Self-adaptive control method, system and equipment for bus flexible bending process

Abstract

The invention discloses a self-adaptive control method, system and equipment for a bus flexible bending process, and relates to the technical field of self-adaptive control, and the method comprises the steps: uploading a bending processing task, driving a task engine, executing a task interpretation and bending control optimization decision, and determining a preprocessing strategy; the preprocessing strategy is issued to a servo control module to execute bus bending control driving; a monitoring array deployed by a numerical control machine tool is triggered to synchronously perform working condition monitoring and return to a bending monitoring module along with bus bending control driving, one kind of out-of-limit judgment of pressure and friction force is performed, strategy evolution consistency judgment based on a stress flow field and a dislocation density field is performed, and if machining deviation exists, the machining precision is improved; processing regulation and control of the servo control module are executed according to an online regulator; the technical problems that in the prior art, control precision is insufficient and machining deviation is difficult to correct in real time in the bending machining process are solved, and the technical effects that the control precision and stability in the bus bending machining process are improved, and the machining deviation is detected and adjusted in real time are achieved.

Description

Technical Field

[0001] This invention relates to the field of adaptive control technology, specifically to an adaptive control method, system, and equipment for busbar flexible bending process. Background Technology

[0002] During the flexible bending process of busbars, the stress distribution and deformation characteristics of the material are difficult to predict accurately, often resulting in uneven pressure and friction, leading to processing deviations. Traditional control methods struggle to adjust and correct these deviations in real time, making it difficult to maintain consistent bending quality and affecting product precision and stability. Summary of the Invention

[0003] This application provides an adaptive control method, system, and equipment for busbar flexible bending process, which is used to address the technical problems of insufficient control accuracy and difficulty in real-time correction of processing deviations during bending processes in the prior art.

[0004] In view of the above problems, this application provides an adaptive control method, system and equipment for busbar flexible bending process.

[0005] The first aspect of this application provides an adaptive control method for a busbar flexible bending process, the method comprising: Upload the bending processing task, drive the task engine, execute task interpretation and bending control optimization decisions, and determine the pre-processing strategy; send the pre-processing strategy to the servo control module to execute the busbar bending control drive; along with the busbar bending control drive, trigger the monitoring array deployed on the CNC machine tool, synchronously monitor the working condition and send it back to the bending monitoring module, perform a type of limit judgment of pressure and friction, and determine the consistency of strategy evolution based on stress flow field and dislocation density field. If there is a processing deviation, execute the processing adjustment of the servo control module according to the online controller; wherein, the bending monitoring module has an embedded dynamic projection surface based on the dislocation density evolution and flow stress evolution of the pre-processing strategy.

[0006] A second aspect of this application provides an adaptive control system for a busbar flexible bending process, the system comprising: The strategy determination module is used to upload bending processing tasks, drive the task engine, perform task interpretation and bending control optimization decisions, and determine the pre-processing strategy. The drive module is used to send the pre-processing strategy to the servo control module to execute the bus bending control drive. The judgment module is used to trigger the monitoring array deployed on the CNC machine tool along with the bus bending control drive, synchronously monitor the working condition and send it back to the bending monitoring module, perform a type of limit judgment of pressure and friction, and a consistency judgment with the strategy evolution based on stress flow field and dislocation density field. If there is a processing deviation, the processing adjustment of the servo control module is executed according to the online controller. The bending monitoring module has a dynamic projection surface embedded in it based on the dislocation density evolution and flow stress evolution of the pre-processing strategy.

[0007] A third aspect of this application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the adaptive control method for the busbar flexible bending process provided in this application when executing the executable instructions stored in the memory.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application uploads a bending processing task, drives a task engine, performs task interpretation and bending control optimization decisions, and determines a pre-processing strategy. The pre-processing strategy is then sent to the servo control module to execute busbar bending control. Along with the busbar bending control, a monitoring array deployed on the CNC machine tool is triggered to synchronously monitor the working conditions and transmit the data back to the bending monitoring module. This module performs a type of limit judgment for pressure and friction, and a consistency judgment based on the strategy evolution of the stress flow field and dislocation density field. If a processing deviation exists, the servo control module performs processing adjustment according to the online controller. The bending monitoring module embeds a dynamic projection surface based on the dislocation density evolution and flow stress evolution of the pre-processing strategy. This invention solves the technical problems of insufficient control accuracy and difficulty in real-time correction of processing deviations in the bending process in the prior art. By introducing a task engine for processing task interpretation and optimization decisions, and combining the real-time feedback and adjustment mechanism of the servo control module and the bending monitoring module, it achieves the technical effect of improving the control accuracy and stability of the busbar bending process and detecting and adjusting processing deviations in real time. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1A schematic flowchart of the adaptive control method for the busbar flexible bending process provided in the embodiments of this application; Figure 2 A schematic diagram of the adaptive control system structure for the busbar flexible bending process provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0011] Explanation of reference numerals in the attached drawings: Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus Interface 305, Strategy Determination Module 11, Driver Module 12, Decision Module 13. Detailed Implementation

[0012] This application provides an adaptive control method, system, and equipment for busbar flexible bending process. It addresses the technical problems of insufficient control accuracy and difficulty in real-time correction of processing deviations in the bending process in the prior art. By introducing a task engine for processing task interpretation and optimization decision-making, and combining the real-time feedback and adjustment mechanism of the servo control module and the bending monitoring module, it achieves the technical effect of improving the control accuracy and stability of the busbar bending process, and detecting and adjusting processing deviations in real time.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides an adaptive control method for busbar flexible bending process, the method comprising: Step S100: Upload the bending processing task, drive the task engine, execute task interpretation and bending control optimization decisions, and determine the pre-processing strategy.

[0016] In this embodiment, a pre-prepared bending task is first uploaded. The task engine reads the bending task to determine the basic information of the busbar, including material grade, thickness, and die geometry, as well as the bending requirements. Then, a first task interpretation is performed to determine the bending constraints, state elements, and control elements. Based on the aforementioned bending constraints, state elements, and control elements, a second optimization decision is used for iterative optimization. A pre-processing strategy is generated by combining a taboo list addition mechanism with a preset preference level and a taboo list unlocking mechanism with iteration count control.

[0017] Furthermore, the method provided in the application embodiments, which drives the task engine to perform task interpretation and bending control optimization decisions, also includes: The task engine reads the bending processing task, determines the basic information of the busbar and the bending processing requirements, wherein the basic information of the busbar includes at least the material grade, thickness, and mold geometry, and the task engine is an embedded plug-in of the machine tool control platform; by executing the first task interpretation, the bending constraints, state elements and control elements are determined, wherein the control elements are used to drive the machine tool end effector; by executing the second optimization decision, the optimization space is initialized with the bending constraints, state elements and control elements, and a pre-processing strategy is generated through iterative optimization, wherein optimization constraints are performed by adding taboos to a taboo list with a preset degree of preference and unlocking taboos to a taboo list with a preset number of iterations.

[0018] In this embodiment, the bending task is first read through the task engine. The task engine, an embedded plugin of the machine tool control platform, uses natural language processing technology to read the bending task, specifically by semantic parsing and keyword extraction to determine the basic information of the busbar and the bending requirements. The basic information of the busbar includes the material grade, thickness, and die geometry.

[0019] Next, the bending constraints, state elements, and control elements are determined through the first task of interpretation. During this process, the yield strength and elastic modulus parameters of the material are obtained by querying the material property database using the material grade. Then, bending constraints are established based on processing requirements. The primary objective is that the net shape after springback meets tolerance requirements; the first sub-objective is that the maximum equivalent stress during bending is less than the material limit; and the second sub-objective is that the compressive stress distribution in the compression zone is uniform. Subsequently, the state elements, including the bending angle and the optimal stress-strain path, are determined through path planning algorithms. Finally, the control elements are determined through kinematic inversion calculations, including the position trajectory and velocity curve of the bending blade, the dynamic clamping force of the pressure shaft, and the adjustment amount of the auxiliary shaft.

[0020] Subsequently, the second optimization decision is executed, employing a tabu search algorithm to generate a pre-processing strategy. In this process, a multi-dimensional optimization space is constructed using bending constraints, state elements, and control elements. An initial solution set is generated, and the goal achievement degree of each solution is evaluated. The goal achievement degree evaluation uses a multi-index weighted method: the compliance of the net shape after springback with tolerance requirements is calculated as the primary goal achievement degree; the ratio of the maximum equivalent stress to the material limit is calculated as the first sub-goal achievement degree; and the uniformity of compressive stress distribution in the compression zone is calculated as the second sub-goal achievement degree. The three indices are then combined according to preset weights to obtain the total goal achievement degree. During the iterative optimization process, superior solutions are added to the tabu list according to a preset preference level. Simultaneously, the tabu list is unlocked according to a preset number of iterations. After multiple iterations, a pre-processing strategy satisfying all constraints is output.

[0021] Furthermore, the method provided in the application embodiments also includes: The bending constraint conditions include one main objective and at least two sub-objectives. The main objective is that the net shape after springback meets the tolerance requirements. The first sub-objective is that the maximum equivalent stress during the bending process is less than the material limit. The second sub-objective is that the compressive stress distribution in the compression zone is uniform. The state elements include at least the bending angle and the optimal stress-strain path. The control elements include at least the position trajectory and speed curve of the bending blade, the dynamic clamping force of the pressure shaft, and the adjustment amount of the auxiliary shaft.

[0022] In this embodiment of the application, in order to determine the bending constraint conditions, the material database is first queried by material grade to obtain the basic parameters of the material such as elastic modulus and yield strength. Then, the springback amount after processing is predicted based on the standard calculation formula, and the main objective is to ensure that the shape of the workpiece after springback meets the tolerance requirements.

[0023] When establishing sub-objectives, the finite element method was used to calculate the stress field. By constructing a geometric model of the workpiece and setting material parameters, the stress distribution during the bending process was simulated, and the maximum equivalent stress value was extracted. The first sub-objective was established as the maximum equivalent stress during the bending process being less than the material's ultimate strength. Simultaneously, by analyzing the data collected by the pressure sensor, the pressure distribution uniformity index of the pressure-bearing area was calculated, and the second sub-objective was established as the uniform distribution of compressive stress in the pressure-bearing area.

[0024] When determining the state elements, a parameter optimization method is used for path planning. By adjusting the bending angle parameter, multiple sets of finite element simulations are performed, and the stress-strain distribution results under different parameters are compared. The processing path that satisfies all constraints is selected as the optimal stress-strain path, which is then finalized. The state elements include the bending angle and the optimal stress-strain path.

[0025] When determining the control elements, a trajectory planning method is used to calculate the motion parameters. Based on the optimal stress-strain path, the motion trajectory of the bending blade is derived in reverse, generating a specific sequence of position trajectory points and corresponding velocity curves. Simultaneously, the required dynamic clamping force of the pressure shaft is calculated through mechanical equilibrium analysis, and the adjustment amount of the auxiliary shaft is determined based on the deflection compensation principle, thus completing the determination of all control elements.

[0026] Step S200: The pre-processing strategy is sent to the servo control module to execute the bus bending control drive.

[0027] Furthermore, the method provided in the application embodiments also includes: The pre-processing strategy is decomposed into minimum execution units and labeled with spatiotemporal codes, and then sent to the servo control module, wherein the servo control module is connected to the end effector of the machine tool. According to the pre-processing strategy, a dynamic projection surface is generated based on the dislocation density evolution and flow stress evolution under the bending control cycle, and then sent to the bending monitoring module. The servo control module and the bending monitoring module are established to have the same frequency constraint.

[0028] In this embodiment, when the pre-machining strategy is sent to the servo control module to execute the bus bending control drive, a motion control discretization method is used to decompose the pre-machining strategy into the smallest execution unit. By dividing the continuous machining path into discrete control instruction units according to the control cycle, a spatiotemporal code containing spatial coordinates and a timestamp is added to each instruction unit. The processed instruction sequence is transmitted to the servo control module via industrial Ethernet. The servo control module maintains a real-time control connection with the machine tool end effector to ensure the accurate execution of machining instructions.

[0029] When generating the dynamic projection surface according to the pre-processing strategy, finite element simulation technology is used. Based on material mechanical parameters and with the bending control cycle as the calculation step size, the evolution of dislocation density and flow stress at each processing moment is calculated through numerical simulation. The calculated evolution data is matched with the processing path to generate a dynamic projection surface that updates with the processing progress. This dynamic projection surface is transmitted to the bending monitoring module as a monitoring reference.

[0030] To establish a synchronous constraint between the servo control module and the bending monitoring module, a clock synchronization method is adopted. A master clock device is configured to distribute synchronization signals to the servo control module and the bending monitoring module, so that the control cycle of the servo control module is consistent with the sampling cycle of the bending monitoring module, ensuring that the execution of control commands and the acquisition of working condition data have a unified time reference.

[0031] The above process completes the execution of the busbar bending control drive.

[0032] Step S300: Driven by the bending control of the busbar, the monitoring array deployed on the CNC machine tool is triggered to simultaneously monitor the working condition and transmit the data back to the bending monitoring module. The module performs a type of limit judgment on pressure and friction, and a consistency judgment on the strategy evolution based on the stress flow field and dislocation density field. If there is a processing deviation, the processing control module of the servo control module is executed according to the online controller. The bending monitoring module is embedded with a dynamic projection surface based on the dislocation density evolution and flow stress evolution of the pre-processing strategy.

[0033] In this embodiment, during the busbar bending control drive process, the monitoring array deployed on the CNC machine tool starts working synchronously. This monitoring array consists of a high-frequency ultrasonic probe integrated into the mold, mechanical sensors mounted on the bending cutter and pressure shaft, and fiber optic grating sensors laid on the surface of the busbar. The real-time operating data collected by the monitoring array is transmitted to the bending monitoring module via industrial Ethernet.

[0034] In the bending monitoring module, the pressure and friction data collected by the mechanical sensors are first subjected to an out-of-limit judgment. This judgment process identifies abnormal working conditions that exceed the allowable range by comparing real-time monitoring values ​​with preset safety thresholds. Simultaneously, the stress distribution data collected by the fiber optic grating sensor is spatially vectorized to construct a stress flow field; and the full waveform data of acoustic emission collected by the high-frequency ultrasonic probe is subjected to amplitude, energy, and frequency feature extraction to construct a dislocation density field.

[0035] Subsequently, the bending monitoring module matches and compares the stress flow field and dislocation density field with the embedded dynamic projection surface, and performs a strategy evolution consistency judgment. When a limit violation judgment or strategy evolution consistency judgment identifies a processing deviation, it sends a feedback command to the servo control module, and makes a feedback decision when the flow passes through the line controller to complete the processing control.

[0036] Furthermore, in the method provided in the application embodiments, the monitoring array deployed on the CNC machine tool, triggered by the busbar bending control drive, further includes: A monitoring array is deployed in a CNC machine tool. The monitoring array consists of a high-frequency ultrasonic probe integrated into the mold, a mechanical sensor integrated into the bending tool and the pressure shaft, and a fiber optic grating sensor integrated into the surface of the busbar. With the control and drive of the servo control module, the monitoring array is synchronously triggered to monitor the bending process and determine the bending monitoring data.

[0037] In this embodiment, a monitoring array is deployed in the CNC machine tool. The monitoring array consists of a high-frequency ultrasonic probe integrated into the mold, a mechanical sensor integrated into the bending tool and the blank holder shaft, and a fiber Bragg grating sensor integrated into the surface of the busbar. The high-frequency ultrasonic probe is used to collect microscopic deformation signals within the material; the mechanical sensor is used to acquire dynamic force parameters during the processing in real time; and the fiber Bragg grating sensor is used to measure the surface strain distribution.

[0038] When the servo control module sends a control drive signal, it synchronously triggers the monitoring array to start data acquisition. Specifically, the high-frequency ultrasonic probe captures the full acoustic emission waveform reflecting changes in the material's internal microstructure; the mechanical sensor collects real-time data on the pressure and friction between the bending tool and the blank holder during processing; and the fiber optic grating sensor continuously monitors the strain distribution on the busbar surface. The acoustic signals, mechanical parameters, and strain information acquired by these three types of sensors are time-aligned and spatially matched to ultimately generate bending monitoring data containing complete processing characteristics.

[0039] Furthermore, in the method provided in the application embodiments, before the processing control module is executed according to the online controller, the construction of the online controller further includes: Based on the constitutive relationship of the busbar bending, a linear control relationship is extracted. In this process, a linear surrogate method is used to perform piecewise linear transformation on the nonlinear relationship. Based on the linear control relationship, an online controller is trained under supervision. The online controller is a peripheral plug-in at the communication midpoint between the bending monitoring module and the servo control module.

[0040] In this embodiment, a linear proxy approach is used to mine linear control relationships based on the constitutive relation of the busbar bending. First, a systematic bending experiment is conducted using a material testing machine, setting different combinations of bending angles, speeds, and clamping forces to collect corresponding stress-strain data throughout the entire process, establishing a complete constitutive relation database. Then, a piecewise linear fitting algorithm is used, based on curvature analysis, to identify inflection points in the constitutive relation curves, dividing the continuous nonlinear relationship into multiple approximately linear segments. Within each segment, the slope and intercept parameters of the linear equations are calculated using the least squares method, resulting in a piecewise linear control relationship composed of multiple linear equations.

[0041] Subsequently, based on the obtained piecewise linear control relationship, a supervised training method was used to construct an online controller. In this process, optimal process parameter label data was obtained through experimental design, and orthogonal experimental design was used to arrange process parameter combinations. Finite element simulation was used to calculate indicators such as springback and stress distribution uniformity under each parameter combination, and the process parameter with the best comprehensive index was selected as the training label. Using the characteristic parameters of the piecewise linear control relationship as input and the optimal process parameter as the output label, the weight parameters of the online controller were iteratively optimized using a gradient descent algorithm to complete the training process of the online controller. The trained online controller was deployed as a peripheral plug-in in the communication link between the bending monitoring module and the servo control module.

[0042] The online controller performs real-time control functions at the communication midpoint. When the bending monitoring module transmits real-time monitoring data, the online controller identifies the working condition and matches the sections according to the built-in piecewise linear control relationship. It calculates the correction amount of the control parameters using a linear interpolation method, generates the adjusted control command, and sends it to the servo control module via the fieldbus to achieve closed-loop control of the machining process.

[0043] Furthermore, the method provided in the application embodiments also includes: The linear control relationship includes a first relationship between dislocation state and pressing speed, a second relationship between work hardening and speed change, and a third relationship between stress distribution and mold state, wherein the mold state is defined by micro-swing angle and mold contact posture.

[0044] In this embodiment, when establishing the first relationship between dislocation state and compression speed, a linear regression analysis method is used to process the experimental data. The amplitude characteristics of the acoustic emission signal collected by a high-frequency ultrasonic probe are used as a quantitative index of the dislocation state. Simultaneously, the compression speed values ​​fed back by the servo motor encoder are recorded. The dislocation state data and compression speed data are imported into statistical analysis software for linear regression calculation to obtain the coefficients of the linear equation between the dislocation state and the compression speed, thus completing the establishment of the first relationship.

[0045] To establish the second relationship between work hardening and speed variation, a controlled variable method was used in the experimental design. The change in material yield strength was measured using a mechanical sensor as an indicator of the degree of work hardening. Simultaneously, speed adjustment command data stored in the servo control system was recorded. While keeping other process parameters constant, the speed variation amplitude was adjusted, and the corresponding work hardening data was recorded. Through data fitting, a linear relationship expression between work hardening and speed variation was obtained, thus establishing the second relationship.

[0046] To establish the third relationship between stress distribution and mold condition, a multiple linear regression method was used to analyze the influence of multiple parameters. Stress distribution cloud maps were acquired using a fiber optic grating sensor network, and uniformity indices were calculated. Simultaneously, mold micro-swing angle data measured by an angle sensor and mold contact posture parameters acquired by an industrial vision device were collected. Using the mold micro-swing angle and mold contact posture as independent variables and stress distribution uniformity as the dependent variable, a mathematical expression for the third relationship was obtained through multiple linear regression, thus completing the establishment of the third relationship.

[0047] Furthermore, in the method provided in the application embodiments, if there is a processing deviation, the processing adjustment of the servo control module based on the online controller further includes: The bending monitoring data is read, and the pressure and friction from the force sensor are judged to exceed the limit to determine a type of bending processing state. The stress data from the fiber optic grating sensor is spatially vectorized to determine the stress flow field. The amplitude, energy, and frequency features of the acoustic emission waveform from the high-frequency ultrasonic probe are extracted to determine the dislocation density field. The stress flow field and dislocation density field are overlaid onto the dynamically updated projection surface, and a type of bending processing state is determined based on consistency. Based on the type of bending processing state and the type of bending processing state, a feedback command is sent to the servo control module to make feedback decisions when the data flows through the online controller.

[0048] In this embodiment, bending monitoring data is first read, and the pressure data and friction data collected by the force sensor are judged to exceed the limit by using the threshold comparison method. The real-time pressure value is compared with the preset pressure safety threshold point by point, and the real-time friction value is compared with the preset friction safety threshold in real time. When any parameter is detected to continuously exceed the threshold range, a type of bending processing state is determined to be an abnormal state.

[0049] Subsequently, during stress field analysis, the stress distribution data collected by the fiber optic grating sensor was spatially vectorized using a cubic spline interpolation algorithm. The discrete stress measurement points were reconstructed into a continuous stress distribution surface, and stress flow field data with intensity and direction characteristics were obtained through vector synthesis calculation.

[0050] Subsequently, during dislocation analysis, the full acoustic emission waveform acquired by the high-frequency ultrasonic probe was extracted using fast Fourier transform, and the time-domain waveform was converted into frequency-domain features. Characteristic frequency components such as grain slip and crack initiation were identified using mode separation technology, and the dislocation density field distribution was obtained based on quantitative analysis of the characteristic parameters.

[0051] Next, during the coverage processing of the stress flow field and dislocation density field, a grid matching method is used to map the actual monitoring data onto the dynamic projection surface. The dynamic projection surface is divided into a uniform 0.5mm × 0.5mm grid, with each grid node storing the theoretical stress and dislocation density values ​​calculated based on the pre-processing strategy. Through spatial coordinate matching, the actual monitored stress flow field and dislocation density field data are precisely mapped to the grid node positions, completing the data coverage process. In the consistency judgment stage, the correlation coefficient calculation method is used to evaluate the degree of matching between the monitoring data and the theoretical data. In this process, the Pearson correlation coefficient between the actual stress value and the theoretical stress value for each grid node is calculated, along with the Pearson correlation coefficient between the actual dislocation density value and the theoretical dislocation density value. A consistency judgment threshold of 0.85 is set. When both correlation coefficients are greater than 0.85, it is considered completely consistent; when either correlation coefficient is between 0.70 and 0.85, it is considered partially consistent; and when either correlation coefficient is less than 0.70, it is considered inconsistent. Based on this, the second type of bending processing state is determined.

[0052] Finally, based on the first and second types of bending processing states, feedback commands are sent to the servo control module. During this process, a decision tree algorithm is used to comprehensively evaluate the first and second types of bending processing states. Specifically, it first determines whether the first type of bending processing state is abnormal; if the pressure or friction exceeds the limit, an emergency stop command is directly triggered. If the first type of state is normal, it is processed according to the consistency level of the second type of state: if completely consistent, the original parameters are maintained; if partially consistent, a fine-tuning command is output; if inconsistent, a large adjustment command is output, generating corresponding feedback commands.

[0053] During the feedback decision-making stage, the online controller receives feedback instructions and processes them in real time, converting the feedback instructions into specific control parameter adjustment amounts. It then generates the final control instructions through linear interpolation and sends these instructions to the servo control module via the fieldbus, completing the closed-loop control of the machining process.

[0054] In summary, the embodiments of this application have at least the following technical effects: This application uploads a bending processing task, drives a task engine, performs task interpretation and bending control optimization decisions, and determines a pre-processing strategy. The pre-processing strategy is then sent to the servo control module to execute busbar bending control. Along with the busbar bending control, a monitoring array deployed on the CNC machine tool is triggered to synchronously monitor the working conditions and transmit the data back to the bending monitoring module. This module performs a type of limit judgment for pressure and friction, and a consistency judgment based on the strategy evolution of the stress flow field and dislocation density field. If a processing deviation exists, the servo control module performs processing adjustment according to the online controller. The bending monitoring module embeds a dynamic projection surface based on the dislocation density evolution and flow stress evolution of the pre-processing strategy. This invention solves the technical problems of insufficient control accuracy and difficulty in real-time correction of processing deviations in the bending process in the prior art. By introducing a task engine for processing task interpretation and optimization decisions, and combining the real-time feedback and adjustment mechanism of the servo control module and the bending monitoring module, it achieves the technical effect of improving the control accuracy and stability of the busbar bending process and detecting and adjusting processing deviations in real time.

[0055] Example 2, based on the same inventive concept as the adaptive control method for the busbar flexible bending process in the foregoing examples, such as... Figure 2 As shown, this application provides an adaptive control system for busbar flexible bending technology. The system and method embodiments in this application are based on the same inventive concept. The system includes: The strategy determination module 11 is used to upload bending processing tasks, drive the task engine, perform task interpretation and bending control optimization decisions, and determine the pre-processing strategy; the drive module 12 is used to send the pre-processing strategy to the servo control module to execute the bus bending control drive; the judgment module 13 is used to trigger the monitoring array deployed on the CNC machine tool along with the bus bending control drive, synchronously monitor the working condition and send it back to the bending monitoring module, perform a type of limit judgment of pressure and friction, and a consistency judgment with the strategy evolution based on the stress flow field and dislocation density field. If there is a processing deviation, the processing control module of the servo control module is executed according to the online controller; wherein, the bending monitoring module is embedded with a dynamic projection surface based on the dislocation density evolution and flow stress evolution of the pre-processing strategy.

[0056] Furthermore, the system is also used to implement the following functions: The task engine reads the bending processing task, determines the basic information of the busbar and the bending processing requirements, wherein the basic information of the busbar includes at least the material grade, thickness, and mold geometry, and the task engine is an embedded plug-in of the machine tool control platform; by executing the first task interpretation, the bending constraints, state elements and control elements are determined, wherein the control elements are used to drive the machine tool end effector; by executing the second optimization decision, the optimization space is initialized with the bending constraints, state elements and control elements, and a pre-processing strategy is generated through iterative optimization, wherein optimization constraints are performed by adding taboos to a taboo list with a preset degree of preference and unlocking taboos to a taboo list with a preset number of iterations.

[0057] Furthermore, the system is also used to implement the following functions: The bending constraint conditions include one main objective and at least two sub-objectives. The main objective is that the net shape after springback meets the tolerance requirements. The first sub-objective is that the maximum equivalent stress during the bending process is less than the material limit. The second sub-objective is that the compressive stress distribution in the compression zone is uniform. The state elements include at least the bending angle and the optimal stress-strain path. The control elements include at least the position trajectory and speed curve of the bending blade, the dynamic clamping force of the pressure shaft, and the adjustment amount of the auxiliary shaft.

[0058] Furthermore, the system is also used to implement the following functions: The pre-processing strategy is decomposed into minimum execution units and labeled with spatiotemporal codes, and then sent to the servo control module, wherein the servo control module is connected to the end effector of the machine tool. According to the pre-processing strategy, a dynamic projection surface is generated based on the dislocation density evolution and flow stress evolution under the bending control cycle, and then sent to the bending monitoring module. The servo control module and the bending monitoring module are established to have the same frequency constraint.

[0059] Furthermore, the system is also used to implement the following functions: A monitoring array is deployed in a CNC machine tool. The monitoring array consists of a high-frequency ultrasonic probe integrated into the mold, a mechanical sensor integrated into the bending tool and the pressure shaft, and a fiber optic grating sensor integrated into the surface of the busbar. With the control and drive of the servo control module, the monitoring array is synchronously triggered to monitor the bending process and determine the bending monitoring data.

[0060] Furthermore, the system is also used to implement the following functions: Based on the constitutive relationship of the busbar bending, a linear control relationship is extracted. In this process, a linear surrogate method is used to perform piecewise linear transformation on the nonlinear relationship. Based on the linear control relationship, an online controller is trained under supervision. The online controller is a peripheral plug-in at the communication midpoint between the bending monitoring module and the servo control module.

[0061] Furthermore, the system is also used to implement the following functions: The linear control relationship includes a first relationship between dislocation state and pressing speed, a second relationship between work hardening and speed change, and a third relationship between stress distribution and mold state, wherein the mold state is defined by micro-swing angle and mold contact posture.

[0062] Furthermore, the system is also used to implement the following functions: The bending monitoring data is read, and the pressure and friction from the force sensor are judged to exceed the limit to determine a type of bending processing state. The stress data from the fiber optic grating sensor is spatially vectorized to determine the stress flow field. The amplitude, energy, and frequency features of the acoustic emission waveform from the high-frequency ultrasonic probe are extracted to determine the dislocation density field. The stress flow field and dislocation density field are overlaid onto the dynamically updated projection surface, and a type of bending processing state is determined based on consistency. Based on the type of bending processing state and the type of bending processing state, a feedback command is sent to the servo control module to make feedback decisions when the data flows through the online controller.

[0063] Example 3: Based on the inventive concept of the adaptive control method for the busbar flexible bending process in the foregoing examples, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of any of the methods described in Example 1 above.

[0064] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0065] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An adaptive control method for busbar flexible bending process, characterized in that, The method includes: Upload bending processing tasks, drive the task engine, execute task interpretation and bending control optimization decisions, and determine pre-processing strategies; The pre-processing strategy is sent to the servo control module to execute the busbar bending control drive; Driven by the bending control of the busbar, the monitoring array deployed on the CNC machine tool is triggered to simultaneously monitor the working condition and transmit it back to the bending monitoring module. The module performs a type of limit judgment on pressure and friction, and a consistency judgment on the strategy evolution based on the stress flow field and dislocation density field. If there is a processing deviation, the processing control module of the servo control module is executed according to the online controller. The bending monitoring module is embedded with a dynamic projection surface based on the evolution of dislocation density and flow stress under a pre-processing strategy.

2. The adaptive control method for the busbar flexible bending process as described in claim 1, characterized in that, The task engine drives task interpretation and bend control optimization decisions, including: The task engine reads the bending processing task and determines the basic information of the busbar and the bending processing requirements. The basic information of the busbar includes at least the material grade, thickness and mold geometry. The task engine is an embedded plug-in of the machine tool control platform. By performing the first task of interpretation, the bending constraint conditions, state elements and control elements are determined, wherein the control elements are used to drive the machine tool end effector; By executing the second optimization decision, the optimization space is initialized with bending constraints, state elements and control elements. Through iterative optimization, a pre-processing strategy is generated, wherein optimization constraints are performed by adding taboos to a taboo list with a preset degree of preference and unlocking taboos to a taboo list with a preset number of iterations.

3. The adaptive control method for the busbar flexible bending process as described in claim 2, characterized in that, The bending constraint condition includes a main objective and at least two sub-objectives. The main objective is that the net shape after springback meets the tolerance requirements. The first sub-objective is that the maximum equivalent stress during the bending process is less than the material limit. The second sub-objective is that the compressive stress distribution in the compression zone is uniform. The state elements include at least the bending angle and the optimal stress-strain path; The control elements include at least the position trajectory and speed curve of the bending blade, the dynamic clamping force of the pressure shaft, and the adjustment amount of the auxiliary shaft.

4. The adaptive control method for the busbar flexible bending process as described in claim 1, characterized in that, The pre-processing strategy is decomposed into minimum execution units and labeled with spatiotemporal codes, and then sent to the servo control module, wherein the servo control module is connected to the end effector of the machine tool. Based on the pre-processing strategy, a dynamic projection surface is generated by the evolution of dislocation density and flow stress under the bending control cycle, and then sent to the bending monitoring module. Establish a frequency synchronization constraint between the servo control module and the bending monitoring module.

5. The adaptive control method for the busbar flexible bending process as described in claim 1, characterized in that, The monitoring array deployed on the CNC machine tool is triggered by the busbar bending control drive, including: A monitoring array is deployed in a CNC machine tool, wherein the monitoring array consists of a high-frequency ultrasonic probe integrated into the mold, a mechanical sensor integrated into the bending tool and the pressure shaft, and a fiber optic grating sensor integrated into the surface of the busbar. Driven by the servo control module, the monitoring array is synchronously triggered to monitor the bending process and determine the bending monitoring data.

6. The adaptive control method for the busbar flexible bending process as described in claim 5, characterized in that, Before the processing control of the servo control module is executed according to the online controller, the construction of the online controller includes: Based on the constitutive relationship of the busbar bending, linear control relationships are extracted. In this process, a linear surrogate method is used to perform piecewise linear transformation on the nonlinear relationships. Based on the linear control relationship, a supervised training online controller is performed, wherein the online controller is a peripheral plug-in at the communication midpoint between the bending monitoring module and the servo control module.

7. The adaptive control method for the busbar flexible bending process as described in claim 6, characterized in that, The linear control relationship includes a first relationship between dislocation state and pressing speed, a second relationship between work hardening and speed change, and a third relationship between stress distribution and mold state, wherein the mold state is defined by micro-swing angle and mold contact posture.

8. The adaptive control method for the busbar flexible bending process as described in claim 7, characterized in that, If processing deviations exist, the processing adjustment of the servo control module is executed according to the online controller, including: Read the bending monitoring data, determine the over-limit judgment of the pressure and friction from the force sensor, and identify a type of bending processing state; Spatial vectorization processing is performed on stress component data from fiber optic grating sensors to determine the stress flow field. Amplitude, energy, and frequency characteristics are extracted from the full waveform of acoustic emission from a high-frequency ultrasonic probe to determine the dislocation density field. The stress flow field and dislocation density field are overlaid onto the dynamically updated projection surface, and the two types of bending processing states are determined based on consistency. Based on the first type of bending processing state and the second type of bending processing state, a feedback command is sent to the servo control module, and a feedback decision is made when the data flows through the online controller.

9. An adaptive control system for busbar flexible bending process, characterized in that, The system is used to execute the adaptive control method for the busbar flexible bending process as described in any one of claims 1-8, and the system includes: The strategy determination module is used to upload bending processing tasks, drive the task engine, perform task interpretation and bending control optimization decisions, and determine the pre-processing strategy. The drive module is used to send the pre-processing strategy to the servo control module to execute the bus bending control drive; The judgment module is used to control and drive the bending of the busbar, trigger the monitoring array deployed on the CNC machine tool, and simultaneously monitor the working condition and send it back to the bending monitoring module to make a type of limit judgment of pressure and friction, and a consistency judgment of strategy evolution based on stress flow field and dislocation density field. If there is a processing deviation, the processing control module of the servo control module is executed according to the online controller. The bending monitoring module is embedded with a dynamic projection surface based on the evolution of dislocation density and flow stress under a pre-processing strategy.

10. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the adaptive control method for the busbar flexible bending process according to any one of claims 1-8.

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