High-precision machining method and system for copper pipes of multiple specifications

Abstract

The invention discloses a high-precision machining method and system for multi-specification copper pipes, and particularly relates to the technical field of copper pipe machining analysis. Raw material parameters, process time sequence data and detection data in the machining process of the multi-specification copper pipes are collected; an enhanced data set is constructed in combination with a B-spline interpolation algorithm and a conditional generative adversarial network, and the problem of insufficient coverage of measured data sparsity and extreme working conditions is solved; a grain evolution-stress coupling simulation model is established to quantify the influence of grain boundary migration on stress, and coordinates and correction coefficients of a high-stress area are marked; a graph convolutional network is adopted to realize cross-scale stress prediction, dynamic process parameters are generated through a multi-objective optimization algorithm, and finally real-time control is performed through digital twin drive. The technical problems of low deformation control precision and high defect rate caused by non-uniform residual stress distribution in traditional copper pipe machining are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of copper tube processing and analysis, and more specifically, to a high-precision processing method and system for copper tubes of multiple specifications. Background Art

[0002] As the core link of copper tube precision processing, cold drawing process makes the tube plastically deformed by die drawing. In the fields of new energy, microelectronics, etc., the high-precision requirements of copper tubes of various specifications are becoming increasingly stringent, especially for thin-walled and large diameter-thickness ratio copper tubes. The nonlinear effect of residual stress distribution after cold drawing is significant, which directly affects the qualification rate and service life of subsequent processing (such as flaring and bending).

[0003] The problems faced by the existing cold drawing process are: Limitations of the experience-driven approach: Traditional process design is highly dependent on empirical formulas and fails to effectively integrate the inherent correlations between multi-source heterogeneous data such as process parameters, material properties, and equipment status. It lacks the ability to model the microstructure evolution of copper tubes (grain boundary migration, dislocation density changes) and macroscopic stress transfer mechanisms across scales, leading to frequent quality problems such as residual stress concentration and dimensional tolerance.

[0004] Dynamic control hysteresis: In industrial production, the real-time regulation of process parameters mainly relies on the PID control strategy based on fixed rules. It is difficult to establish a real-time coupling relationship model between geometric specification parameters and dynamic stress field. Under abnormal working conditions such as sudden changes in cold drawing speed and temperature fluctuations, the control system has a response lag. The virtual data generation method has the defect of violating the stress balance condition, resulting in distortion of the prediction model. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a high-precision processing method and system for copper tubes of multiple specifications, which realizes intelligent closed-loop control of processing quality through multi-modal data fusion and physical constraint modeling to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a high-precision processing method for copper tubes of multiple specifications, comprising: Step 1. Multi-source data acquisition and physical enhancement generation: Collect raw material parameters (grain size distribution data, impurity content, geometric dimensions), process timing data (cold drawing speed timing, heat treatment temperature curve, cooling rate curve), and detection data (actual residual stress data, such as X-ray diffraction residual stress distribution of X-ray surface and cross section, ultrasonic flaw detection defect coordinates) during the processing of multi-specification copper tubes to obtain real sample data; process through B-spline interpolation algorithm and conditional adversarial generation network, and improve the spatial matching accuracy through B-spline interpolation algorithm; expand the data set through finite element simulation, and obtain virtual sample data through conditional adversarial generation network. The virtual sample data and the aligned real sample data constitute an enhanced data set; the virtual sample data must meet the constraints of axial force balance and Hollomon hardening model; the loss function of the conditional adversarial generation network includes adversarial loss, axial force balance term and Hollomon hardening model constraints to ensure that the generated data conforms to the laws of mechanics; the axial force balance term means that in the z-axis direction, the vector sum of all normal stresses (tensile stress or compressive stress) acting on section A is zero, indicating that the object has no net external force in the axial direction and is in a state of static equilibrium; the Hollomon hardening model refers to the relationship in which the stress of the copper tube increases with the increase of strain in a power function during the plastic deformation process; Step 2: Input the enhanced data set, modify the enhanced data set through grain evolution-stress coupling simulation modeling and physical constraint-driven process parameter correction, output the physical verification data set, and mark the high stress area coordinates and correction coefficients; Step 3: Input the physical verification data set and defect-stress mapping table, and build a stress prediction model through the XGBoost regression model; Step 4: Cross-modal causal enhancement hybrid agent optimization: Input stress prediction model and processing target constraints; Generate the optimal parameter combination that meets the processing target constraints based on the multi-objective optimization algorithm (NSGA-II), and dynamically adjust the process parameters based on real-time stress data; Step 5. Real-time control driven by digital twins: Input the optimal parameter combination and real-time sensor data, dynamically adjust the process parameters through the combination of fuzzy logic and PID control, and output real-time process adjustment instructions, process adjustment log information and processing quality reports.

[0007] Preferably, a B-spline interpolation algorithm with a local curvature weight factor is used to perform three-dimensional interpolation on the ultrasonic defect coordinates. When the ultrasonic defect coordinates are mapped to the X-ray stress grid, the spatial offset error between the defect location and the stress field is eliminated, and the spatial matching accuracy is improved. When the conditional adversarial generative network is used to generate virtual sample data, extreme working conditions of copper tubes are covered, such as cold drawing speed of 1.0-3.0m / s, temperature of 450-600℃, and stress data under extreme working conditions of copper tubes with a wall thickness of less than 1mm and a diameter-to-thickness ratio of greater than 30.

[0008] Preferably, the enhanced data set is used to perform grain evolution-stress coupling simulation modeling, quantify the dynamic effect of grain boundary migration on residual stress, and generate a physical verification data set containing grain boundary migration effect data; The grain boundary migration impact data include dynamic changes in grain boundary position, grain size and dislocation density, and corresponding residual stress distribution; The cellular automaton model is used for simulation modeling, the grain unit state is defined as the orientation angle θ and the dislocation density ρ, the grain boundary migration rate is calculated based on the Moore neighborhood rule, and the grain orientation field is dynamically updated; the grain boundary migration rate reflects the influence of the dynamic evolution of the grain on the residual stress; According to the grain size d and dislocation density ρ, the anisotropic yield strength is calculated by the following formula, and the simulated residual stress distribution is obtained based on the anisotropic yield strength: ; in, is the matrix strength of the copper tube, G represents the shear modulus, k is the grain boundary strengthening coefficient, which is calibrated by nanoindentation experiment, and δ is the dislocation strengthening coefficient, which is obtained based on the Taylor relationship of dislocation density ρ; usually ; k, , G and b (Burgess vector) are material constants; they help simulate the microscopic behavior of materials in copper tube processing by combining the contributions of dislocation hardening and grain refinement to strength; The generated simulated residual stress distribution is compared with the actual residual stress data in the enhanced data set (such as X-ray residual stress distribution) to verify the simulation accuracy; if differences are found, the abnormal parts in the enhanced data set are identified and corrected.

[0009] Preferably, the process parameter correction driven by physical constraints: the proportion of the circumferential tensile stress area in the simulated residual stress distribution is calculated in real time. If it exceeds a preset value, such as 40%, the sequential quadratic programming is triggered to optimize and adjust the cold drawing speed v and temperature T. The objective function is: ; in, is the proportion of circumferential tensile stress area, indicating the proportion of abnormal area of ​​circumferential tensile stress of copper tube; is the deviation between the cold drawing speed and the initial speed, is the deviation of temperature from the initial temperature; The defect-stress mapping table with the coordinates of the high stress area and the correction coefficient marked based on the objective function optimization results is as follows: The objective function reduces the proportion of circumferential tensile stress area by optimizing the cold drawing speed and temperature; the area that still exceeds the threshold in the simulated residual stress distribution after optimization is identified as a high stress area, and its coordinates and the corresponding speed and temperature correction coefficients are recorded to generate a defect-stress mapping table.

[0010] Preferably, building a stress prediction model includes: The copper tube geometry is converted into a graph structure, where nodes are discrete grid points, and features include position coordinates, real-time hardness, and local cold drawing speed; edges are defined as connections between adjacent nodes, and edge weights are calculated based on adjacent node stress gradients and grain boundary orientation differences; In the present invention, the edge weight is ;in, represents the circumferential stress gradient, is the grain orientation angle, which is used to describe the grain orientation arc; It indicates the grain boundary orientation difference, reflecting the difference in the orientation curvature of adjacent grains; Explanation: The edge weight coefficient is set based on the contribution of the grain boundary orientation difference to stress transfer. For example, the contribution of the grain boundary orientation difference to stress transfer is 30%-50%, so 0.5 is taken to balance the influence of the gradient and orientation difference; By combining graph convolutional networks (GAT+IGC), cross-scale coupled modeling of grain boundary migration and dislocation density evolution is achieved: the effect of grain boundary sliding on the stress of adjacent nodes is calculated, the grain boundary misorientation in the edge weight is directly related to the stress gradient, and the node hardness characteristics are related to the dislocation density evolution; the graph structure is updated regularly to reflect the change in edge weight caused by grain coarsening; incremental graph convolution (IGC) is used to manage computational efficiency, and multi-head graph convolution (GAT) is used to calculate the effect of grain boundary sliding on the stress of adjacent nodes; Train the XGBoost regression model to predict the stress value at the node using node features and edge weights as input; use a physical verification data set to ensure axial force balance verification; during the training process, the loss function includes mean square error and axial force balance penalty terms, and the hyperparameters of the stress prediction model are optimized using the Bayesian method; The post-processing model predicts that the proportion of the circumferential tensile stress area does not exceed 20%, and the regional growing algorithm is used to correct the excessive area.

[0011] Preferably, the training process of the stress prediction model includes: Pre-training using finite element simulation data verified by axial force balance to ensure the axial force balance term ,in is the predetermined tolerance; Fine-tuning using experimental data, the loss function of the stress prediction model includes mean square error and axial force balance penalty terms , hyperparameters are tuned via Bayesian optimization; The regional growing algorithm is used in post-processing to ensure that the proportion of the circumferential tensile stress area does not exceed 20% and to correct the excessive areas.

[0012] Preferably, the process of obtaining the optimal parameter combination includes: Construct a cross-modal causal model, input the prediction results of the stress prediction model and the processing target constraints (maximum deformation <0.05mm, circumferential stress, axial stress), identify the causal effect of process parameters on stress distribution through causal inference methods, and generate a causal effect diagram; Based on the cause-effect diagram, the goal is set to minimize stress concentration and deformation, and the NSGA-II algorithm is used to generate the Pareto optimal parameter set; Combined with real-time stress data, the real-time stress concentration is calculated to trigger process parameter adjustments; Output the optimal parameter combination and switching strategy.

[0013] Preferably, the method includes a stress prediction model performance verification step: calculating the stress prediction model adaptation index through the processing quality report and the process adjustment log information; if the stress prediction model adaptation index is lower than a threshold, triggering an early warning and updating the stress prediction model.

[0014] Preferably, the stress prediction model adaptation index is obtained in the following manner: Step S11: Calculate the processing quality fluctuation factor : ; in, Indicates the actual deformation of the copper tube. Indicates the standard deviation of the benchmark deformation (historical data statistics), represents the benchmark stress uniformity index, and S represents the actual stress uniformity index. The exponential function is introduced to enhance the sensitivity of S. When S is lower than The fluctuation factor is significantly improved, which more accurately reflects the deterioration of stress distribution; Step S12: Process adjustment stability factor : ; in, Indicates the actual adjustment frequency of the process, Indicates reasonable adjustment of the frequency threshold. Indicates the actual adjustment range. Indicates the upper limit of the adjustment range allowed; an inverse proportional function is used to suppress excessive adjustment range to avoid system shock caused by frequent and large adjustments; Step S13: Stress prediction model adaptation index : ; in, represents the time attenuation coefficient, Indicates the current time. Indicates the last update time of the model; notifies the addition of a time decay term to adapt the model that has not been updated for a long time to an accelerated exponential decline and enforces periodic verification; square root operations smooth out the impact of stability and avoid interference from extreme values.

[0015] To achieve the above object, the present invention provides the following technical solution: a high-precision processing system for copper tubes of multiple specifications, comprising: Data acquisition and enhancement module: collects raw material parameters (grain size, impurities, size), process timing data (cold drawing speed, temperature curve, cooling rate), and detection data (residual stress, defect coordinates) during the processing of multi-specification copper tubes, improves spatial matching accuracy through B-spline interpolation, and uses conditional adversarial generative networks to generate virtual sample data covering extreme working conditions. The virtual sample data must meet the constraints of axial force balance and Hollomon hardening model, and output enhanced data sets; Simulation verification module: Based on the enhanced data set, the dynamic change of grain boundary migration and dislocation density is simulated through grain evolution-stress coupling simulation, the anisotropic yield strength is calculated, and the physical verification data set is generated; the coordinates and correction coefficients of the high stress area are marked, and the physical verification data set and defect-stress mapping table are output; Predictive modeling module: input physical verification data and defect-stress mapping table, train XGBoost regression model to predict local stress distribution, combine axial force balance to verify and optimize model parameters, and output stress prediction model and stress transfer path thermal map; The optimization and adjustment module inputs the stress prediction model and processing target constraints, identifies the parameter causal effects through the cross-modal causal model, uses the NSGA-II algorithm to generate the Pareto optimal parameter set, dynamically adjusts the cold drawing speed and temperature based on real-time stress data, and outputs the optimal parameter combination and the corresponding dynamic adjustment strategy.

[0016] Real-time control module: Based on the optimal parameters and real-time sensor data, the fuzzy PID controller dynamically adjusts the process parameters (such as triggering the gradient slow cooling mode when the cold drawing speed changes suddenly), and outputs real-time adjustment instructions, process logs and processing quality reports.

[0017] Technical effects and advantages of the present invention: (1) The present invention constructs a multimodal correlation system of raw material-process-testing data, uses B-spline interpolation to align spatiotemporal data, and combines graph convolutional networks (GAT+IGC) to explicitly characterize grain boundary migration (edge ​​weight = grain boundary orientation difference + stress gradient) and dislocation density evolution (node ​​hardness characteristics), thereby achieving cross-scale coupling modeling of microstructure-macro stress and obtaining a more accurate stress prediction model to solve the existing problems of residual stress concentration and dimensional tolerance.

[0018] (2) The present invention establishes a fuzzy PID closed loop driven by digital twins, dynamically analyzes the coupling relationship between geometric specifications and stress fields based on the stress prediction model (update frequency 10 Hz), generates a Pareto parameter set through hybrid agent optimization, and realizes the optimization of the processing technology; when the cold drawing speed changes suddenly, the dynamic path strategy (such as the stress gradient over-threshold switching mode) is used to realize real-time optimization of process parameters to solve the problems of prediction model distortion and response lag. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of the high-precision processing method of copper tubes with multiple specifications. DETAILED DESCRIPTION

[0020] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0021] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0022] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0023] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0024] Example 1, see Figure 1 The present invention provides a high-precision processing method flow chart of a multi-specification copper tube. Figure 1 A high-precision processing method for copper tubes of multiple specifications is shown, comprising: Step 1. Multi-source data acquisition and physical enhancement generation: Collect raw material parameters, process timing data and detection data (actual residual stress data, such as X-ray diffraction residual stress distribution on the surface and cross section), ultrasonic flaw detection defect coordinates) to obtain real sample data; process through B-spline interpolation algorithm and conditional adversarial generation network, and improve the spatial matching accuracy through B-spline interpolation algorithm; expand the data set through finite element simulation, and obtain virtual sample data through conditional adversarial generation network. The virtual sample data and the aligned real sample data constitute an enhanced data set; the virtual sample data must meet the axial force balance and Hollomon hardening model constraints; the loss function of the conditional adversarial generation network includes adversarial loss, axial force balance term and Hollomon hardening model constraints to ensure that the generated data conforms to the laws of mechanics; Explanation: The raw material parameters include chemical composition (such as copper content, percentage of impurity elements, such as Cu≥99.9%, Zn≤0.05%), initial grain size (measured by optical microscope, unit is micron, typical range is 10-50μm), initial hardness (measured by Vickers hardness tester, unit is HV, typical range is 50-100HV). These raw material parameters affect the processing performance and stress distribution of copper tubes; the process timing data is collected by time series sensors (such as encoders, thermocouples) on the processing equipment, including cold drawing speed (unit is m / min, record each pass), processing temperature (unit is ℃, record ambient and mold temperature), lubricant usage (unit is mL / min) and tensile deformation, cooling rate curve; use ultrasonic flaw detector to obtain ultrasonic flaw detection defect coordinates, record the three-dimensional coordinates of the defect and defect type; Explanation: The B-spline interpolation algorithm is a 3rd order B-spline, and the implementation process includes: Calculate the centroids of the two sets of coordinates and translate them so that the centroids coincide; The rotation matrix was optimized using the iterative closest point (ICP) algorithm with an error convergence threshold of 0.01 mm; Project the interpolated defect coordinates onto the stress grid (grid resolution 1mm×1mm×1mm) to ensure spatial consistency; Step 2: Input the enhanced data set, modify the enhanced data set through grain evolution-stress coupling simulation modeling and physical constraint-driven process parameter correction, output the physical verification data set, and mark the high stress area coordinates and correction coefficients; Step 3: Input the physical verification data set and defect-stress mapping table, and build a stress prediction model through the XGBoost regression model; Step 4: Cross-modal causal enhancement hybrid agent optimization: Input stress prediction model and processing target constraints; Generate the optimal parameter combination that meets the processing target constraints based on the multi-objective optimization algorithm (NSGA-II), and dynamically adjust the process parameters based on real-time stress data; Step 5. Real-time control driven by digital twins: Input the optimal parameter combination and real-time sensor data, dynamically adjust the process parameters through the combination of fuzzy logic and PID control, and output real-time process adjustment instructions, process adjustment log information and processing quality reports (deformation distribution, stress uniformity index).

[0025] In a possible embodiment, in step five, a fuzzy PID closed-loop control system driven by a digital twin is established, the optimal parameter combination and real-time sensor data are input, and the coupling relationship between geometric specifications and stress fields is dynamically analyzed based on the stress prediction model; a Pareto parameter set is generated through hybrid agent optimization to achieve optimization of the processing technology; and a dynamic path strategy is adopted when the cold drawing speed changes suddenly.

[0026] What needs to be further explained in the embodiments of the present invention is that a B-spline interpolation algorithm with a local curvature weight factor is used to perform three-dimensional interpolation on the ultrasonic defect coordinates. When the ultrasonic defect coordinates are mapped to the X-ray stress grid, the spatial offset error between the defect location and the stress field is eliminated, and the spatial matching accuracy is improved; when the conditional adversarial generative network is used to generate virtual sample data, extreme working conditions of copper tubes are covered, such as cold drawing speed of 1.0-3.0m / s, temperature of 450-600℃, and stress data under extreme working conditions of copper tubes with a wall thickness of less than 1mm and a diameter-to-thickness ratio greater than 30.

[0027] In a possible embodiment, the conditional adversarial generative network can be replaced by a variational autoencoder, and an axial force balance term and a Hollomon hardening model constraint are added to the variational autoencoder loss function to ensure that the generated data conforms to the laws of mechanics.

[0028] What needs to be further explained in the embodiments of the present invention is that the enhanced data set is used to perform grain evolution-stress coupling simulation modeling, quantify the dynamic effect of grain boundary migration on residual stress, and generate a physical verification data set containing grain boundary migration effect data; The grain boundary migration impact data include dynamic changes in grain boundary position, grain size and dislocation density, and corresponding residual stress distribution; The cellular automaton model is used for simulation modeling, the grain unit state is defined as the orientation angle θ and the dislocation density ρ, the grain boundary migration rate is calculated based on the Moore neighborhood rule, and the grain orientation field is dynamically updated; the grain boundary migration rate reflects the influence of the dynamic evolution of the grain on the residual stress; According to the grain size d and dislocation density ρ, the anisotropic yield strength is calculated by the following formula, and the simulated residual stress distribution is obtained based on the anisotropic yield strength: ; in, is the matrix strength of the copper tube, G represents the shear modulus, k is the grain boundary strengthening coefficient, which is calibrated by nanoindentation experiment, and δ is the dislocation strengthening coefficient, which is obtained based on the Taylor relationship of dislocation density ρ; usually ; k, , G and b (Burgess vector) are material constants; they help simulate the microscopic behavior of materials in copper tube processing by combining the contributions of dislocation hardening and grain refinement to strength; The specific steps are as follows: 30 measurement points are selected on the surface of the copper tube in an area of ​​1cm×1cm, the loading rate is 0.05mN / s, the indentation depth is 200nm, the measurement points cover the grain boundary and the intragranular area, the k value is calculated from the difference between the hardness near the grain boundary and the hardness inside the grain, and the average value is taken to obtain the grain boundary strengthening coefficient; The generated simulated residual stress distribution is compared with the actual residual stress data in the enhanced data set (such as X-ray residual stress distribution) to verify the simulation accuracy; if differences are found, the abnormal parts in the enhanced data set are identified and corrected.

[0029] In a possible embodiment, the cellular automaton can be replaced by a phase field model, which requires the definition of phase field variables (such as grain orientation field) and evolution equations (such as Allen-Cahn equations), which is suitable for continuous medium simulation.

[0030] What needs to be further explained in the embodiment of the present invention is the process parameter correction driven by physical constraints: the proportion of the circumferential tensile stress area in the simulated residual stress distribution is calculated in real time. If it exceeds a preset value, such as 40%, the sequential quadratic programming is triggered to optimize and adjust the cold drawing speed v and temperature T. The objective function is: ; in, is the proportion of circumferential tensile stress area, indicating the proportion of abnormal area of ​​circumferential tensile stress of copper tube; is the deviation between the cold drawing speed and the initial speed, is the deviation of temperature from the initial temperature; The defect-stress mapping table with the coordinates of the high stress area and the correction coefficient marked based on the objective function optimization results is as follows: The objective function reduces the proportion of circumferential tensile stress area by optimizing the cold drawing speed and temperature; the area in the simulated residual stress distribution after optimization that still exceeds the threshold (the threshold is set to 20% in the embodiment of the present invention) is identified as a high stress area, and its coordinates and the corresponding speed and temperature correction coefficients are recorded to generate a defect-stress mapping table.

[0031] In a possible embodiment, sequential quadratic programming can be replaced by particle swarm optimization (PSO), the fitness function is defined as the objective function, the population size is ≥50, and the number of iterations is ≥200.

[0032] Explanation: The objective function tries to reduce the proportion of circumferential tensile stress area, and at the same time, tries to keep the changes in speed and temperature as small as possible. However, the change in speed is less penalized than the change in temperature because its weight is 0.1, while the weight of temperature is 1. The purpose of the objective function is to find an optimal cold drawing speed and temperature that can reduce the tensile stress problem without causing too much parameter change, especially temperature change. It is more inclined to solve the problem of large proportion of circumferential tensile stress area by adjusting the speed.

[0033] It needs to be further explained in the embodiment of the present invention that building a stress prediction model includes: The copper tube geometry is converted into a graph structure, where nodes are discrete grid points, and features include position coordinates, real-time hardness, and local cold drawing speed; edges are defined as connections between adjacent nodes, and edge weights are calculated based on adjacent node stress gradients and grain boundary orientation differences; In one possible embodiment, the edge weight is ;in, represents the circumferential stress gradient, is the grain orientation angle, which is used to describe the grain orientation arc; It indicates the grain boundary orientation difference, reflecting the difference in the orientation curvature of adjacent grains; Explanation: The edge weight coefficient is set based on the contribution of the grain boundary orientation difference to stress transfer. For example, the contribution of the grain boundary orientation difference to stress transfer is 30%-50%, so 0.5 is taken to balance the influence of the gradient and orientation difference; By combining graph convolutional networks (GAT+IGC), cross-scale coupled modeling of grain boundary migration and dislocation density evolution is achieved: the effect of grain boundary sliding on the stress of adjacent nodes is calculated, the grain boundary misorientation in the edge weight is directly related to the stress gradient, and the node hardness characteristics are related to the dislocation density evolution; the graph structure is updated regularly to reflect the change in edge weight caused by grain coarsening; incremental graph convolution (IGC) is used to manage computational efficiency, and multi-head graph convolution (GAT) is used to calculate the effect of grain boundary sliding on the stress of adjacent nodes; Train the XGBoost regression model to predict the stress value at the node using node features and edge weights as input; use a physical verification data set to ensure axial force balance verification; during the training process, the loss function includes mean square error and axial force balance penalty terms, and the hyperparameters of the stress prediction model are optimized using the Bayesian method; The post-processing model predicts that the proportion of the circumferential tensile stress area does not exceed 20%, and the regional growing algorithm is used to correct the excessive area.

[0034] It needs to be further explained in the embodiment of the present invention that the training process of the stress prediction model includes: Pre-training using finite element simulation data verified by axial force balance to ensure the axial force balance term ,in is the predetermined tolerance; Fine-tuning using experimental data, the loss function of the stress prediction model includes mean square error and axial force balance penalty terms , To regulate the parameters, hyperparameters were adjusted through Bayesian optimization; The post-processing uses the regional growing algorithm to ensure that the circumferential tensile stress area does not exceed 20% and correct the excessive area; In the region growing algorithm, the threshold setting and iteration rules are as follows: Seed point selection: The grid point with the maximum circumferential tensile stress, with coordinates (x max ,y max ) as the initial seed point. If there are multiple maximum points, the geometric center point is selected; Neighborhood expansion condition: Traverse the Moore neighborhood (8 neighborhoods) of the seed point. If the circumferential tensile stress value σ of the adjacent node θ >0.9σ threshold (σ threshold is a preset threshold, such as 200MPa), then the node is included in the area to be corrected; Iteration termination condition: When the number of newly added nodes in three consecutive iterations is less than 1% of the total number of nodes (or the absolute number is ≤ 5), the expansion is terminated to avoid overfitting; Correction strategy: For nodes in the exceeded area, adjust the cold drawing speed and temperature according to the correction coefficients marked in the defect-stress mapping table, specifically: If σ θ ∈[0.9σ threshold , σ threshold ], the cold drawing speed is reduced by 5%; If σ θ >σ threshold σ, the cold drawing speed is reduced by 10% and the temperature is increased by 20℃ to relieve local stress concentration.

[0035] Technical basis: Threshold 0.9σ threshold The setting is based on the finite element simulation results and can cover 90% of the potential risk areas of exceeding the standard; The iteration termination condition is verified through engineering experiments, balancing the correction efficiency and accuracy; The correlation between the correction coefficient and the process parameters (speed, temperature) was calibrated through a multi-objective optimization experiment, and the proportion of the circumferential tensile stress area did not exceed 20%.

[0036] It needs to be further explained in the embodiment of the present invention that the process of obtaining the optimal parameter combination includes: Construct a cross-modal causal model, input the prediction results of the stress prediction model and the processing target constraints (maximum deformation <0.05mm, circumferential stress, axial stress), identify the causal effect of process parameters on stress distribution through causal inference methods, and generate a causal effect diagram; Based on the cause-effect diagram, the goal is set to minimize stress concentration and deformation, and the NSGA-II algorithm is used to generate the Pareto optimal parameter set; Combined with real-time stress data, the real-time stress concentration is calculated to trigger process parameter adjustments; Output the optimal parameter combination and switching strategy.

[0037] Summary: Through multi-source data enhancement and microstructure evolution modeling, the problem of process parameters relying on experience and being difficult to cover extreme working conditions in traditional copper tube processing is solved; specifically, the following technical means are included: the B-spline interpolation algorithm with local curvature weight factor is used to eliminate defect positioning errors, and the conditional adversarial generation network is combined to generate virtual data covering extreme working conditions, and mechanical constraints are used to ensure that the generated data conforms to actual laws; based on cellular automata, the grain boundary migration and dislocation density evolution are dynamically simulated, and the anisotropic yield strength formula is used to quantify the grain refinement and dislocation strengthening effects. The process parameter optimization is triggered by the threshold of the circumferential tensile stress area ratio, and the cold drawing speed is adjusted preferentially to reduce the influence of temperature fluctuations, so as to achieve high-precision parameter optimization for thin-walled tubes (wall thickness less than 1 mm) and under extreme working conditions; the fuzzy PID controller is combined to adjust the cold drawing speed and temperature in real time, and the causal effect of process parameters on stress distribution is extracted based on the cross-modal causal model, and the Pareto optimal parameter set is generated by a multi-objective optimization algorithm.

[0038] Embodiment 2: The difference between the embodiment of the present invention and embodiment 1 is that the method further includes a stress prediction model performance verification step: calculating the stress prediction model adaptation index through the processing quality report and the process adjustment log information, and if the stress prediction model adaptation index is lower than the threshold, triggering an early warning and updating the stress prediction model; specifically including: Extract control instruction data from process adjustment logs, analyze instruction delays and execution deviations, and eliminate processing quality data corresponding to abnormal control instructions; Extract the standard deviation of deformation distribution and the average value of stress uniformity index from the processing quality report, and extract the average value of adjustment frequency and adjustment amplitude per unit time based on the process adjustment log after eliminating abnormal data; The processing quality fluctuation factor reflecting the degree of fluctuation of processing quality is calculated; the process adjustment stability factor reflecting the stability and effectiveness of process adjustment is calculated, and the stress prediction model adaptability index is calculated through the nonlinear combination of the processing quality fluctuation factor and the process adjustment stability factor.

[0039] It needs to be further explained in the embodiment of the present invention that the stress prediction model adaptation index is obtained in the following manner: Step S11: Calculate the processing quality fluctuation factor : ; in, Indicates the actual deformation of the copper tube. Indicates the standard deviation of the benchmark deformation (historical data statistics), represents the benchmark stress uniformity index, and S represents the actual stress uniformity index. The exponential function is introduced to enhance the sensitivity of S. When S is lower than The fluctuation factor is significantly improved, which more accurately reflects the deterioration of stress distribution; Step S12: Process adjustment stability factor : ; in, Indicates the actual adjustment frequency of the process, Indicates reasonable adjustment of the frequency threshold. Indicates the actual adjustment range. Indicates the upper limit of the adjustment range allowed; an inverse proportional function is used to suppress excessive adjustment range to avoid system shock caused by frequent and large adjustments; Step S13: Stress prediction model adaptation index : ; in, represents the time attenuation coefficient, Indicates the current time. Indicates the last update time of the model; notifies the addition of a time decay term to adapt the model that has not been updated for a long time to an accelerated exponential decline and enforces periodic verification; square root operations smooth out the impact of stability and avoid interference from extreme values.

[0040] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-precision processing method for copper tubes of multiple specifications, characterized in that: include: Collect raw material parameters, process timing data and test data during the processing of copper tubes of multiple specifications. The test data includes actual residual stress data and ultrasonic flaw detection defect coordinates to obtain real sample data. Through B-spline interpolation algorithm and conditional adversarial generative network processing, virtual sample data is obtained through conditional adversarial generative network. The real sample data and virtual sample data constitute an enhanced data set. The virtual sample data must meet the constraints of axial force balance and Hollomon hardening model. Input the enhanced data set, modify the enhanced data set through grain evolution-stress coupling simulation modeling and physical constraint-driven process parameter correction, output the physical verification data set, and mark the high stress area coordinates and correction coefficients; Input the physical verification data set and defect-stress mapping table, and build a stress prediction model through the XGBoost regression model; Input stress prediction model and machining target constraints; Generate the optimal parameter combination that meets the processing target constraints based on the multi-objective optimization algorithm, and dynamically adjust the process parameters based on real-time stress data; Input the optimal parameter combination and real-time sensor data, combine fuzzy logic with PID control, dynamically adjust the process parameters, and output real-time process adjustment instructions, process adjustment log information and processing quality reports.

2. A high-precision processing method for multi-specification copper tubes according to claim 1, characterized in that: The B-spline interpolation algorithm with local curvature weight factor is used to perform three-dimensional interpolation of ultrasonic defect coordinates. When the ultrasonic defect coordinates are mapped to the X-ray stress grid, the spatial offset error between the defect location and the stress field is eliminated. When the conditional adversarial generative network is used to generate virtual sample data, the extreme working conditions of the copper pipe are covered.

3. A high-precision processing method for multi-specification copper tubes according to claim 1 or 2, characterized in that: Use enhanced data sets to perform grain evolution-stress coupled simulation modeling, quantify the dynamic effect of grain boundary migration on residual stress, and generate a physical verification data set containing grain boundary migration effect data; The grain boundary migration impact data include dynamic changes in grain boundary position, grain size and dislocation density, and corresponding residual stress distribution; The cellular automaton model is used for simulation modeling, the grain unit state is defined as the orientation angle θ and the dislocation density ρ, the grain boundary migration rate is calculated based on the Moore neighborhood rule, and the grain orientation field is dynamically updated; the grain boundary migration rate reflects the influence of the dynamic evolution of the grain on the residual stress; According to the grain size d and dislocation density ρ, the anisotropic yield strength is calculated by the following formula, and the simulated residual stress distribution is obtained based on the anisotropic yield strength: ； in, is the matrix strength of the copper tube, G represents the shear modulus, k is the grain boundary strengthening coefficient, which is calibrated by nanoindentation experiment, δ is the dislocation strengthening coefficient, which is obtained based on the Taylor relationship of dislocation density ρ, and b is the Burgers vector of the crystal; The generated simulated residual stress distribution is compared with the actual residual stress data in the enhanced dataset to verify the simulation accuracy; if differences are found, the abnormal parts in the enhanced dataset are identified and corrected.

4. The high-precision processing method for multi-specification copper tubes according to claim 3 is characterized in that: Process parameter correction driven by physical constraints: Real-time calculation of the proportion of circumferential tensile stress area in the simulated residual stress distribution. If it exceeds the preset value, the sequence quadratic programming is triggered to optimize and adjust the cold drawing speed v and temperature T. The objective function is: ； in, is the proportion of circumferential tensile stress area, indicating the proportion of abnormal area of ​​circumferential tensile stress of copper tube; is the deviation between the cold drawing speed and the initial speed, is the deviation of temperature from the initial temperature; The defect-stress mapping table with the coordinates of the high stress area and the correction coefficient marked based on the objective function optimization results is as follows: The objective function reduces the proportion of circumferential tensile stress area by optimizing the cold drawing speed and temperature; The areas in the simulated residual stress distribution after optimization that still exceed the threshold are identified as high-stress areas, and the coordinates and corresponding velocity and temperature correction coefficients are recorded to generate a defect-stress mapping table.

5. The high-precision processing method for multi-specification copper tubes according to claim 3 is characterized in that: Building a stress prediction model includes: The copper tube geometry is converted into a graph structure, where nodes are discrete grid points, and features include position coordinates, real-time hardness, and local cold drawing speed; edges are defined as connections between adjacent nodes, and edge weights are calculated based on adjacent node stress gradients and grain boundary orientation differences; The edge weight is ;in, represents the circumferential stress gradient, is the grain orientation angle, which is used to describe the grain orientation arc; It indicates the grain boundary orientation difference, reflecting the difference in the orientation curvature of adjacent grains; By combining graph convolutional networks, we can achieve cross-scale coupled modeling to characterize grain boundary migration and dislocation density evolution: calculate the effect of grain boundary slip on the stress of adjacent nodes, directly correlate the grain boundary misorientation in edge weights with stress gradients, and correlate node hardness characteristics with dislocation density evolution; The graph structure is updated regularly to reflect the changes in edge weights caused by grain coarsening; Train an XGBoost regression model to predict stress values ​​at nodes using node features and edge weights as input; use a physical verification dataset to ensure axial force balance verification; During the training process, the loss function includes the mean square error and the axial force balance penalty term, and the hyperparameters of the stress prediction model are optimized by the Bayesian method.

6. The high-precision processing method for copper tubes of multiple specifications according to claim 4 is characterized in that: The training process of the stress prediction model includes: Pre-training using finite element simulation data verified by axial force balance to ensure the axial force balance term ,in is the predetermined tolerance; Fine-tuning using experimental data, the loss function of the stress prediction model includes mean square error and axial force balance penalty terms , hyperparameters are tuned via Bayesian optimization; The post-processing uses the region growing algorithm to correct the areas exceeding the standard.

7. The high-precision processing method for multi-specification copper tubes according to claim 1 is characterized in that: The process of obtaining the optimal parameter combination includes: Construct a cross-modal causal model, input the prediction results of the stress prediction model and the processing target constraints, identify the causal effect of process parameters on stress distribution through causal inference methods, and generate a causal effect diagram; Based on the cause-effect diagram, the goal is set to minimize stress concentration and deformation, and the NSGA-II algorithm is used to generate the Pareto optimal parameter set; Combined with real-time stress data, the real-time stress concentration is calculated to trigger process parameter adjustments; Output the optimal parameter combination and switching strategy.

8. The high-precision processing method for copper tubes of multiple specifications according to claim 1 is characterized in that: The method includes the following steps of verifying the performance of the stress prediction model: calculating the adaptation index of the stress prediction model through the processing quality report and the process adjustment log information; if the adaptation index of the stress prediction model is lower than the threshold, triggering an early warning and updating the stress prediction model.

9. A high-precision processing method for copper tubes of multiple specifications according to claim 8, characterized in that: The stress prediction model adaptation index is obtained as follows: Step S11: Calculate the processing quality fluctuation factor : ； in, Indicates the actual deformation of the copper tube. represents the standard deviation of the reference deformation, represents the benchmark stress uniformity index, and S represents the actual stress uniformity index. The exponential function is introduced to enhance the sensitivity of S. When S is lower than The fluctuation factor is significantly improved, which more accurately reflects the deterioration of stress distribution; Step S12: Process adjustment stability factor : ； in, Indicates the actual adjustment frequency of the process, Indicates reasonable adjustment of the frequency threshold. Indicates the actual adjustment range. Indicates the upper limit of the adjustment range allowed; an inverse proportional function is used to suppress excessive adjustment range to avoid system shock caused by frequent and large adjustments; Step S13: Stress prediction model adaptation index : ； in, represents the time attenuation coefficient, Indicates the current time. Indicates the last update time of the model; notifies the addition of a time decay term to adapt the model that has not been updated for a long time to an accelerated exponential decline and enforces periodic verification; square root operations smooth out stability effects to avoid interference from extreme values.

10. A high-precision processing system for copper tubes of various specifications, applied to the method described in claim 1, characterized in that: include: Data acquisition and enhancement module: collects raw material parameters, process timing data and inspection data of copper tubes of various specifications during processing, improves spatial matching accuracy through B-spline interpolation, and uses conditional adversarial generative networks to generate virtual sample data covering extreme working conditions. The virtual sample data must meet the constraints of axial force balance and Hollomon hardening model, and output enhanced data sets; Simulation verification module: Based on the enhanced data set, the dynamic change of grain boundary migration and dislocation density is simulated through grain evolution-stress coupling simulation, the anisotropic yield strength is calculated, and the physical verification data set is generated; the coordinates and correction coefficients of the high stress area are marked, and the physical verification data set and defect-stress mapping table are output; Predictive modeling module: input physical verification data and defect-stress mapping table, train XGBoost regression model to predict local stress distribution, combine axial force balance to verify and optimize model parameters, and output stress prediction model and stress transfer path thermal map; The optimization and adjustment module inputs the stress prediction model and processing target constraints, identifies the parameter causal effect through the cross-modal causal model, generates the Pareto optimal parameter set using the NSGA-II algorithm, dynamically adjusts the cold drawing speed and temperature based on the real-time stress data, and outputs the optimal parameter combination and the corresponding dynamic adjustment strategy; Real-time control module: Based on the optimal parameters and real-time sensor data, the process parameters are dynamically adjusted through the fuzzy PID controller, and real-time adjustment instructions, process logs and processing quality reports are output.

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