometric deformations in the stamped workpiece. In subsequent processes such as welding, heat treatment, and machining, the lack of feedforward control based on the distribution of individual physical properties of the workpiece prevents downstream processes from adaptively adjusting to the actual state of the workpiece. This causes the deviations to accumulate and amplify step by step, ultimately leading to dimensional errors, performance degradation, or even scrap. Existing process control methods typically monitor and control each process as an independent link, lacking an effective mechanism to transmit and interpret the actual state information of the workpiece between processes and dynamically adjust processing parameters based on this information to compensate for and correct deviations introduced from upstream.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a method and system for controlling the manufacturing process of chassis brackets, which aims to solve the technical problems of low equipment coordination efficiency, untimely adjustment of process parameters, unstable product quality, and the accumulation and amplification of individual workpiece state deviations generated in upstream processes in downstream processes, ultimately leading to product scrapping.

[0006] The technical solution of this application is as follows: In a first aspect, this application discloses a method for controlling the manufacturing process of a chassis bracket, including: Obtain the physical property data of the workpiece entering the processing equipment. The physical property data includes the local thickness, local hardness and internal structure uniformity data of the workpiece. The physical attribute data are integrated into a representational data that reflects the distribution of individual physical attributes of the workpiece; The characterization data is transmitted to the control unit of the processing equipment so that the control unit can determine the processing parameters of the processing equipment based on the characterization data. The processing equipment processes the workpiece according to the processing parameters; Geometric feature detection is performed on the processed workpiece to obtain geometric feature data; Based on the geometric feature data, the logic for determining the machining parameters is adjusted, and the adjusted logic is used to determine the machining parameters for subsequent batches of workpieces.

[0007] Secondly, this application also discloses a chassis bracket manufacturing process control system, which includes: The physical property data acquisition module is used to acquire the physical property data of the workpiece entering the processing equipment. The physical property data includes the local thickness, local hardness and internal structure uniformity data of the workpiece. The characterization data integration module is used to integrate physical attribute data into a characterization data that reflects the distribution of individual physical attributes of the workpiece, and transmit the characterization data to the control unit of the processing equipment. The parameter determination module, located in the control unit, is used to determine the processing parameters of the processing equipment based on the characterization data. The processing execution module is used to enable the processing equipment to process the workpiece according to the processing parameters; The geometric feature detection module is used to detect the geometric features of the processed workpiece and obtain geometric feature data. The parameter determination logic adjustment module is used to adjust the determination logic of machining parameters based on geometric feature data, and feeds back the adjusted determination logic to the parameter determination module for use in determining the machining parameters of subsequent batches of workpieces.

[0008] This application discloses a method and system for controlling the manufacturing process of a chassis support. By acquiring the physical attribute data of the workpiece entering the processing equipment and integrating it into a representational data reflecting the distribution of individual physical attributes of the workpiece, precise perception of the workpiece's initial state is achieved. This representational data is transmitted to the control unit of the processing equipment, enabling the control unit to determine processing parameters based on the actual individual characteristics of the workpiece, thereby realizing feedforward control of the processing parameters. After processing is completed, geometric feature detection is performed on the workpiece, and the logic for determining the processing parameters is adjusted based on the detected geometric feature data. This adjusted logic is then used to determine the processing parameters for subsequent batches of workpieces, forming a closed-loop feedback control. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating a chassis support manufacturing process control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a chassis support manufacturing process control system provided in an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0011] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0012] In the field of high-end equipment manufacturing, especially in the production of key components such as chassis supports, traditional manufacturing processes often face challenges such as low equipment coordination efficiency, untimely adjustment of process parameters, and unstable product quality. These problems are particularly prominent on complex, multi-process production lines. Even small initial deviations can accumulate and amplify in subsequent stages, ultimately leading to serious quality problems or even product scrap. Existing control systems often manage each process as an independent unit, lacking a mechanism to effectively transmit and interpret information about the actual state of the workpiece. This prevents downstream processes from adaptively adjusting to changes in upstream processes, thus affecting the stability and yield of the entire manufacturing process.

[0013] In this regard, refer to Figure 1 , Figure 1 This is a flowchart illustrating a chassis bracket manufacturing process control method according to an embodiment of the present invention, including: S11, Obtain the physical property data of the workpiece entering the processing equipment, the physical property data including the local thickness, local hardness and internal structure uniformity data of the workpiece; S12, integrate the physical attribute data into a set of characterization data reflecting the distribution of individual physical attributes of the workpiece; S13, the characterization data is transmitted to the control unit of the processing equipment, so that the control unit determines the processing parameters of the processing equipment based on the characterization data; S14, the processing equipment processes the workpiece according to the processing parameters; S15, Perform geometric feature detection on the processed workpiece to obtain geometric feature data; S16, Based on the geometric feature data, adjust the determination logic of the processing parameters, and use the adjusted determination logic to determine the processing parameters of subsequent batches of workpieces.

[0014] This application effectively solves the problems of information silos and deviation accumulation in traditional manufacturing processes by introducing a real-time perception, characterization and feedforward control mechanism for individual physical attribute data of workpieces, combined with geometric feature feedback adjustment after processing, and significantly improves the quality stability, efficiency and yield of chassis bracket manufacturing.

[0015] To better understand the chassis bracket manufacturing process control method proposed in this application, the key terms and implementation environment involved will be explained in detail below.

[0016] "Physical property data" refers to data describing the inherent characteristics of a workpiece material, such as local thickness, local hardness, and internal microstructure uniformity. This data is crucial for evaluating the workpiece's behavior during processing. Local thickness refers to the thickness value of the workpiece at different locations, which affects the material's resistance to deformation; local hardness reflects the material's ability to resist plastic deformation and has a significant impact on springback and cracking during stamping; internal microstructure uniformity data describes the distribution of microscopic features such as the material's grain structure and phase composition, directly related to the material's overall mechanical properties and processing stability.

[0017] "Characteristic data" refers to comprehensive data that reflects the distribution of individual physical attributes of a workpiece after processing and integrating the original physical attribute data. It is not merely a simple collection of original data, but an abstract description of the inherent correlation and spatial distribution characteristics of these data, providing a basis for the accurate determination of subsequent processing parameters.

[0018] "Processing equipment" refers to industrial equipment used for forming, connecting, heat treating, and other operations on chassis support workpieces, such as stamping presses, welding robots, heat treatment furnaces, or CNC machine tools.

[0019] The "control unit" is the core intelligent part of the processing equipment. It is responsible for receiving and processing various data, and determining and adjusting processing parameters according to preset logic or real-time algorithms to achieve precise control of the processing process.

[0020] "Processing parameters" refer to various process variables that need to be set and adjusted during processing, such as stamping pressure, holding time, die fine-tuning stroke, stamping speed, welding current, and heat treatment temperature profile. The precise control of these parameters directly affects the final quality and performance of the workpiece.

[0021] "Geometric feature data" refers to the macroscopic geometric information of a workpiece after machining, such as its shape, size, and surface roughness, obtained through inspection methods. This data is an important basis for evaluating machining quality and discovering potential defects.

[0022] "The logic for determining processing parameters" refers to the algorithm, model, or rule by which the control unit calculates and sets processing parameters based on input data (such as characterization data and geometric feature data). This logic can be an experience-based lookup table or a complex machine learning model.

[0023] The implementation environment of this application is typically an automated or semi-automated chassis support production line, which includes multiple processing stations connected in series. Each station is equipped with corresponding testing equipment and a programmable control unit, which can realize real-time data acquisition, transmission, processing and feedback control.

[0024] The core of the chassis support manufacturing process control method proposed in this application lies in the refined perception and feedforward control of the individual physical properties of the workpiece, as well as the feedback adjustment of the geometric features after processing.

[0025] First, before a workpiece enters the processing equipment, its physical property data needs to be acquired. For example, non-contact or contact sensors such as ultrasonic thickness gauges, portable hardness testers, or X-ray diffractometers can be used to measure the workpiece's local thickness, local hardness, and internal microstructure uniformity. These measurements can be performed in multiple key areas of the workpiece to obtain comprehensive information on the distribution of its physical properties. For instance, for a steel plate to be stamped, multiple thickness measurements can be taken at its edges, center, and areas prone to large deformation; simultaneously, a portable hardness tester can be used to sample and test the hardness of these areas; for internal microstructure uniformity, ultrasonic testing or eddy current testing can be used to assess the presence of inclusions, abnormal grains, and other defects within the material. This data can be entered manually or collected using automated testing equipment.

[0026] Subsequently, the acquired physical property data is integrated into a characterization dataset reflecting the distribution of individual physical properties of the workpiece. This integration process can be carried out in various ways. For example, thickness, hardness data, and internal microstructure uniformity data at different locations can be spatially mapped to generate a three-dimensional data model or a two-dimensional distribution map. Alternatively, statistical methods can be used to calculate the mean, standard deviation, skewness, and other statistical characteristics of these data, or data dimensionality reduction techniques such as principal component analysis (PCA) and independent component analysis (ICA) can be used to extract the most representative feature vectors. For instance, a matrix containing information such as thickness, hardness, and grain size distribution in various regions of the workpiece can be constructed; this matrix is ​​the characterization dataset.

[0027] Next, the characterization data is transmitted to the control unit of the processing equipment. The transmission method can be industrial Ethernet, a wireless communication module, or a fieldbus, etc. After receiving the characterization data, the control unit determines the processing parameters of the processing equipment based on this data. For example, if the characterization data shows that a certain area of ​​the workpiece is too thin and has too high a hardness, the control unit may adjust the stamping pressure, holding time, or die fine-tuning stroke accordingly to avoid cracking or excessive thinning of that area during stamping. The determination of processing parameters can be based on a preset lookup table, that is, directly matching the corresponding combination of processing parameters according to different characterization data ranges; or it can be based on a mathematical model, for example, by establishing a regression model or neural network model between physical property data and processing parameters to calculate the optimal processing parameters in real time.

[0028] Then, the processing equipment processes the workpiece according to the determined processing parameters. For example, in the stamping process, the stamping machine will stamp the workpiece into shape according to the stamping pressure, holding time, die fine-tuning stroke, and stamping speed set by the control unit. In the welding process, the welding robot will perform welding according to the set welding current, voltage, welding speed, and other parameters.

[0029] After machining, the workpiece undergoes geometric feature inspection to obtain geometric feature data. This can be achieved using high-precision inspection equipment such as coordinate measuring machines, laser scanners, and machine vision systems. For example, the dimensional accuracy, shape deviation, and surface flatness of stamped parts can be measured. This inspection data reflects the actual effect of the machining process and can be compared with design benchmarks to identify potential quality problems.

[0030] Finally, based on the geometric feature data, the logic for determining the machining parameters is adjusted, and this adjusted logic is used to determine the machining parameters for subsequent batches of workpieces. This step is crucial for achieving adaptive control. For example, if detection reveals that a batch of workpieces consistently exhibits slight springback in a specific area, the control unit analyzes the geometric feature data causing the springback and, combined with previous characterization data and machining parameters, optimizes the logic for determining the machining parameters. This optimization can involve adjusting parameter values ​​in a lookup table or updating weights or coefficients in the mathematical model. Through this feedback mechanism, the system can continuously learn and improve, making the determination of machining parameters for subsequent batches of workpieces more accurate and robust.

[0031] The chassis support manufacturing process control method proposed in this application forms a closed-loop adaptive control system by real-time sensing, characterization and feedforward control of the physical attribute data of individual workpieces, combined with feedback adjustment of geometric features after processing.

[0032] This application transmits characterization data to the control unit of the processing equipment, enabling the control unit to determine processing parameters based on this individualized data. For example, if the characterization data indicates that a certain area of ​​the workpiece has high hardness, the control unit can correspondingly increase the stamping pressure or extend the holding time in that area to ensure sufficient material deformation and reduce springback. This feedforward control mechanism allows the processing equipment to perform "tailor-made" processing according to the differences in the "material properties" of each workpiece, thereby effectively suppressing quality problems caused by fluctuations in raw material quality.

[0033] After the workpiece is machined, the system also performs geometric feature detection to acquire geometric feature data. This data reflects the actual effect of the machining process. For example, by acquiring the three-dimensional morphological data of the stamped part through a laser scanner and comparing it with the design model, any dimensional deviations, deformations, or surface defects can be accurately identified. Based on this geometric feature data, the system can adjust the logic for determining the machining parameters. This means that if a batch of workpieces is found to consistently exhibit slight deformation in a specific area, the system will analyze the geometric feature data causing the deformation and, combined with previous characterization data and machining parameters, optimize the algorithm or model for determining the machining parameters. This feedback adjustment mechanism allows the system to continuously learn and improve, thereby enhancing the machining accuracy and quality stability of subsequent batches of workpieces.

[0034] In summary, this application introduces a real-time sensing, characterization, and feedforward control mechanism for individual workpiece physical attribute data, combined with post-processing geometric feature feedback adjustment, forming a closed-loop adaptive control system. Compared to the limitations of traditional manufacturing processes lacking individualized data sensing and feedback adjustment, this application can significantly improve the quality stability, efficiency, and yield of chassis bracket manufacturing. This method can not only effectively compensate for and correct deviations introduced by upstream processes, but also dynamically optimize according to the actual situation in the production process, thereby ensuring that the manufacturing quality of key components such as chassis brackets reaches a higher standard.

[0035] This application further proposes a method for controlling the manufacturing process of a chassis bracket, wherein the step of transmitting characterization data to the control unit of the processing equipment, so that the control unit determines the processing parameters of the processing equipment based on the characterization data, further includes: The control unit receives frictional force and heat flow data collected by the transient surface interaction sensing array embedded in the key deformation area of ​​the stamping die; The control unit processes the friction force data and heat flow data to obtain the local friction coefficient and local heat transfer efficiency; The control unit integrates characterization data, local friction coefficient, and local heat transfer efficiency to generate a comprehensive state vector; Based on the comprehensive state vector, the control unit derives the adjustment amounts of stamping pressure, holding time, die fine-tuning stroke, and stamping speed to determine the processing parameters of the processing equipment; After processing is completed, the control unit receives the workpiece geometry and subsurface force distribution data detected by the online detection system for workpiece micro-defects and subsurface forces. The control unit compares the geometric topography and subsurface force distribution data with the design benchmark, calculates the deviation value, and quantifies the defects caused by surface interaction differences. Based on the deviation value and the quantified defect data, the control unit optimizes the logic for determining the adjustment amount of stamping pressure, holding time, die fine-tuning stroke, and stamping speed.

[0036] Specifically, the control unit receives frictional force and heat flow data collected by a transient surface interaction sensing array embedded in the critical deformation area of ​​the stamping die. The transient surface interaction sensing array can be understood as a sensor network integrated on the die surface, especially in critical areas where workpiece deformation is most severe or stress is most concentrated. This array can capture the dynamic frictional force and heat flow changes at the die-workpiece contact interface in real time with high precision, aiming to obtain the most direct and detailed surface interaction information during processing. For example, this array can be implemented using technologies such as piezoelectric sensors, thermocouple arrays, or thin-film sensors.

[0037] Furthermore, the control unit processes the received friction force and heat flow data to obtain the local friction coefficient and local heat transfer efficiency. This processing may involve data filtering, calibration, and calculations based on physical models, with the aim of transforming the raw sensor data into parameters with clear physical meaning, thereby more accurately reflecting the stress state and temperature changes of the workpiece in a local area.

[0038] Subsequently, the control unit fuses the aforementioned characterization data, local friction coefficient, and local heat transfer efficiency to generate a comprehensive state vector. This comprehensive state vector provides a complete and multi-dimensional description of the workpiece's state before, during, and after processing, aiming to provide a unified, comprehensive assessment that includes macroscopic physical properties, microscopic surface interactions, and potential defect risks. The fusion process can be implemented using weighted averaging, machine learning algorithms, or multi-sensor data fusion techniques to ensure effective integration of data from different sources.

[0039] Based on this, the control unit derives the adjustment amounts for stamping pressure, holding time, die fine-tuning stroke, and stamping speed according to the comprehensive state vector, in order to determine the processing parameters of the processing equipment. This means that the determination of processing parameters no longer depends solely on the initial physical properties of the workpiece, but incorporates real-time dynamic feedback during the processing, thereby enabling more precise adaptation to individual differences in workpieces and transient changes during the processing.

[0040] After processing, the control unit receives data on the workpiece's geometry and subsurface stress distribution detected by the online detection system for workpiece micro-defects and subsurface stress. This detection system can employ non-contact or contact technologies such as ultrasonic testing, eddy current testing, X-ray diffraction, or laser scanning. Its purpose is to obtain microstructural information about the workpiece's interior and subsurface, such as the presence of microcracks, pores, and residual stress distribution. This information is crucial for assessing the long-term reliability of the chassis support.

[0041] Next, the control unit compares the acquired geometric topography and subsurface stress distribution data with the design baseline, calculates the deviation value, and quantifies the defects caused by surface interaction differences. The design baseline typically refers to the product's CAD model, material performance standards, and quality control specifications. Through comparison, deviations in the workpiece's geometric dimensions and internal quality can be accurately identified, and defects caused by improper interaction between the mold and the workpiece surface (such as excessive local friction or heat concentration) can be quantified, such as surface scratches, uneven material stretching, or abnormal subsurface hardening layers.

[0042] Finally, based on the calculated deviation values ​​and quantitative characterization data of defects, the control unit optimizes the logic for determining the adjustment amounts of stamping pressure, holding time, die fine-tuning stroke, and stamping speed. This means the system can learn from actual processing results, analyze the type, location, and severity of defects, and reverse-correct parameters to determine the weights, thresholds, or algorithms in the model. This allows for more effective prediction and avoidance of similar defects in subsequent batches of workpieces, continuously improving processing accuracy and product quality.

[0043] This application's solution introduces a transient surface interactive sensing array in the key deformation area of ​​the stamping die to acquire frictional force and heat flow data in real time, processing them into local friction coefficients and local heat transfer efficiency. These real-time, microscopic dynamic data are fused with the initial physical property characterization data of the workpiece to form a comprehensive state vector. Because this comprehensive state vector contains multi-dimensional information about the workpiece before, during, and after processing, the control unit can more comprehensively and accurately understand the current state and potential deformation trends of the workpiece. Based on this, the control unit can derive more precise adjustments to stamping pressure, holding time, die fine-tuning stroke, and stamping speed, thereby achieving real-time, dynamic, and localized control of the workpiece's deformation behavior during processing, effectively avoiding localized stress concentration, uneven material flow, or microscopic damage caused by insufficient adjustment of macroscopic parameters.

[0044] Furthermore, by introducing an online detection system for workpiece micro-defects and subsurface stress after processing, geometric morphology and subsurface stress distribution data are acquired and compared with design benchmarks to quantify defects caused by surface interaction differences. This deep-level quality feedback mechanism enables the system not only to identify macroscopic geometric deviations but also to detect microstructural damage and subsurface stress distribution anomalies that are difficult to perceive with the naked eye. Based on these detailed deviation values ​​and defect quantification data, the control unit can iteratively optimize the logic for determining processing parameters. This optimization is a "learning" process based on actual processing results, which can specifically correct the parameter determination model, enabling it to more intelligently predict and prevent defects in subsequent batches of workpieces. This overcomes the limitations of traditional methods that rely solely on macroscopic geometric detection and cannot effectively address micro-defects and subsurface quality issues.

[0045] In some preferred embodiments, a specific example is given below. Suppose that during the manufacturing of a high-strength steel chassis bracket, conventional methods often result in localized material thinning and microcracks in specific bend areas during the stamping process. Using the method of this application, firstly, a transient surface interaction sensing array is embedded in the bend area of ​​the stamping die to monitor the frictional force and heat flow data in this area in real time during the stamping process. For example, when a sudden increase in frictional force and abnormally concentrated heat flow are detected in this area, the control unit immediately processes these data to obtain the local friction coefficient and local heat transfer efficiency. These data are then fused with characterization data such as the initial local thickness and local hardness of the workpiece to generate a comprehensive state vector.

[0046] Based on this comprehensive state vector, the control unit derives the processing parameters that need adjustment. For example, to avoid excessive local material stretching, the stamping speed in that area may be reduced, and the die fine-tuning stroke increased to optimize material flow, while the local cooling strategy is adjusted to control temperature. After processing, the online detection system for workpiece micro-defects and subsurface stress scans the corner area, detecting its geometry and subsurface stress distribution. If slight subsurface residual stress concentration or extremely fine crack initiation is found, the control unit compares these geometry and subsurface stress distribution data with the design baseline, calculates the deviation value, and quantifies these defects caused by surface interaction differences. Finally, based on these detailed deviation values ​​and defect quantification data, the control unit optimizes the logic for determining the adjustment amounts of stamping pressure, holding time, die fine-tuning stroke, and stamping speed. For example, the system may learn that under specific material and die conditions, when the local friction coefficient reaches a certain threshold, it is necessary to reduce the stamping speed more significantly or adjust the die fine-tuning stroke in advance to completely eliminate the risk of micro-defects. Through this continuous real-time feedback and logic optimization, the material thinning and micro-crack issues in the corner area of ​​the chassis brackets produced in subsequent batches will be effectively suppressed, thereby significantly improving the overall quality and reliability of the product.

[0047] In some of the embodiments described above in this application, although processing parameters are determined and optimized in real time by integrating the individual physical property distribution of the workpiece, the local friction coefficient, and the local heat transfer efficiency, in actual production, the characteristic fluctuations between batches of workpieces and changes in production environment parameters may still affect the stability of the processing process and product quality. For example, if the material hardness of a batch of workpieces is generally high, or the workshop ambient temperature rises significantly, relying solely on the real-time detection data of a single workpiece may not be able to fully capture and effectively address these systematic deviations, which may result in insufficient robustness in determining processing parameters, thereby affecting the final performance and consistency of the chassis support.

[0048] In response, this application further proposes a more comprehensive method for determining processing parameters, which enhances the adaptability and stability of the processing process by incorporating consideration of batch-to-batch characteristic fluctuations and changes in production environment parameters.

[0049] In this regard, this application further proposes that the step of the control unit deriving the adjustment amounts of stamping pressure, holding time, die fine-tuning stroke, and stamping speed based on the aforementioned comprehensive state vector, in order to determine the processing parameters of the aforementioned processing equipment, also includes: The control unit receives information on the fluctuation of characteristics between workpiece batches and information on changes in production environment parameters; The control unit determines whether the fluctuation information or the change information exceeds a preset range; When the fluctuation information or the change information exceeds the preset range, the control unit selects a parameter adjustment strategy based on the fluctuation pattern or change trend that exceeds the preset range; and the control unit derives the adjustment amount of the stamping pressure, the holding time, the mold fine-tuning stroke and the stamping speed based on the parameter adjustment strategy to determine the processing parameters of the processing equipment.

[0050] Specifically, the fluctuation information of workpiece batch characteristics can be understood as the statistical differences in physical properties of different batches of raw materials, such as the changes in the mean or variance of parameters like average thickness, hardness distribution, and internal structure uniformity between batches. The changes in production environment parameters refer to external environmental factors affecting the processing, such as workshop temperature, humidity, power supply voltage stability, and equipment vibration frequency. This information can be received by the control unit through a preset sensor network, historical production data analysis, or manual input. The preset range refers to the threshold for permissible fluctuations in workpiece characteristics and changes in environmental parameters under normal production conditions. This range can be set based on historical production data, material supplier specifications, or process expert experience. The control unit monitors the received fluctuation and change information in real time and compares it with the preset range to determine whether there are any anomalies or trend deviations.

[0051] In practical applications, when fluctuations or changes are detected exceeding the preset range, the control unit intelligently selects appropriate parameter adjustment strategies based on the specific pattern or trend of the deviation. For example, if the average hardness of a batch of workpieces is detected to be continuously increasing, the control unit may choose a strategy of "increasing stamping pressure and extending holding time"; if a significant increase in ambient temperature is detected, a strategy of "adjusting cooling strategy and slightly reducing stamping speed" may be selected. These parameter adjustment strategies can be pre-stored in the control unit's knowledge base and associated with specific fluctuation patterns or trends. Thus, based on the selected parameter adjustment strategy, the control unit derives adjustments to stamping pressure, holding time, die fine-tuning stroke, and stamping speed. These adjustments are correction values ​​for processing parameters under the current batch or environmental conditions, designed to counteract the adverse effects of external factors and ensure the stability of the processing and the consistency of product quality.

[0052] This application's solution effectively overcomes the limitations of relying solely on real-time data from a single workpiece for parameter adjustment by introducing a mechanism for receiving and judging information on fluctuations in characteristics between workpiece batches and changes in production environment parameters. When the control unit receives this macroscopic fluctuation or change information, it can promptly determine whether it exceeds a preset stability range. If it does, the control unit no longer relies solely on instantaneous individual data, but instead combines broader fluctuation patterns or trends to select a more macroscopic and systematic parameter adjustment strategy. This strategy selection mechanism allows the determination of processing parameters to not only respond to differences in individual workpieces but also proactively address systemic challenges brought about by batches or the environment. In this way, the adjustment of processing parameters can more comprehensively adapt to dynamic changes in the production process, thereby maintaining process stability and product quality consistency over a broader production cycle.

[0053] In some preferred embodiments, a specific example is given below. Assume that during the stamping process of the chassis bracket, the control unit continuously receives batch reports from the raw material supplier, containing data on the average yield strength and elongation of the new batch of steel plates. Simultaneously, temperature and humidity sensors within the workshop transmit environmental parameters to the control unit in real time. Specifically, when the control unit detects that the average yield strength of the new batch of steel plates is 5% higher than the upper limit of a preset range, this is considered a fluctuation exceeding the preset range. Similarly, if the workshop ambient temperature continues to rise and exceeds the preset upper limit of 25 degrees Celsius, this is also considered a change exceeding the preset range. At this point, the control unit identifies two fluctuation patterns and trends—"high-strength material batch" and "high-temperature environment"—based on a preset knowledge base. For these two situations, the control unit selects appropriate parameter adjustment strategies. For example, for high-strength materials, the strategy might include "appropriately increasing the stamping pressure to ensure sufficient deformation and slightly extending the holding time to stabilize the morphology"; for high-temperature environments, the strategy might include "adjusting the mold coolant flow rate to control the mold temperature and fine-tuning the stamping speed curve to avoid localized overheating." Based on these selected parameter adjustment strategies, the control unit derives specific adjustments to the stamping pressure, holding time, die fine-tuning stroke, and stamping speed. For example, the stamping pressure might be increased by 50 MPa, the holding time extended by 0.2 seconds, the die fine-tuning stroke increased by 0.1 mm, and the stamping speed reduced by 5% at a specific stage. These adjustments are then applied to the control unit of the processing equipment to ensure that the stamping quality of the chassis bracket remains within design requirements even when raw material properties and environmental conditions change. In this way, the solution of this application enables intelligent and adaptive control of the manufacturing process, effectively addressing external uncertainties.

[0054] In response, this application further proposes that the step of the control unit deriving the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the parameter adjustment strategy to determine the processing parameters of the processing equipment also includes: The control unit receives the comprehensive state vector indicating that the workpiece may have localized stress concentration or uneven material flow. The control unit identifies potential microstructural damage areas based on the combination of local hardness, local thickness, local friction coefficient, and local heat transfer efficiency reflected in the comprehensive state vector. The control unit instantly adjusts the local morphology of the mold based on the microstructural damage area to change the local force path and deformation rate of the workpiece. The control unit adjusts the stamping speed curve to reduce the local stamping speed during the critical deformation stage; The control unit adjusts the local cooling or heating strategy based on real-time heat flow data to control the temperature of the workpiece during the deformation process; and the control unit derives the adjustment amount of the stamping pressure, the holding time, the die fine-tuning stroke and the stamping speed based on the local shape adjustment of the mold, the stamping speed curve adjustment and the local cooling or heating strategy adjustment, so as to determine the processing parameters of the processing equipment.

[0055] Specifically, the control unit receives the comprehensive state vector indicating potential localized stress concentration or uneven material flow in the workpiece. This refers to the analysis of the comprehensive state vector generated by fusing frictional force and heat flow data collected from the transient surface interaction sensing array embedded in the critical deformation area of ​​the stamping die, along with data on the workpiece's local thickness, localized hardness, and internal microstructure uniformity. This analysis aims to identify specific patterns or outliers that suggest the workpiece may face risks of excessive localized stress or uneven material flow during the upcoming processing. For example, a machine learning model can be used to perform real-time pattern recognition on the comprehensive state vector to determine if features match known defect patterns.

[0056] The control unit identifies potential microstructural damage areas based on a combination of local hardness, local thickness, local friction coefficient, and local heat transfer efficiency reflected in the comprehensive state vector. This can be understood as the control unit using multi-parameter collaborative analysis to accurately locate risk areas. For example, when local hardness is too high, local thickness is too thin, local friction coefficient is abnormally increased, and local heat transfer efficiency is reduced, these combined characteristics may collectively indicate that the area is highly susceptible to microstructural damage such as microcracks, uneven grain deformation, or local necking during the stamping process.

[0057] In practical applications, the control unit instantly adjusts the local morphology of the mold based on the microstructural damage area to change the local force path and deformation rate of the workpiece. Specifically, the control unit drives a micro-actuator integrated inside the mold (such as a piezoelectric ceramic actuator or a micro-hydraulic actuator) to dynamically correct the local geometry of the mold-workpiece contact surface within milliseconds. The aim is to redistribute the stamping load by changing the contact conditions between the mold and the workpiece, guiding the material to flow in a more uniform direction, thereby avoiding stress concentration in specific areas and optimizing the material's deformation path and rate.

[0058] Furthermore, the control unit adjusts the stamping speed curve to reduce local stamping speed during critical deformation stages. This means that the control unit dynamically modifies the stamping speed configuration file of the stamping equipment based on the identified microstructural damage areas and their potential damage mechanisms. The purpose is to provide more sufficient deformation time for the material by reducing the stamping speed at critical deformation moments when the workpiece experiences maximum stress or strain, thereby reducing defects caused by strain rate sensitivity and effectively suppressing crack initiation and propagation.

[0059] Furthermore, the control unit adjusts local cooling or heating strategies based on real-time heat flow data to control the workpiece temperature during deformation. This means the control unit dynamically activates or adjusts local temperature control units inside or outside the mold using data acquired by a real-time heat flow sensor embedded in the mold. For example, it can activate local cooling nozzles in areas prone to overheating or perform local induction heating in areas requiring improved plasticity. The aim is to precisely control the temperature distribution of the workpiece during deformation, ensuring the material remains within the optimal plastic deformation temperature window and preventing thermal tearing due to excessively high temperatures or brittle fracture due to excessively low temperatures.

[0060] This application's solution, by receiving a comprehensive state vector indicating localized stress concentration or uneven material flow, can identify potential microstructural damage areas earlier and more accurately. Through instantaneous adjustment of the mold's local morphology, the stress path and deformation rate of the workpiece can be directly intervened, optimizing the material's deformation behavior from the source. Simultaneously, by combining local adjustments to the stamping speed curve and localized temperature control driven by real-time heat flow data, refined, multi-dimensional, and dynamic management of the workpiece deformation process is achieved. These synergistic adjustment mechanisms mean that the determination of processing parameters is no longer based on rough derivations of macroscopic strategies, but rather on customized optimization for specific workpieces, specific regions, and specific moments, effectively addressing the shortcomings of traditional methods in dealing with complex localized deformation problems.

[0061] In some preferred embodiments, it is assumed that the material is extremely sensitive to localized stress concentration and temperature changes when manufacturing a novel high-strength steel chassis support. During the stamping process, the integrated state vector received by the control unit shows that in a complex bending region of the support, the localized hardness is slightly higher, while transient frictional force data and heat flow data indicate that there is a high heat accumulation and uneven material flow trend in this region, suggesting the possibility of localized necking or microcracks.

[0062] In response, the control unit immediately identified the curved area as a potential region of microstructural damage. To address this risk, the control unit performs the following adjustments: First, the control unit drives the mold to make micron-level instantaneous adjustments to the local topography of the curved area. For example, by slightly changing the curvature of the mold cavity through a micro hydraulic cylinder, the material flow is guided more smoothly and the local stress is dispersed.

[0063] Secondly, the control unit dynamically adjusts the stamping speed curve. At the critical stage when the punch is about to contact and begin to deform in the bending area, the stamping speed is reduced by about 10-15% to slow down the strain rate of the material and give the material more time to deform uniformly.

[0064] At the same time, based on real-time heat flow data, the control unit activates the local cooling channels inside the mold and precisely sprays cooling medium into the bending area to control the temperature of the area within the optimal range for plastic deformation, thus avoiding a decline in material properties due to local overheating.

[0065] Ultimately, the control unit integrates the combined effects of these adjustments to the local mold shape, stamping speed curve, and local cooling strategy to derive the final adjustment amounts for the stamping pressure, holding time, mold fine-tuning stroke, and stamping speed for the current workpiece. This refined, multi-dimensional dynamic adjustment effectively prevents microscopic defects from forming in complex bending areas of the high-strength steel chassis support, ensuring the product's superior performance.

[0066] This application further proposes a more refined parameter adjustment mechanism. By introducing an adaptive calibration module, the adjustment coefficients of the processing parameters are dynamically corrected to address the challenges posed by material property deviations and the accumulation of mechanical wear on the equipment. According to the aforementioned chassis bracket manufacturing process control method, the step of the control unit deriving the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the parameter adjustment strategy to determine the processing parameters of the processing equipment further includes: The control unit receives the comprehensive state vector indicating whether there is a deviation between the workpiece material properties and the existing knowledge base, or the accumulation of mechanical wear on the stamping equipment. The control unit activates the adaptive calibration module based on the degree of deviation or wear reflected in the comprehensive state vector. The adaptive calibration module dynamically adjusts the adjustment coefficients of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed according to the degree of deviation or the degree of wear. The dynamic adjustment includes: when a deviation is detected between the characteristics of the new high-strength steel plate material and the existing knowledge base, nonlinear correction is made to the adjustment coefficients related to the material hardness, thickness, and uniformity of the microstructure based on the magnitude and direction of the deviation; when mechanical wear accumulation is detected in the stamping equipment, compensatory correction is made to the adjustment coefficients related to the die fine-tuning stroke and the stamping speed based on the location and degree of the wear; and the control unit derives the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the adjusted adjustment coefficients to determine the processing parameters of the processing equipment.

[0067] Specifically, the comprehensive state vector received by the control unit not only includes the distribution of individual physical properties of the workpiece, local friction coefficient, and local heat transfer efficiency, but also, through advanced analysis algorithms, can identify potential deviations between the workpiece material properties and a pre-established knowledge base, such as minor compositional differences between material batches and changes in grain structure. Simultaneously, this comprehensive state vector can also integrate long-term operating data from equipment sensors to indicate whether there are signs of accumulated mechanical wear in key components of the stamping equipment (such as dies and hydraulic systems), such as increased die surface roughness and increased guide post clearance.

[0068] When the degree of material property deviation or mechanical wear accumulation reflected in the comprehensive state vector reaches a preset threshold, the control unit is triggered, thereby activating a specially designed adaptive calibration module. The purpose of this module is to dynamically correct the adjustment coefficients used to calculate the initial adjustments for stamping pressure, holding time, die fine-tuning stroke, and stamping speed.

[0069] The dynamic adjustment mechanism is the key component of this application. Specifically, when a deviation is detected between the characteristics of a novel high-strength steel plate or other materials and existing knowledge, the adaptive calibration module performs nonlinear corrections on adjustment coefficients closely related to the material's physical properties, such as hardness, thickness, and microstructure uniformity, based on the magnitude and direction of the deviation. For example, if the material hardness is higher than expected, the adjustment coefficients for stamping pressure and holding time may need to be increased to ensure sufficient deformation; if there are slight fluctuations in material thickness, the adjustment coefficients for the die's fine-tuning stroke may need to be precisely adjusted. This nonlinear correction can more accurately adapt to the complex behavior of materials.

[0070] On the other hand, when accumulated mechanical wear of the stamping equipment is detected, the adaptive calibration module compensates for the adjustment coefficients related to the die fine-tuning stroke and the stamping speed based on the location and extent of the wear. For example, if slight wear on the die guide pillars leads to a decrease in positioning accuracy, the adjustment coefficient for the die fine-tuning stroke may be corrected to compensate for this mechanical clearance; if the hydraulic system response speed is slightly delayed due to wear, the adjustment coefficient for the stamping speed may be adjusted to ensure consistency between the actual stamping speed and the target speed. This compensatory correction aims to offset the negative impact of equipment aging.

[0071] Finally, the control unit will derive the adjustments for stamping pressure, holding time, die fine-tuning stroke, and stamping speed based on these dynamically adjusted coefficients. These adjustments are then used to determine the final processing parameters of the processing equipment, thereby ensuring that the processing maintains high precision and stability even when material properties change or the equipment wears down.

[0072] This application's solution, by introducing an adaptive calibration module, effectively addresses the problem of insufficient accuracy in determining processing parameters when traditional methods encounter deviations between workpiece material properties and existing knowledge bases, or when mechanical wear of stamping equipment accumulates. Through the aforementioned technical solution, this application can significantly improve the control accuracy and adaptability of the chassis support manufacturing process.

[0073] In some preferred embodiments, suppose a batch of workpieces entering the processing equipment is made of a new type of high-strength steel plate. Its local hardness, as detected, is slightly higher than the average value of similar materials in the existing knowledge base, and there are minor differences in the uniformity of its internal structure. In this case, the integrated state vector received by the control unit will indicate the deviation of this material characteristic from the existing knowledge base. After the adaptive calibration module is activated, it will nonlinearly correct the adjustment coefficients related to the stamping pressure and holding time based on the magnitude and direction of this higher hardness and difference in structural uniformity. For example, the adjustment coefficient for the stamping pressure may be moderately increased to ensure that the material can deform sufficiently without springback or cracking.

[0074] For example, in another scenario, a stamping machine has been running continuously for several months. The pressure response data of its hydraulic cylinders and the displacement accuracy data of the die guide pillars reflect slight accumulated mechanical wear in the integrated state vector. This wear may cause a slight deviation between the actual execution of the die fine-tuning stroke and the command, or a slight lag in the stamping speed response. In this case, the adaptive calibration module will compensate for the adjustment coefficients of the die fine-tuning stroke and stamping speed based on the location and extent of the wear. For example, if the wear of the guide pillars causes the die fine-tuning stroke to be slightly less than expected, the adjustment coefficient will be corrected to increase the commanded stroke, thereby compensating for the clearance caused by the wear; if the stamping speed response slows down, the adjustment coefficient will be corrected to start earlier or increase the driving force to ensure that the target speed is reached during the critical deformation stage. Through these dynamic and targeted adjustments, even with slight wear on the equipment, the machining accuracy and geometric features of the chassis support can still meet the design requirements.

[0075] In some embodiments described above, the control unit derives the adjustment amounts for stamping pressure, holding time, die fine-tuning stroke, and stamping speed based on adjustments to the local morphology of the die, the stamping speed curve, and local cooling or heating strategies. However, in actual manufacturing processes, mechanical components of the stamping equipment, such as die guide pillars and hydraulic cylinders, may experience wear, aging, or momentary malfunctions, leading to deviations between the actual execution of command parameters and theoretical values. If the operating conditions and execution deviations of these devices are not considered, even with optimized parameter adjustment strategies, the final processing effect may not meet expectations, thus affecting the manufacturing accuracy and product quality of the chassis bracket.

[0076] In response, this application further proposes to take into account the operating status of key components of the stamping equipment when determining the adjustment amount of processing parameters, so as to improve the accuracy and effectiveness of parameter adjustment.

[0077] Specifically, the steps by which the control unit derives the adjustment amounts for stamping pressure, holding time, die fine-tuning stroke, and stamping speed based on adjustments to the local shape of the mold, the stamping speed curve, and local cooling or heating strategies also include: The control unit receives operating status data of key components of the stamping equipment, including displacement accuracy data of the mold guide pillars and pressure response data of the hydraulic cylinders. The control unit determines whether there is a fault or wear in the key components of the stamping equipment based on the operating status data, and quantifies the deviation of the fault or wear from the execution of the die fine-tuning stroke and stamping pressure. The control unit integrates the local shape adjustment of the mold, the stamping speed curve adjustment, the local cooling or heating strategy adjustment, and the execution deviation to derive the adjustment amount of the stamping pressure, the holding time, the mold fine-tuning stroke, and the stamping speed, so as to determine the processing parameters of the processing equipment.

[0078] Among these, operational status data refers to real-time monitoring data reflecting the current working status of key mechanical or hydraulic components inside the stamping equipment. The displacement accuracy data of the die guide pillar can be understood as the deviation between the actual movement trajectory of the die guide pillar and the theoretically designed trajectory during the stamping process; its purpose is to evaluate the accuracy of die positioning and movement. The pressure response data of the hydraulic cylinder refers to the degree of matching between the internal pressure change and the expected pressure change after receiving a control command; its purpose is to reflect the execution capability and stability of the hydraulic system. In practical applications, the control unit analyzes this operational status data, for example, by comparing it with historical data or preset thresholds, to determine whether there are potential faults or accumulated wear in the key components of the stamping equipment. For example, a persistent deviation in the displacement accuracy of the die guide pillar from the allowable range may indicate guide pillar wear or insufficient lubrication; a sluggish or excessively fluctuating pressure response in the hydraulic cylinder may indicate leakage in the hydraulic system or valve malfunction.

[0079] Furthermore, once a fault or wear is identified, the control unit quantifies the impact of these issues on the actual execution of the die fine-tuning stroke and stamping pressure, i.e., it calculates the execution deviation. For example, if wear on the die guide pillars causes the actual die fine-tuning stroke to be 0.01 mm smaller than the commanded value, or if sluggish response of the hydraulic cylinder causes the actual stamping pressure to be 3 MPa lower than the commanded value, these deviations will be precisely quantified.

[0080] Subsequently, the control unit integrates the aforementioned adjustments to the local shape of the mold, the stamping speed curve, and the local cooling or heating strategy (these are adjustments derived based on the workpiece material properties and process conditions) with the quantified execution deviation. This integration aims to incorporate the equipment's own execution capabilities into the derivation of the final processing parameter adjustments, thereby ensuring that the derived parameter adjustments not only consider the workpiece's processing requirements but also the actual execution limitations and errors of the equipment.

[0081] This application's solution addresses the problem of inaccurate parameter adjustments caused by equipment execution errors in the basic solution by introducing operational status data of key components of the stamping equipment and quantifying their deviations from the execution of processing parameters. Because the operational status of key components (such as the displacement accuracy of the die guide pillars and the pressure response of the hydraulic cylinders) directly affects the actual application of die fine-tuning stroke and stamping pressure, these execution deviations must be taken into account when deriving the adjustment amounts of processing parameters. By quantifying and integrating these deviations, the control unit can more accurately predict the actual processing effect, thereby deriving more robust and adaptable adjustments to stamping pressure, holding time, die fine-tuning stroke, and stamping speed.

[0082] In one specific implementation, assuming that during the stamping process of the chassis bracket, the control unit receives displacement accuracy data of the mold guide post, showing a downward deviation of 0.02 mm at a critical stamping stage, and simultaneously, the hydraulic cylinder pressure response data indicates a 10-millisecond delay in reaching peak pressure, resulting in an actual stamping pressure 3 MPa lower than the commanded value. In the basic scheme, based on the local stress concentration of the workpiece, it may have been deduced that the adjustment amount needed to increase the mold fine-tuning stroke by 0.05 mm and the stamping pressure by 5 MPa should be increased. However, without considering the execution deviation of the equipment, the actual mold fine-tuning stroke may only increase by 0.03 mm (0.05 - 0.02), and the stamping pressure may only increase by 2 MPa (5 - 3), which would result in the ineffective suppression of microstructural damage to the workpiece in that area. In the scheme of this application, the control unit quantifies this 0.02 mm mold fine-tuning stroke execution deviation and the 3 MPa stamping pressure execution deviation. During the integration phase, the control unit comprehensively considers these execution deviations in conjunction with existing adjustments to the local mold shape, stamping speed curve, and local cooling or heating strategies. For example, to compensate for the downward deviation of the mold guide pillars, the control unit may further increase the command value of the mold fine-tuning stroke from 0.05 mm to 0.07 mm; to compensate for insufficient hydraulic cylinder pressure, the command value of the stamping pressure may be further increased from 5 MPa to 8 MPa. In this way, the final derived adjustments to stamping pressure, holding time, mold fine-tuning stroke, and stamping speed will more accurately reflect the actual physical adjustments required, thereby ensuring precise control of workpiece processing quality even under certain operating deviations in the equipment.

[0083] In some embodiments described above in this application, although it is proposed to integrate adjustments to the local shape of the mold, the stamping speed curve, the local cooling or heating strategy, and the execution deviation to derive the adjustment amount of the processing parameters, in the process of implementation, if the dynamic response characteristics of the stamping equipment, the material deformation differences in complex geometric feature areas of the workpiece, and the impact of the production environment on data quality are not fully considered, the accuracy and adaptability of the adjustment amount may be insufficient, thereby affecting the final processing quality and efficiency. Therefore, this application further proposes to refine and optimize the derivation process of the above-mentioned adjustment amount to more comprehensively address the complexity and uncertainty in actual production.

[0084] The control unit integrates the local shape adjustment of the mold, the stamping speed curve adjustment, the local cooling or heating strategy adjustment, and the execution deviation to derive the adjustment amounts of stamping pressure, holding time, mold fine-tuning stroke, and stamping speed, in order to determine the processing parameters of the processing equipment. The steps include: The control unit acquires real-time dynamic response data of the hydraulic system and servo motor of the stamping equipment, compares the real-time dynamic response data with the preset equipment response characteristics, and calculates the dynamic response deviation. The control unit divides the workpiece into multiple sub-regions based on the material deformation characteristics of the complex geometric feature areas of the workpiece, and assigns micro-damage suppression weights to the multiple sub-regions. The control unit receives the electromagnetic interference intensity, sensor drift, and data transmission delay time of the production environment, and performs real-time data quality assessment on the local shape adjustment of the mold, the stamping speed curve adjustment, the local cooling or heating strategy adjustment, and the execution deviation based on the electromagnetic interference intensity, the sensor drift, and the data transmission delay time. The control unit performs a weighted fusion of the local morphology adjustment of the mold, the stamping speed curve adjustment, the local cooling or heating strategy adjustment, the execution deviation, the dynamic response deviation, and the micro-damage suppression weight of each sub-region after the real-time data quality assessment, and derives the adjustment amount of the stamping pressure, the holding time, the mold fine-tuning stroke, and the stamping speed. The control unit performs fine-tuning of the local adjustment amounts in the derived adjustment amounts to meet the processing requirements of the complex geometric feature region. The control unit performs predictive simulation of the effect of the refined adjustment amount; and the control unit iteratively optimizes the adjustment amount based on the results of the predictive simulation.

[0085] Specifically, the real-time dynamic response data of the hydraulic system and servo motor of the stamping equipment refers to the instantaneous data of the changes in parameters such as hydraulic system pressure, flow rate, servo motor speed, and position over time during the actual stamping process. By comparing this data with preset equipment response characteristics (e.g., ideal pressure rise curve, speed response curve, etc.), the dynamic response deviation can be calculated. The purpose is to quantify the degree of deviation between the equipment and the ideal state during actual operation, thereby more accurately assessing the equipment's ability to execute adjustments and potential errors.

[0086] Based on the material deformation characteristics of complex geometric features, the workpiece is divided into multiple sub-regions. For example, critical deformation areas such as bending, stretching, or punching of the chassis support can be divided according to characteristics such as material thickness variation and stress concentration risk. Micro-damage suppression weights are assigned to these sub-regions to give higher attention and adjustment priority to areas prone to micro-damage (such as cracks and wrinkles) during subsequent parameter adjustment and fusion, thereby ensuring the processing quality of these critical areas.

[0087] In practical applications, the intensity of electromagnetic interference in the production environment, sensor drift, and data transmission delay are important factors affecting data accuracy. Receiving this information and performing real-time data quality assessment aims to identify and quantify the impact of these external factors on data such as mold local shape adjustments, stamping speed curve adjustments, local cooling or heating strategy adjustments, and execution deviations. This allows for necessary corrections or weighting of the data before fusion, improving the reliability of decision-making.

[0088] The adjustment of various parameters after real-time data quality assessment (adjustment of local mold shape, adjustment of stamping speed curve, adjustment of local cooling or heating strategy, execution deviation, dynamic response deviation) and the micro-damage suppression weight of each sub-region are weighted and integrated. The purpose is to comprehensively consider multi-dimensional information such as equipment dynamics, local characteristics of workpiece, and environmental interference, so as to derive the adjustment amount of stamping pressure, holding time, mold fine-tuning stroke and stamping speed more comprehensively and accurately.

[0089] Furthermore, in response to the processing requirements of complex geometric feature areas, the local adjustment amounts in the derived adjustment amounts are refined. The purpose is to ensure that more precise control can be applied in these areas with higher requirements for accuracy and quality. For example, more detailed pressure or speed adjustments may be required in sharp-angle bends or deep-stretched areas.

[0090] Therefore, predictive simulation of the implementation effect of the refined adjustment amount aims to verify the effectiveness and potential risks of the adjustment plan through virtual simulation before actual processing, so as to avoid trial and error costs and defective products in actual production.

[0091] Finally, based on the results of predictive simulation, the adjustment amount is iteratively optimized to form a closed-loop feedback mechanism, continuously improve the adjustment strategy, and achieve the best processing effect and product quality.

[0092] The solution proposed in this application effectively addresses the problem of insufficient accuracy and adaptability of parameter adjustment in complex manufacturing environments by introducing multi-dimensional real-time data and optimization mechanisms.

[0093] In some preferred embodiments, a specific example is given below. Suppose that when manufacturing a chassis support with a complex U-shaped bend and thin-walled reinforcing ribs, the control unit first acquires real-time dynamic response data of the hydraulic system and servo motor of the stamping equipment. For example, if a slight lag in the pressure response of the hydraulic cylinder is detected during a specific stamping stage, the dynamic response deviation is calculated. Simultaneously, based on the geometric characteristics and material deformation properties of the chassis support, the U-shaped bend area is identified as a high-stress-concentration sub-region and assigned a higher micro-damage suppression weight, while the thin-walled reinforcing rib area is identified as a wrinkle-prone sub-region and assigned a corresponding weight. During the stamping process, the control unit receives information about a sudden increase in electromagnetic interference intensity in the production workshop. Combining this with sensor drift and data transmission delay, it performs real-time data quality assessment on the friction force and heat flow data collected by the transient surface interaction sensing array embedded in the key deformation area of ​​the stamping die to correct for potential measurement errors.

[0094] Subsequently, the control unit performs a weighted fusion of the following adjustments after real-time data quality evaluation: local mold morphology adjustments (e.g., local mold fine-tuning for U-bend regions), stamping speed curve adjustments (e.g., reducing stamping speed during the U-bend formation stage), local cooling or heating strategy adjustments (e.g., local cooling of U-bend regions), execution deviations (deviations from mold guide post displacement accuracy and hydraulic cylinder pressure response), dynamic response deviations (hysteresis from the hydraulic system and servo motor), and micro-damage suppression weights for each sub-region. For example, the weight of high-stress U-bend regions will have a greater impact on the adjustment amount of stamping pressure and mold fine-tuning stroke during the fusion process.

[0095] To address the machining requirements of the complex geometric features of the U-shaped bend, the control unit refines the derived stamping pressure and the local adjustment amounts of the die fine-tuning stroke to ensure the accuracy of the radius and angle of the U-shaped bend. Next, the control unit performs predictive simulations of the execution effects of the refined adjustments, simulating the deformation process and final morphology of the workpiece under these parameters. If the simulation results indicate a slight risk of material thinning on the inner side of the U-shaped bend, the control unit iteratively optimizes the stamping pressure and holding time based on the simulation results. For example, it slightly increases the holding time or adjusts the local pressure distribution until the simulation results meet the design requirements, thus effectively avoiding potential quality defects before actual machining.

[0096] This application further proposes optimizations to the above-mentioned predictive simulation steps by introducing in-depth evaluation of the workpiece's microstructure and macroscopic mechanical properties to more comprehensively predict and suppress potential structural damage.

[0097] In this regard, this application further proposes the following steps for predictive simulation of the execution effect of the above-mentioned control unit on the finely corrected adjustment amount: The control unit receives the microstructure parameters and macromechanical property parameters of the workpiece, and compares the microstructure parameters and macromechanical property parameters with the design benchmark to identify potential risk areas of microstructure damage or macromechanical property degradation. The control unit estimates the material microstructure evolution process of the workpiece for the risk area based on the workpiece's deformation history and temperature history. The control unit evaluates the fatigue life and fracture toughness of the risk area based on the estimation results; The control unit, based on the evaluation results, corrects the refined adjustment amount to suppress microstructural damage and improve macroscopic mechanical properties; and, The control unit performs the predictive simulation based on the corrected adjustment amount.

[0098] Specifically, the microstructure parameters may include grain size, grain boundary characteristics, phase composition, dislocation density, precipitate distribution, etc., which directly affect the strength, plasticity, and toughness of the material. The macroscopic mechanical property parameters may include yield strength, tensile strength, elongation, hardness, etc., which reflect the overall mechanical response of the material. The design benchmark refers to the material performance standards and structural integrity requirements determined during the product design phase. By comparing the microstructure parameters and macroscopic mechanical property parameters of the actual workpiece with the design benchmark, regions that may have microstructure damage (e.g., grain boundary cracking, voids, inclusions, etc.) or decreased macroscopic mechanical properties (e.g., insufficient strength, reduced toughness, etc.) can be effectively identified; these regions are defined as risk areas.

[0099] The estimation of the material microstructure evolution process refers to the changes in the material's microstructure during processing due to deformation and temperature variations, such as recrystallization, phase transformation, and grain growth. By combining the workpiece's deformation history (e.g., strain path, strain rate) and temperature history (e.g., heating and cooling rates, holding time), these changes can be predicted using material constitutive models and microstructure evolution models. The aim is to gain a deeper understanding of the material's internal state after processing, providing accurate input for subsequent performance evaluation.

[0100] In practical applications, fatigue life refers to the number of cycles required for a material to fracture from crack initiation under cyclic loading, while fracture toughness refers to the material's ability to resist crack propagation. For identified risk areas, based on estimated material microstructure evolution, finite element analysis, damage mechanics models, or empirical formulas can be used to quantitatively assess the fatigue life and fracture toughness of these areas. The aim is to predict whether the chassis support will experience fatigue failure or brittle fracture under specific load conditions during long-term service.

[0101] Furthermore, the control unit corrects the refined adjustment amount based on the evaluation results. This means that if the evaluation results show that the fatigue life or fracture toughness of the risk area is lower than the design requirements, or there is a significant risk of microstructural damage, then the adjustment amount of the processing parameters needs to be further corrected. For example, the stamping pressure, holding time, die fine-tuning stroke, or stamping speed can be adjusted to change the local stress state, material flow, or cooling rate, thereby suppressing the occurrence or development of microstructural damage and improving the macroscopic mechanical properties of the workpiece. The aim is to fundamentally improve the intrinsic quality and reliability of the chassis support by optimizing the processing technology.

[0102] Therefore, the predictive simulation performed by the control unit based on the corrected adjustment amount means that after correcting the adjustment amount, the predictive simulation is run again to verify whether the corrected processing parameters can effectively solve the previously identified problems and achieve the expected performance goals. This process can be iterated multiple times until the optimal processing parameters that meet all design requirements are obtained.

[0103] The solution proposed in this application, by introducing the reception and comparison of the microstructure parameters and macroscopic mechanical property parameters of the workpiece in predictive simulation, can identify potential risk areas of microstructure damage or macroscopic mechanical property degradation, thereby making up for the shortcomings of traditional simulation that may only focus on macroscopic geometry and mechanical behavior.

[0104] This application further proposes to simulate the initiation and propagation process of fatigue cracks in predictive simulation by introducing microscopic defect distribution data, thereby achieving more accurate fatigue life assessment and processing parameter optimization. The steps of the control unit performing predictive simulation based on the corrected adjustment amount include: The control unit receives microscopic defect distribution data from different batches of workpieces, including the size, shape, location, and density of the defects. The control unit overlays the micro-defect distribution data with the three-dimensional geometric model of the workpiece to generate a simulation model with an initial defect distribution. In the predictive simulation, the control unit simulates the initiation process of fatigue cracks at the initial defect based on the simulation model, the deformation history and temperature history of the workpiece. The control unit simulates the propagation path and rate of the fatigue crack based on the initiation location and size of the fatigue crack, as well as the microstructural parameters of the workpiece. During the simulation, the control unit dynamically adjusts the local stress field and material damage accumulation parameters according to the propagation of the fatigue crack. The control unit evaluates the fatigue life and fracture toughness of the workpiece during long-term service based on the simulation results of the initiation and propagation of fatigue cracks. The control unit further modifies the adjusted amount based on the evaluation results to suppress the initiation and propagation of fatigue cracks and improve the overall structural reliability; and the control unit performs the predictive simulation based on the further modified adjusted amount.

[0105] Specifically, the control unit receives microscopic defect distribution data from different batches of workpieces, which forms the basis for accurate fatigue life assessment. Microscopic defect distribution data can be understood as detailed information about tiny discontinuous or heterogeneous regions existing within the workpiece, such as inclusions, micropores, and grain boundary defects. Their size, shape, location, and density directly affect the initiation and early propagation of fatigue cracks. This data can be obtained using high-resolution non-destructive testing techniques (such as X-ray computed tomography, ultrasonic microscopy, or scanning electron microscopy combined with image processing).

[0106] The control unit overlays microscopic defect distribution data onto the workpiece's three-dimensional geometric model to create a highly realistic simulation model. This model not only includes the workpiece's macroscopic geometric features but also accurately maps the initial internal defect distribution, providing a realistic spatial and material context for subsequent simulations of fatigue crack initiation and propagation.

[0107] In practical applications, predictive simulations use a control unit to simulate the initiation of fatigue cracks at initial defects, based on the simulation model, the workpiece's deformation history, and temperature history. The deformation and temperature histories provide information on the mechanical and thermal loads experienced by the workpiece during processing and service, which is crucial for accurately predicting fatigue crack initiation. Due to stress concentration effects, initial defects are often the most likely sites for fatigue crack initiation.

[0108] Furthermore, the control unit simulates the propagation path and rate of fatigue cracks based on the initiation location and size of the fatigue cracks, as well as the microstructural parameters of the workpiece. Microstructural parameters, such as grain size, grain boundary characteristics, and phase composition, have a significant impact on crack propagation behavior. By combining this information, the simulation can more accurately predict how cracks propagate along the weakest paths of the material and their propagation rate.

[0109] During the simulation, the control unit dynamically adjusts the local stress field and material damage accumulation parameters based on the propagation of the fatigue crack. This means that as the crack propagates, the stress distribution near its tip changes, and the material damage accumulation also changes accordingly. Dynamically adjusting these parameters ensures that the simulation model always reflects the true physical process of crack propagation, improving the accuracy of predictions.

[0110] Therefore, the control unit evaluates the fatigue life and fracture toughness of the workpiece during long-term service based on the simulation results of fatigue crack initiation and propagation. Fatigue life refers to the number of cycles a workpiece undergoes under cyclic loading until failure, while fracture toughness measures the material's ability to resist crack propagation. Through detailed simulation of crack initiation and propagation, these key performance indicators can be more accurately quantified and evaluated.

[0111] refer to Figure 2 , Figure 2 This is a schematic diagram of a chassis bracket manufacturing process control system provided in an embodiment of the present invention. The system includes: The physical property data acquisition module is used to acquire the physical property data of the workpiece entering the processing equipment. The physical property data includes the local thickness, local hardness and internal structure uniformity data of the workpiece. The characterization data integration module is used to integrate the physical attribute data into a characterization data reflecting the distribution of individual physical attributes of the workpiece, and transmit the characterization data to the control unit of the processing equipment; A parameter determination module, located in the control unit, is used to determine the processing parameters of the processing equipment based on the characterization data. A processing execution module is used to enable the processing equipment to process the workpiece according to the processing parameters; The geometric feature detection module is used to perform geometric feature detection on the processed workpiece and obtain geometric feature data; and the parameter determination logic adjustment module is used to adjust the determination logic of the processing parameters based on the geometric feature data, and feed back the adjusted determination logic to the parameter determination module for use in determining the processing parameters of subsequent batches of workpieces.

[0112] The chassis support manufacturing process control system proposed in this application, through modular design, achieves real-time perception, characterization, and feedforward control of individual workpiece physical attribute data. Combined with post-processing geometric feature feedback adjustments, it effectively solves the problems of information silos and deviation accumulation in traditional manufacturing processes. This system ensures that processing equipment performs customized processing based on the actual state of each workpiece, and through a continuous feedback learning mechanism, it continuously optimizes the logic for determining processing parameters, significantly improving the quality stability, efficiency, and yield of chassis support manufacturing.

[0113] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling the manufacturing process of a chassis bracket, characterized in that, include: The physical property data of the workpiece entering the processing equipment is obtained, including the local thickness, local hardness and internal structure uniformity data of the workpiece. The physical attribute data are integrated into a characterization data that reflects the distribution of individual physical attributes of the workpiece; The characterization data is transmitted to the control unit of the processing equipment, so that the control unit determines the processing parameters of the processing equipment based on the characterization data; The processing equipment processes the workpiece according to the processing parameters; The processed workpiece is subjected to geometric feature detection to obtain geometric feature data; Based on the geometric feature data, the determination logic of the processing parameters is adjusted, and the adjusted determination logic is used to determine the processing parameters of subsequent batches of workpieces.

2. The method for controlling the manufacturing process of a chassis bracket according to claim 1, characterized in that, The step of transmitting the characterization data to the control unit of the processing equipment, so that the control unit determines the processing parameters of the processing equipment based on the characterization data, further includes: The control unit receives frictional force data and heat flow data collected by the transient surface interaction sensing array embedded in the key deformation area of ​​the stamping die; The control unit processes the friction force data and the heat flow data to obtain the local friction coefficient and the local heat transfer efficiency. The control unit integrates the characterization data, the local friction coefficient, and the local heat transfer efficiency to generate a comprehensive state vector; The control unit derives the adjustment amounts of stamping pressure, holding time, die fine-tuning stroke, and stamping speed based on the comprehensive state vector, so as to determine the processing parameters of the processing equipment; After processing is completed, the control unit receives the workpiece's geometric shape and subsurface force distribution data detected by the online detection system for workpiece micro-defects and subsurface forces. The control unit compares the geometric topography and subsurface force distribution data with the design benchmark, calculates the deviation value, and quantitatively characterizes the defects caused by surface interaction differences. The control unit optimizes the logic for determining the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the deviation value and the quantitative characterization data of the defect.

3. The method for controlling the manufacturing process of a chassis bracket according to claim 2, characterized in that, The step of determining the processing parameters of the processing equipment by deriving the adjustment amounts of stamping pressure, holding time, die fine-tuning stroke, and stamping speed based on the comprehensive state vector, further includes: The control unit receives information on the fluctuation of characteristics between workpiece batches and information on changes in production environment parameters; The control unit determines whether the fluctuation information or the change information exceeds a preset range; When the fluctuation information or the change information exceeds the preset range, the control unit selects a parameter adjustment strategy based on the fluctuation pattern or change trend that exceeds the preset range; The control unit derives the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the parameter adjustment strategy, in order to determine the processing parameters of the processing equipment.

4. The method for controlling the manufacturing process of a chassis bracket according to claim 3, characterized in that, The step of determining the processing parameters of the processing equipment by deriving the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the parameter adjustment strategy by the control unit further includes: The control unit receives the comprehensive state vector indicating that the workpiece may have localized stress concentration or uneven material flow. The control unit identifies potential microstructural damage areas based on the combination of local hardness, local thickness, local friction coefficient, and local heat transfer efficiency reflected in the comprehensive state vector. The control unit instantly adjusts the local morphology of the mold based on the microstructural damage area to change the local force path and deformation rate of the workpiece. The control unit adjusts the stamping speed curve to reduce the local stamping speed during the critical deformation stage; The control unit adjusts the local cooling or heating strategy based on real-time heat flow data to control the temperature of the workpiece during the deformation process; The control unit derives the adjustment amounts of the stamping pressure, the holding time, the mold fine-tuning stroke, and the stamping speed based on the local shape adjustment of the mold, the stamping speed curve adjustment, and the local cooling or heating strategy adjustment, so as to determine the processing parameters of the processing equipment.

5. The method for controlling the manufacturing process of a chassis bracket according to claim 3, characterized in that, The step of determining the processing parameters of the processing equipment by deriving the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the parameter adjustment strategy by the control unit further includes: The control unit receives the comprehensive state vector indicating whether there is a deviation between the workpiece material properties and the existing knowledge base, or the accumulation of mechanical wear on the stamping equipment. The control unit activates the adaptive calibration module based on the degree of deviation or wear reflected in the comprehensive state vector. The adaptive calibration module dynamically adjusts the adjustment coefficients of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed according to the degree of deviation or the degree of wear. The dynamic adjustment includes: when a deviation is detected between the characteristics of the new high-strength steel plate material and the existing knowledge base, nonlinear correction is made to the adjustment coefficients related to the material hardness, thickness, and uniformity of the microstructure according to the magnitude and direction of the deviation; when mechanical wear of the stamping equipment is detected to accumulate, compensatory correction is made to the adjustment coefficients related to the die fine-tuning stroke and the stamping speed according to the location and degree of the wear. The control unit derives the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the adjusted adjustment coefficients, in order to determine the processing parameters of the processing equipment.

6. The method for controlling the manufacturing process of a chassis bracket according to claim 4, characterized in that, The step of determining the processing parameters of the processing equipment by deriving the adjustment amounts of the stamping pressure, the holding time, the die fine-tuning stroke, and the stamping speed based on the local shape adjustment of the mold, the stamping speed curve adjustment, and the local cooling or heating strategy of the control unit, in order to determine the processing parameters of the processing equipment, further includes: The control unit receives operating status data of key components of the stamping equipment, including displacement accuracy data of the mold guide pillars and pressure response data of the hydraulic cylinders. The control unit determines whether there is a fault or wear in the key components of the stamping equipment based on the operating status data, and quantifies the deviation of the fault or wear from the execution of the die fine-tuning stroke and stamping pressure. The control unit adjusts the local shape of the mold, the stamping speed curve, and the local cooling or heating strategy, and integrates the execution deviations to derive the adjustment amounts of the stamping pressure, the holding time, the mold fine-tuning stroke, and the stamping speed, so as to determine the processing parameters of the processing equipment.

7. The method for controlling the manufacturing process of a chassis bracket according to claim 6, characterized in that, The control unit adjusts the local shape of the mold, the stamping speed curve, and the local cooling or heating strategy, and integrates the execution deviations to derive the adjustment amounts for stamping pressure, holding time, mold fine-tuning stroke, and stamping speed, in order to determine the processing parameters of the processing equipment. The steps include: The control unit acquires real-time dynamic response data of the hydraulic system and servo motor of the stamping equipment, compares the real-time dynamic response data with the preset equipment response characteristics, and calculates the dynamic response deviation. The control unit divides the workpiece into multiple sub-regions based on the material deformation characteristics of the complex geometric feature areas of the workpiece, and assigns micro-damage suppression weights to the multiple sub-regions. The control unit receives the electromagnetic interference intensity, sensor drift, and data transmission delay time of the production environment, and adjusts the local shape of the mold, the stamping speed curve, and the local cooling or heating strategy based on the electromagnetic interference intensity, the sensor drift, and the data transmission delay time, and performs real-time data quality assessment on the execution deviation. The control unit performs weighted fusion of the local morphology adjustment of the mold, the stamping speed curve adjustment, the local cooling or heating strategy adjustment, the execution deviation, the dynamic response deviation, and the micro-damage suppression weight of each sub-region after the real-time data quality assessment, and derives the adjustment amount of the stamping pressure, the holding time, the mold fine-tuning stroke, and the stamping speed. The control unit performs fine-tuning of the local adjustment amounts in the derived adjustment amounts to meet the processing requirements of the complex geometric feature region. The control unit performs predictive simulation of the execution effect of the finely corrected adjustment amount; The control unit iteratively optimizes the adjustment amount based on the results of the predictive simulation.

8. The method for controlling the manufacturing process of a chassis bracket according to claim 7, characterized in that, The step of the control unit performing predictive simulation of the effect of the refined adjustment amount includes: The control unit receives the microstructure parameters and macromechanical property parameters of the workpiece, and compares the microstructure parameters and macromechanical property parameters with the design benchmark to identify potential risk areas of microstructure damage or macromechanical property degradation. The control unit estimates the material microstructure evolution process of the workpiece for the risk area based on the workpiece's deformation history and temperature history. The control unit evaluates the fatigue life and fracture toughness of the risk area based on the estimation results; The control unit corrects the refined adjustment amount based on the evaluation results in order to suppress microstructural damage and improve macroscopic mechanical properties. The control unit performs the predictive simulation based on the corrected adjustment amount.

9. The method for controlling the manufacturing process of a chassis bracket according to claim 8, characterized in that, The step of the control unit performing the predictive simulation based on the corrected adjustment amount includes: The control unit receives microscopic defect distribution data from different batches of workpieces, including the size, shape, location, and density of the defects. The control unit overlays the micro-defect distribution data with the three-dimensional geometric model of the workpiece to generate a simulation model with an initial defect distribution. In the predictive simulation, the control unit simulates the initiation process of fatigue cracks at the initial defect based on the simulation model, the deformation history and temperature history of the workpiece. The control unit simulates the propagation path and rate of the fatigue crack based on the initiation location and size of the fatigue crack and the microstructural parameters of the workpiece. During the simulation, the control unit dynamically adjusts the local stress field and material damage accumulation parameters according to the propagation of the fatigue crack. The control unit evaluates the fatigue life and fracture toughness of the workpiece during long-term service based on the simulation results of the initiation and propagation of fatigue cracks. The control unit further corrects the adjusted amount based on the evaluation results to suppress the initiation and propagation of fatigue cracks and improve the overall structural reliability. The control unit performs the predictive simulation based on the adjusted amount after further correction.

10. A chassis support manufacturing process control system, characterized in that, The system includes: The physical property data acquisition module is used to acquire the physical property data of the workpiece entering the processing equipment. The physical property data includes the local thickness, local hardness and internal structure uniformity data of the workpiece. The characterization data integration module is used to integrate the physical attribute data into a characterization data reflecting the distribution of individual physical attributes of the workpiece, and transmit the characterization data to the control unit of the processing equipment; A parameter determination module, located in the control unit, is used to determine the processing parameters of the processing equipment based on the characterization data. A processing execution module is used to enable the processing equipment to process the workpiece according to the processing parameters; The geometric feature detection module is used to perform geometric feature detection on the processed workpiece and obtain geometric feature data; The parameter determination logic adjustment module is used to adjust the determination logic of the processing parameters based on the geometric feature data, and feed the adjusted determination logic back to the parameter determination module for use in determining the processing parameters of subsequent batches of workpieces.

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