Surface wiredrawing treatment method for metal plate

Abstract

The invention discloses a surface wiredrawing treatment method for a metal plate, which comprises the following steps: firstly, constructing an intelligent sensing system to acquire material, thickness, surface state and environmental data of the plate, and transmitting the data to a central processing unit after preprocessing; the processing unit extracts key features through a multi-dimensional analysis engine, and calls a self-adaptive algorithm to generate a regulation and control instruction containing a basic parameter, a compensation parameter and an emergency threshold value in combination with a process influence weight model; the wire drawing equipment is preheated after receiving the instruction, parameters are calibrated, formal wire drawing is carried out after a test run test reaches the standard, and abrasive material abrasion and equipment parameters are monitored in real time in the process. And electroplating, anodic oxidation or spraying composite processes can be connected, a detection and clamping scheme is optimized for complex curved surfaces and special-shaped parts, finally, the treatment precision and efficiency are guaranteed through multi-level quality detection and an intelligent production line, the rate of unqualified products is reduced, and the device is suitable for surface treatment of various metal plates.

Description

Technical Field

[0001] This invention belongs to the field of sheet metal processing technology, and particularly relates to a method for surface wire drawing of metal sheets. Background Technology

[0002] In the field of sheet metal processing, surface brushing is a key process for improving the appearance and surface performance of products, and it is widely used in home appliances, automobiles, electronic equipment, aerospace, and other fields. However, existing sheet metal surface brushing technologies still have many problems that need to be solved: First, traditional wire drawing processes rely on manual experience to set process parameters, making it difficult to accurately match the material differences, thickness fluctuations, and initial surface conditions of metal sheets. For example, using the same drawing speed and pressure for aluminum alloy and stainless steel sheets can easily lead to over-drawing damage in aluminum alloy sheets or uneven wire drawing texture in stainless steel sheets. At the same time, when the sheet thickness distribution is uneven, manually set fixed parameters cannot adapt to the processing needs of different areas, further reducing the stability of wire drawing quality. Secondly, the existing detection and parameter adjustment processes are "discrete," lacking real-time and closed-loop control capabilities. In most processes, it is necessary to first obtain sheet information through offline detection, and then manually input parameters into the wire drawing equipment. The time difference between detection and processing can easily lead to a disconnect between parameters and the actual sheet condition. Furthermore, the wire drawing effect cannot be monitored in real time during processing; it can only be sampled after processing. Once parameter deviations occur, batch products will be scrapped, increasing production costs. Finally, insufficient data processing capabilities hinder process optimization. Existing technologies struggle to perform in-depth noise reduction and feature extraction on collected sheet metal inspection data, making it impossible to accurately assess the coupled influence of multiple parameters such as material, thickness, and surface condition on the wire drawing effect. This results in a lack of scientific basis for parameter adjustments, forcing optimization through repeated trial and error, which is not only inefficient but also makes it difficult to develop standardized process solutions, failing to meet the stringent requirements for wire drawing quality in high-end fields. In summary, there is an urgent need for a metal sheet surface wire drawing method that can achieve intelligent and real-time processing of the entire process of detection, analysis, and parameter adjustment, and can accurately adapt to the characteristics of the sheet, in order to overcome the shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide a surface wire drawing method for metal sheets to solve the problems mentioned in the background art.

[0004] In view of this, the present invention provides a method for surface wire drawing treatment of metal sheets, comprising the following steps: S1: Construct a communication architecture between the intelligent sensing system and the central processing unit, deploy a data preprocessing unit, and build a detection network through the sensor array to ensure the integrity and real-time performance of data acquisition; S2: Initiate the detection process of the intelligent sensing system to obtain information on the material properties, thickness distribution, and surface condition of the metal sheet, forming a complete detection dataset; S3: The detection dataset is transmitted to the central processing unit, which processes the data and extracts features, and analyzes the influence of each parameter on the wire drawing effect based on the preset model. S4: The central processing unit calls the parameter generation algorithm and combines the analysis results to generate a set of wire drawing parameter control instructions that include basic parameters, compensation parameters and emergency adjustment thresholds; S5: Transmit the parameter control instruction set to the control unit of the wire drawing equipment. After the control unit verifies that the instruction is correct, it starts the equipment preheating and performs the parameter adjustment operation. S6: After the parameters are adjusted, start the trial operation mode of the wire drawing equipment, conduct a small-scale wire drawing test on a designated area of ​​the metal sheet, and collect the wire drawing effect data of the test area; S7: The central processing unit receives the trial operation feedback data and compares it with the preset effect standard. If it meets the requirements, it issues a formal wire drawing command and the equipment enters the continuous wire drawing state. If it does not meet the requirements, it re-optimizes the parameters and repeats steps S5-S6 until the trial operation effect meets the standard.

[0005] In a further embodiment of the present invention, in step S4, the grinding material adaptation database constructed in the central processing unit adopts a dynamic update architecture, including a basic adaptation module and a working condition correction module. The basic adaptation module stores the reference correlation between different materials, thicknesses, surface conditions and grinding material types, while the working condition correction module dynamically calibrates the reference correlation based on historical process data and environmental temperature and humidity parameters. S41: When generating the parameter control instruction set, the central processing unit first calls the basic adaptation module to match the current metal plate detection data with the benchmark correlation and initially screen out 3-5 candidate grinding materials. S42: Start the working condition correction module, input the current ambient temperature and humidity data and the historical process effect data of similar plates, and use a multi-factor regression algorithm to score the suitability of candidate grinding materials and select the target grinding material with the highest suitability. S43: Incorporate the information on the target grinding material's type, particle size, and replacement cycle into the parameter control instruction set to form a complete grinding material control instruction; In step S5, after receiving the grinding material control command, the local controller of the wire drawing equipment starts the automatic grinding material replacement system. This system includes a material identification unit, an automatic disassembly unit, and a precision installation unit. S51: The material identification unit uses image recognition technology to confirm the model of the currently installed grinding material and compares it with the target grinding material; S52: If the model does not match, the automatic disassembly unit will start the robotic arm to perform the disassembly operation and transfer the old grinding material to the waste recycling bin; S53: The precision installation unit picks up the target grinding material from the material storage bin, calibrates the installation position through the vision positioning system, and initiates pressure testing and concentricity detection after installation is completed; In step S7, during the formal wire drawing process, the real-time monitoring module continuously collects wear data of the grinding material. When the wear reaches the replacement cycle threshold, an early warning is automatically triggered to remind staff to replenish the grinding material in a timely manner. In a further embodiment of the present invention, in step S3, the central processing unit constructs a coupled analysis model of material hardness-thickness-surface roughness when analyzing the detection data. S31: Determine the yield strength and elastic modulus of the metal sheet by hardness test data, judge the rigidity difference in different areas of the sheet by combining the thickness distribution map, and determine the micro-undulation characteristics of the initial surface by surface roughness data. S32: Input the above data into the preset process calculation model. This model is based on the theory of elastic-plastic mechanics and the principle of friction and wear, and calculates the optimal wire drawing speed and pressure for different areas of the plate. S33: Integrate the calculation results of different regions to generate a wire drawing speed gradient distribution scheme and pressure dynamic adjustment scheme covering the entire plate, determine the upper limit, lower limit and adjustment rate of the wire drawing speed in the transition area, as well as the reference value, compensation value and allowable fluctuation range of the wire drawing pressure. In step S5, after receiving the speed and pressure control commands, the local controller of the wire drawing equipment starts the dual closed-loop control system, which includes a speed control subsystem and a pressure control subsystem. S51: The speed control subsystem collects the rotational speed data of the drawing roller in real time through the encoder, compares it with the speed gradient distribution scheme, and adjusts the output frequency of the drive motor through the PID algorithm to achieve precise control of the rotational speed. S52: The pressure control subsystem collects the contact pressure data between the wire drawing roller and the plate through a pressure sensor, and combines it with the dynamic pressure adjustment scheme to adjust the contact force in real time through a hydraulic or pneumatic pressure regulating mechanism to ensure that the pressure is stable within the set range. During the formal wire drawing process in step S7, the central processing unit receives speed and pressure data in real time through edge computing nodes and generates an adjustment curve every 5 seconds. If the data shows a deviation that exceeds the allowable fluctuation range, a correction command is immediately sent to adjust the speed and pressure parameters. In a further embodiment of the present invention, after the formal wire drawing process is completed in step S7, the wire drawing-electroplating composite process is started, specifically including: S8: performing multi-stage pretreatment on the wire-drawn metal sheet; S81: The first stage is alkaline degreasing treatment. The plates are placed in the degreasing tank and ultrasonic-assisted cleaning technology is used. The ultrasonic vibration of 28-40kHz enhances the penetration ability of the degreasing agent to remove surface oil and wire debris. The treatment time is dynamically adjusted according to the amount of oil residue. S82: The second stage is acidic oxide film removal treatment. An acidic solution suitable for the material is selected. The solution temperature is maintained at 40-60℃ through a temperature control system. The solution concentration is monitored in real time during the treatment process. When the concentration is lower than the threshold, the original solution is automatically replenished. S83: The third stage is the activation treatment, in which an activator is sprayed onto the surface of the board to form a uniform active adsorption layer. After activation, the board is rinsed with pure water to remove any residual activator. S84: The fourth stage is drying. The panels are sent into a hot air circulating drying oven and the temperature is gradually increased from room temperature to 80-120℃ using a gradient heating method to avoid deformation of the panels due to excessive temperature difference. After drying, the surface moisture content is detected by a humidity sensor to ensure that the moisture content is below 0.5%. S9: Initiate the electroplating process parameter optimization process; S91: The central processing unit retrieves the corresponding basic electroplating parameters from the electroplating process database based on the material type of the metal sheet and the surface microstructure after wire drawing, including the composition of the electroplating solution, temperature, and current density. S92: Combining the surface condition data of the pre-treated board, the deposition rate and uniformity of the electroplating layer are predicted by numerical simulation algorithm, and the basic electroplating parameters are optimized and adjusted. S93: Transmit the optimized electroplating parameters to the control system of the electroplating equipment to form a personalized electroplating process solution; S10: Perform electroplating operations and quality monitoring; S101: Fix the pre-treated qualified plates with hangers and place them in the electroplating tank to ensure good contact between the plates and the electrodes and avoid local abnormal current density. S102: Start the electroplating equipment and perform the electroplating operation according to the personalized electroplating process plan. During the process, the purity of the electroplating solution is maintained through a circulating filtration system, and the pH value of the electroplating solution is monitored in real time through a pH sensor to ensure parameter stability. S103: After electroplating, the plate is removed from the electroplating tank and then rinsed with pure water, passivated, and dried in sequence. The passivation treatment uses an environmentally friendly passivating agent to form a protective film on the surface of the electroplated layer. The temperature of the second drying is controlled at 60-80℃ to ensure the bonding strength between the electroplated layer and the brushed surface. In a further embodiment of the present invention, after the formal wire drawing process is completed in step S7, the integrated wire drawing-anodic oxidation process is initiated, specifically including: S8: Conduct refined pretreatment of the brushed sheet metal; S81: The first stage is high-pressure water cleaning, using 3-5MPa high-pressure pure water. The surface of the board is rinsed in all directions through an adjustable nozzle to remove metal debris left by wire drawing. The effect of debris removal is confirmed by a visual recognition system during the rinsing process. S82: The second stage is chemical degreasing, which uses environmentally friendly degreasing agents and combines soaking and spraying. The treatment time is set according to the surface oil stains. The degreasing effect is confirmed by conductivity testing after treatment. S83: The third stage is alkaline washing treatment. The plates are placed in an alkaline washing tank, which is equipped with a real-time temperature and concentration monitoring system. The alkaline concentration and treatment temperature are adjusted according to the material of the plates. The treatment time is controlled by a corrosion rate sensor to avoid excessive corrosion. S84: The fourth stage is neutralization treatment, which uses a dilute acid solution to neutralize the surface of the board and remove residual alkali. After neutralization, the surface acidity and alkalinity are tested with pH test paper to ensure that it reaches neutrality. S85: The fifth stage is fine polishing, which uses an ultra-fine particle polishing agent to lightly polish the surface of the board, optimize the surface micro-smoothness, and lay the foundation for anodizing. S9: Perform intelligent matching of anodizing process parameters; S91: The central processing unit selects a suitable electrolyte type from the electrolyte database based on the material of the metal plate and determines the basic components and concentration range of the electrolyte. S92: Combining the surface roughness data after wire drawing, the oxide film thickness and density under different oxidation voltages and times are predicted by the oxide film growth model, and multiple sets of candidate process parameters are generated. S93: Call the historical process database, compare the candidate process parameters with the oxidation effect data of similar plates, and use machine learning algorithms to select the optimal oxidation voltage, oxidation time and electrolyte temperature parameters. S10: Perform anodizing and film strengthening operations; S101: Install the pre-treated qualified plates on the anode bracket, ensuring good conductivity between the plates and the bracket. Place the bracket into the oxidation tank containing the appropriate electrolyte, and install the cathode plate at the same time, ensuring that the distance between the anode and cathode is uniform. S102: Start the anodic oxidation power supply, adjust the voltage and current according to the optimal process parameters, maintain the temperature of the electrolyte uniform through the circulation system during the oxidation process, monitor the changes in electrolyte composition through sampling and analysis, and replenish the consumed components in a timely manner; S103: After oxidation is completed, the plate is removed from the oxidation tank and sealed. High-temperature sealing or low-temperature sealing process is used. The appropriate sealing agent is selected according to the type of oxide film. After sealing, wear resistance test and corrosion resistance test are conducted to confirm that the performance indicators of the oxide film meet the standards. S104: After sealing the holes, the surface of the plate is cleaned and dried. Surface moisture is blown away with compressed air, and then it is placed in a constant temperature drying oven for low-temperature drying to ensure stable bonding between the oxide film and the brushed surface. In a further embodiment of this invention, after the formal brushing process is completed in step S7, the brushing-spraying composite process is initiated, specifically including: S8: Performing a pretreatment to control the cleanliness of the plate after brushing; S81: The first stage is electrostatic dust removal. The electrostatic dust removal equipment is started, and the tiny particles on the surface of the board are charged by a high-voltage electrostatic field. The charged particles are then removed by an adsorption device. After dust removal, the number of particles on the surface is detected by a particle counter to ensure that the number of particles per square meter is less than 100. S82: The second stage is plasma cleaning, which uses low-temperature plasma to treat the surface of the board. High-energy particles bombard the surface to remove organic contaminants and oxide layers, while improving the wettability of the surface. The treatment time is set according to the degree of surface contamination. S83: The third stage is surface activation, in which the surface of the board is wiped with an activator to further enhance the surface activity and lay the foundation for the adhesion of the spray coating. After activation, the board enters the spraying process within 1 hour to avoid surface contamination. S9: Customize the spraying process design; S91: The central processing unit determines the performance requirements of the spray coating based on the usage scenario of the metal sheet. S92: Select candidate materials that meet the performance requirements from the spraying material database, including resin type, pigment and filler ratio, and type of curing agent, and predict the coating performance of different materials using performance simulation software; S93: Combine the texture depth and density data of the brushed surface to determine the coating thickness parameters, ensuring that the coating can cover the texture without obscuring the brushed texture. At the same time, calculate the spraying angle and spray gun movement speed to avoid coating accumulation or missed spraying. S94: Based on the characteristics of the spraying material, determine the curing temperature, curing time, and curing atmosphere to form a complete spraying process plan; S10: Perform spraying operations and control coating quality; S101: Fix the pre-treated qualified panels on the spraying worktable and calibrate the position of the panels using a vision positioning system; S102: Start the spraying equipment, adjust the atomization pressure, spray flow and movement trajectory of the spray gun according to the customized process plan, adopt a multi-coat spraying method, and perform flash drying treatment after each coat to avoid coating sagging. S103: After the coating is completed, the panel is sent into the curing equipment and the curing operation is performed according to the set curing parameters. During the curing process, the curing temperature and time are monitored in real time to ensure that the curing is complete. S104: After curing, the coating is subjected to quality inspection, including adhesion testing, hardness testing, thickness testing, and appearance inspection. In a further embodiment of this invention, when the metal plate has a complex curved surface structure, the layer detection process in step S2 is optimized as follows: S21: Start the 3D scanning system and use laser scanning technology to perform a full-area scan on the complex curved metal plate. The scanning accuracy is controlled within 0.01mm, and a 3D point cloud model of the plate is generated. S22: Perform data processing on the 3D point cloud model, including denoising, simplification, and stitching, to generate a complete 3D geometric model. Extract various curvature feature parameters such as radius of curvature, rate of change of curvature, and slope of the surface in each region through model analysis software. S23: Set the detection path on the three-dimensional geometric model, and deploy material identification sensors, thickness measurement sensors and surface imaging sensors according to the path to perform targeted detection on different areas of the curved surface and obtain the material properties, thickness data and surface state information of each area; S24: Associate the curvature feature parameters with the detection data of each region to form a comprehensive dataset containing geometric features and physical properties; Step S3's multi-dimensional analysis engine adds a surface adaptation analysis module: S31: This module inputs the comprehensive dataset into the surface wire drawing process model, which is based on differential geometry and robot kinematics theory to analyze the influence of surface curvature changes on the motion trajectory of the wire drawing tool. S32: By combining material hardness and thickness distribution data, determine the difficulty of wire drawing in different areas of the curved surface, highlight areas with high curvature changes and thin-walled areas, and formulate differentiated wire drawing strategies. The adaptive parameter generation algorithm in step S4 adds trajectory planning functionality: S41: Based on the three-dimensional geometric model and surface curvature characteristic parameters, the B-spline curve interpolation algorithm is used to generate the reference motion trajectory of the wire drawing tool, ensuring that the trajectory fits the surface with a degree of greater than 95%. S42: Based on the differentiated wire drawing strategy, optimize the baseline trajectory, adjust the radius of curvature and movement step length of the trajectory in the high curvature change area, and adjust the spacing of the trajectory in the thin-walled area to avoid deformation of the sheet metal; S43: Integrate the optimized trajectory data with the wire drawing speed and pressure parameters to generate a complete parameter control instruction set including trajectory coordinates, speed changes, and pressure adjustments; In step S5, after receiving the parameter control instruction set, the multi-degree-of-freedom robotic arm equipped with the wire drawing equipment starts the trajectory tracking system: S51: The motion controller parses the trajectory coordinate data and generates motion commands for each joint of the robotic arm; S52: The vision guidance system is activated, and the position of the workpiece is captured in real time by an industrial camera to dynamically calibrate the motion trajectory of the robotic arm. In the formal wire drawing process of step S7, force feedback control technology is used to collect the end force data of the robotic arm in real time. When the force exceeds the set range, the movement speed and pressure are automatically adjusted to avoid uneven wire drawing or damage to the plate in the curved area. In a further embodiment of the present invention, when the metal sheet is an irregularly shaped part, the method further includes the following steps: S8: Conduct customized design of clamping solutions for irregularly shaped parts; S81: Obtain the 3D model and structural feature data of irregular parts, including irregular shape, size and location of protrusions / recesses, and distribution of thin-walled areas; S82: Based on structural feature data, the structure of the clamping device is designed using a topology optimization algorithm. The device includes a positioning base, an elastic clamping component, and an auxiliary support component. S83: The positioning base is designed with positioning grooves according to the bottom contour of the irregular part to ensure the initial positioning accuracy after the irregular part is placed; the elastic clamping component is made of silicone or polyurethane material, and the shape of the clamping head is designed according to the surface morphology of the irregular part to avoid damaging the surface; the auxiliary support component is designed with support points for thin-walled areas and protruding parts to prevent deformation during the wire drawing process. S84: The clamping scheme is simulated using finite element analysis software to verify the stress distribution of the irregular part after clamping, and to ensure that the stress value in the stress concentration area is lower than the yield strength of the material. S9: Perform precise clamping operations on irregularly shaped parts; S91: Install the customized clamping device on the worktable of the wire drawing equipment, and calibrate the levelness and flatness of the clamping device using a level and a dial indicator; S92: The irregularly shaped parts are placed in the positioning slot of the positioning base by means of hoisting or robotic arm handling. The initial positioning position is confirmed by the vision positioning system. If there is a deviation, it is fine-tuned. S93: Activate the elastic clamping component and use a step-by-step pressurization method. First, apply 50% of the set pressure and monitor the displacement change of the irregular part through the displacement sensor. If the displacement is within the allowable range, continue to apply the remaining 50% pressure. If the displacement exceeds the allowable range, stop pressurization, readjust the position of the clamping head, and then continue pressurization until the set pressure is reached and the displacement meets the requirements. S94: Activate the auxiliary support component, drive the support head to contact the thin-walled area and protrusion of the irregular part through the micro cylinder, apply the preset support force, and monitor the magnitude of the support force in real time through the pressure sensor during the support process to ensure that the support force is stable within the set range; S10: Perform wire drawing and dynamic monitoring of irregularly shaped parts; S101: Following the process of steps S5-S6, complete the parameter adjustment and trial operation test of the wire drawing equipment. The trial operation area should be the non-critical structural area of ​​the irregular part. S102: After the trial run meets the standards, the formal wire drawing process is started. During the wire drawing process, the multi-dimensional monitoring system collects data on the clamping stability of the irregular parts, the force data of the wire drawing tool, and the quality data of the wire drawing surface in real time. S103: If loose clamping or abnormal wire drawing quality is detected, the central processing unit immediately issues a pause command to analyze the cause of the abnormality. If it is a clamping problem, the clamping parameters are readjusted; if it is a wire drawing parameter problem, the parameters are optimized. After the problem is solved, the wire drawing process is restarted until all areas of the irregular part are wire drawn. In a further embodiment of this invention, after completing the combination of wire drawing and electroplating, anodizing, or spraying processes, a comprehensive quality inspection and process optimization process is initiated, specifically including: S11: Construct a multi-level quality inspection system, which includes a basic inspection layer, a deep inspection layer, and a reliability inspection layer. Each inspection layer works together to achieve a comprehensive evaluation of the composite coating. S12: Perform the detection operation of the basic detection layer; S121: The appearance inspection module uses a high-definition industrial camera and image analysis software to capture the entire surface of the metal sheet, generate a surface image dataset, and identify appearance defects such as bubbles, cracks, pinholes, and color differences in the coating through image segmentation and feature extraction algorithms. S122: The thickness detection module uses a non-destructive coating thickness gauge to measure the coating thickness at multiple points according to a preset grid path, generate a thickness distribution map, and determine whether the coating thickness is uniform and meets the design requirements. S123: The adhesion testing module selects either the cross-cut method or the pull-off method according to the coating type to test the adhesion of the coating. The cross-cut method involves drawing a grid on the coating surface using a cross-cutting device, and then observing the coating peeling off after applying tape. The pull-off method involves applying a pulling force through the equipment and recording the pulling force value when the coating peels off, thereby evaluating the bonding strength between the coating and the metal substrate. S13: Perform the detection operation of the depth detection layer; S131: The hardness testing module uses a microhardness tester to test the hardness of the coating cross section, obtain the microhardness value of the coating, analyze the hardness distribution law of the coating, and determine the basic wear resistance performance of the coating; S132: The composition testing module uses an X-ray fluorescence spectrometer to analyze the elemental composition of the coating, confirm whether the coating composition is consistent with the design formula, and whether there are impurity elements. S133: The microstructure detection module uses a scanning electron microscope to observe the micromorphology and structure of the coating, analyze whether there are micro-defects such as pores and delamination inside the coating, and evaluate the structural compactness of the coating. S14: Perform the detection operation of the reliability detection layer; S141: The corrosion resistance testing module selects salt spray test, immersion test or electrochemical corrosion test according to the application scenario of the board to simulate harsh corrosion environment to accelerate corrosion test of the board, observe the corrosion of the coating regularly, and record the corrosion start time and corrosion degree. S142: The weather resistance testing module uses a xenon lamp aging test chamber to simulate natural environmental factors such as sunlight, rain, and humidity to conduct aging tests on the panels. After the test, the changes in the appearance, adhesion, and hardness of the coating are detected to evaluate the weather resistance performance of the coating. S143: The wear resistance testing module uses a friction and wear testing machine to set different loads and friction cycles to conduct friction and wear tests on the coating. The wear amount of the coating is calculated by weighing method or surface profile measurement method, and the wear resistance life of the coating is evaluated. S15: Conduct comprehensive analysis of testing data and optimize processes; S151: The detection data of each detection layer is transmitted to the data analysis platform of the central processing unit. The platform uses multivariate statistical analysis algorithms to standardize the data and perform correlation analysis to identify key process parameters that affect coating quality. S152: If the test results meet the preset quality standards, the process parameters and test data will be stored in the process database as a reference for the subsequent processing of similar panels. S153: If the test results do not meet the quality standards, activate the fault diagnosis algorithm, analyze the correlation between non-conforming items and process parameters, and determine the root cause of the quality problem; S154: Based on the fault diagnosis results, generate targeted process optimization plans, adjust the corresponding process parameters, apply the optimization plans to the processing of the next batch of boards, and verify the effectiveness of the optimization plans by comparing the test data before and after optimization, forming a closed-loop process improvement mechanism of "detection-analysis-optimization-verification". A further embodiment of this invention includes a step of constructing an intelligent, flexible, and automated production line, specifically comprising: S16: Plan the overall architecture of the production line, including the central control system, logistics transmission system, process execution system, quality monitoring system and data management system, and realize data interaction and collaborative control through the industrial internet; S17: Deploy a central control system, which serves as the core hub of the production line and includes an intelligent scheduling module, a process management module, and an emergency response module; S171: The intelligent scheduling module adopts a scheduling strategy that combines genetic algorithm and particle swarm optimization algorithm. Based on order demand, equipment status and material inventory information, it automatically generates the optimal production plan and task allocation scheme, and reasonably arranges the processing order of each board and equipment usage time. S172: The process management module constructs a process parameter database and a process knowledge base, storing the processing process parameters, historical process data and optimization schemes for various metal plates. It can automatically call the corresponding process scheme according to the plate type, and also supports staff to manually adjust the process parameters. After adjustment, the parameter change log is automatically recorded. S173: The emergency response module monitors the operating status of each system on the production line in real time. When equipment failure, material shortage, or quality abnormality occurs, the early warning mechanism is automatically triggered to remind staff through audible and visual alarms and system pop-ups. At the same time, an emergency response plan is generated. When there is a material shortage, a material purchase request is triggered. When there is a quality abnormality, the relevant process is suspended to ensure the safe and stable operation of the production line. S18: Deploy a logistics transmission system, which includes an automatic feeding unit, an intelligent conveying unit, and an automatic unloading unit; S181: The automatic feeding unit is equipped with a multi-degree-of-freedom robotic arm and a vision positioning system. The robotic arm picks up the metal sheet to be processed from the material storage area, calibrates the position of the sheet through vision positioning, and accurately places the sheet on the conveyor belt to complete the automatic feeding operation. S182: The intelligent conveying unit uses a magnetic levitation conveyor belt or a servo motor to drive the conveyor belt. According to the scheduling instructions of the central control system, it adjusts the running speed and transmission path of the conveyor belt to accurately transport the board to each process station. During the conveying process, the position and status of the board are tracked in real time through RFID tags or visual recognition technology. S183: The automatic unloading unit is equipped with a classification and storage mechanism. According to the quality inspection results, qualified and unqualified boards are transported to the corresponding storage areas. Qualified boards are classified and stored according to the order number. Unqualified boards are marked with the reason and then transferred to the rework area or scrap area. At the same time, the inventory data is automatically updated. S19: Deploy the process execution system, which includes equipment and auxiliary equipment corresponding to each process. Each piece of equipment is equipped with a local controller and a data acquisition module. S191: The local controller receives process parameter instructions from the central control system and controls the equipment to execute the corresponding process operations. The electroplating equipment performs electroplating parameter adjustment and electroplating operations. S192: The data acquisition module collects the equipment's operating parameters, process data, and board processing status data in real time, and transmits them to the central control system and data management system via industrial Ethernet to provide data support for production monitoring and data analysis. S20: Deploy a data management system that adopts a cloud-edge collaborative architecture, including edge data processing nodes and a cloud data center; S201: Edge data processing nodes preprocess real-time acquired production data to ensure data accuracy and validity, while enabling local data storage and rapid retrieval to meet real-time control requirements; S202: The cloud data center performs long-term storage and in-depth analysis of pre-processed production data. Through big data analysis algorithms and machine learning models, it uncovers patterns and trends in the production data and generates production reports, quality analysis reports, and process optimization suggestions. S203: The data management system has data security and traceability functions. Through data encryption and access control technologies, it ensures the security of production data and establishes a product traceability system. Through the unique identifier of the board, the production time, operators, equipment information, process parameters, and test data of the board can be traced.

[0006] The beneficial effects of this invention are: 1. By constructing a two-way communication architecture between the intelligent sensing system and the central processing unit, and combining a multimodal sensing array and edge computing nodes, comprehensive and real-time detection of the material, thickness, and surface condition of metal sheets is achieved, avoiding the time lag problem of offline detection. At the same time, the central processing unit starts a multi-dimensional analysis engine to perform data noise reduction and feature extraction, and judges the coupling law of multiple parameters based on the process influence weight model. Compared with traditional manual experience judgment, the accuracy of data processing is greatly improved, providing a scientific basis for subsequent parameter generation and ensuring the compatibility between wire drawing parameters and sheet characteristics from the source. 2. The solution incorporates a closed-loop process of "trial run-feedback-optimization": After parameter adjustment, a small-scale trial run is conducted on the edge area of ​​the sheet metal. Real-time feedback sensors collect data on the wire drawing effect. The central processing unit compares the feedback data with preset standards. If the standards are not met, the parameters are re-optimized until the trial run results are satisfactory. This mechanism effectively avoids the batch quality risks caused by the traditional "one-time parameter setting," allowing for real-time correction of parameter deviations. It ensures that the parameters are always adapted to the sheet metal characteristics during the wire drawing process, significantly improving the uniformity of the wire drawing texture, the stability of surface finish and other quality indicators, and reducing the defect rate. 3. An adaptive parameter generation algorithm generates an instruction set containing basic parameters, compensation parameters, and emergency adjustment thresholds. This set can automatically adapt to metal sheets of different materials, thicknesses, and surface conditions, eliminating the need for manual parameter template changes. Simultaneously, the wire drawing equipment automatically completes preheating, parameter adjustment, and trial operation upon receiving instructions. System optimization is only required if the trial operation fails to meet standards, without manual intervention. Compared to traditional processes relying on manual operation and experience, this significantly improves automation, reduces labor costs and operational errors, and enables rapid response to the processing needs of different types of sheets, enhancing process adaptability and production efficiency. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the processing method of the present invention. Detailed Implementation

[0008] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0009] In the description of this application, it should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0010] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and are not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0011] It should be noted that in the description of this application, the directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0012] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0013] This embodiment provides a surface wire drawing method for metal sheets, including the following steps: S1: Construct a bidirectional communication architecture between an intelligent sensing system and a central processing unit. Industrial Ethernet and 5G edge gateways are used to achieve high-speed data transmission. Edge computing nodes are deployed to preprocess the collected raw data by filtering, noise reduction, and format conversion, reducing the data processing load of the central processing unit. A comprehensive detection network is built through a multimodal sensing array. This array includes material identification sensors, thickness measurement sensors, surface imaging sensors, and environmental temperature and humidity sensors, covering key dimensions such as the material, size, surface condition, and processing environment of the metal sheet. This ensures the integrity and real-time nature of data acquisition, effectively avoiding misjudgment of process parameters due to missing or delayed data, and improving the accuracy of subsequent wire drawing processing.

[0014] S2: Initiate the layered detection process of the intelligent sensing system. First, a material identification sensor performs a full-area scan of the metal sheet using X-ray fluorescence spectroscopy. The scanning range covers the entire area of ​​the sheet, and the scanning speed is set to 20 mm / s. This acquires information on the elemental composition and microstructure properties of the material, including the content ratio of each element, grain size, and distribution. Next, a thickness measurement sensor performs multi-point sampling along the latitude and longitude of the sheet, with a sampling interval of 10 mm × 10 mm. Each sheet has at least 50 sampling points, generating an overall thickness distribution map that clearly shows the thickness differences in different areas of the sheet. Finally, a surface imaging sensor captures surface details. This sensor has a resolution of 2048 × 1536 pixels and a frame rate of 10 frames / second. It identifies surface features such as scratches, oxide spots, and microcracks, forming a complete detection dataset. This provides comprehensive data support for subsequent process parameter analysis and reduces wire drawing quality problems caused by inaccurate initial state assessment.

[0015] S3: The detection dataset is transmitted to the central processing unit. The central processing unit starts a multi-dimensional analysis engine. First, it performs noise reduction and feature extraction on the material properties, thickness distribution, and surface condition data. Wavelet transform algorithm is used to remove data noise, and principal component analysis algorithm is used to extract key feature parameters, reducing data dimensionality while retaining core information. Then, based on the preset process influence weight model, which is generated by training a large amount of historical process data, the influence weight of parameters such as material hardness, plate thickness, and surface roughness on the depth and uniformity of the brushed texture is determined. The coupling effect of each parameter on the brushing effect is analyzed. For example, high-hardness materials need to be matched with higher brushing pressure, and thick plates need to be appropriately reduced in brushing speed to ensure that the subsequently generated process parameters can be adapted to the characteristics of the plate and improve the consistency of the brushing effect.

[0016] S4: The central processing unit calls the adaptive parameter generation algorithm. This algorithm combines the analysis results of the multi-dimensional analysis engine with historical process data of similar plates to generate a set of wire drawing parameter control instructions, which includes basic parameters, compensation parameters, and emergency adjustment thresholds. The basic parameters include wire drawing speed, wire drawing pressure, and wire drawing roller speed. The compensation parameters are adjusted for plate thickness deviation and surface roughness differences. The emergency adjustment thresholds set the upper and lower limits of wire drawing speed and pressure and the range of abnormal fluctuations. When the actual parameters exceed the thresholds, the adjustment mechanism is triggered to effectively deal with unexpected situations in the processing and ensure the stability of the wire drawing process.

[0017] S5: The parameter control instruction set is transmitted to the local controller of the wire drawing equipment via industrial Ethernet. The local controller parses and verifies the instructions, checks whether the instruction format is correct and whether the parameters are within the equipment's operating range. After confirming that there are no errors, the equipment preheating program is started. During the preheating process, the temperature of key components such as the wire drawing roller and drive motor is monitored in real time. Once the temperature of each component reaches the preset working range, with the wire drawing roller temperature controlled at 40-60℃ and the drive motor temperature not exceeding 80℃, the parameter adjustment operation is performed to adjust the wire drawing speed to the basic parameter setting value. The wire drawing pressure sensor and the wire drawing roller speed encoder are calibrated to ensure that the equipment parameters are consistent with the instruction requirements, avoiding wire drawing quality defects caused by insufficient equipment preheating or parameter calibration deviation.

[0018] S6: After parameter adjustment, start the trial operation mode of the wire drawing equipment and conduct a small-scale wire drawing test on the edge area of ​​the metal sheet. The test area is set to 50mm×50mm. The wire drawing effect data of the test area is collected through real-time feedback sensors, including a laser profilometer and a surface roughness meter. The laser profilometer measures the depth and width of the wire drawing texture with a measurement accuracy of 0.001mm. The surface roughness meter detects the Ra value of the test area to ensure that the data can accurately reflect the wire drawing effect. During the trial operation, the equipment operation status is monitored in real time to promptly identify parameter and equipment compatibility issues and avoid large-area quality problems caused by direct formal wire drawing.

[0019] S7: The central processing unit receives the trial operation feedback data and compares it with the preset effect standards. The preset standards include a wire drawing texture depth deviation of ≤ ±0.01mm, a surface roughness Ra value controlled within 0.2-0.4μm, and no obvious scratches or uneven texture. If the requirements are met, a formal wire drawing command is issued, and the wire drawing equipment enters a continuous wire drawing process. If the requirements are not met, the parameters are re-optimized, and key indicators such as wire drawing speed and pressure are adjusted. Steps S5-S6 are repeated until the trial operation effect meets the standards. Through the cycle of trial operation and parameter optimization, the formal wire drawing process can be ensured to stably achieve the expected effect and reduce the defect rate.

[0020] In step S4, the abrasive material matching database built within the central processing unit adopts a dynamically updated architecture, including a basic matching module and a working condition correction module. The basic matching module stores the baseline correlation between different materials, thicknesses, surface conditions, and abrasive material types. For example, aluminum alloys are matched with alumina abrasive materials, stainless steel is matched with silicon carbide abrasive materials, thick plates are matched with coarser abrasive materials, and plates with high surface roughness need to use coarse abrasive materials first and then switch to fine abrasive materials. The working condition correction module dynamically calibrates the baseline correlation based on historical process data and environmental temperature and humidity parameters. When the environmental humidity is higher than 60%, the wear resistance requirements of the abrasive materials are appropriately increased. When a certain type of abrasive material shows uneven texture on a specific material in historical data, the abrasive material model corresponding to that material is adjusted to ensure that the selection of abrasive materials always matches the actual processing conditions.

[0021] S41: When generating the parameter control instruction set, the central processing unit first calls the basic adaptation module to match the current metal plate's detection data with the benchmark correlation. Based on the plate's material, thickness range, and initial surface roughness, it initially selects 3-5 candidate grinding materials. For example, for an aluminum alloy plate with a thickness of 5mm and a surface Ra value of 1.0μm, it initially selects 80-mesh, 120-mesh, and 180-mesh alumina grinding materials. S42: The working condition correction module is activated, inputting the current ambient temperature and humidity data and historical process effect data for similar plates. The ambient temperature and humidity are acquired in real time through temperature and humidity sensors deployed in the workshop. Historical process effect data includes the uniformity of the brushed texture and the surface roughness compliance rate of similar plates after using different grinding materials. A multi-factor regression algorithm is used to score the compatibility of the candidate grinding materials. The scoring indicators... The selection process includes considering factors such as texture uniformity (40%), surface roughness compliance rate (30%), and abrasive material lifespan (30%). The target abrasive material with the highest compatibility is selected. For example, in an environment with 55% humidity, 120-mesh alumina abrasive material has the highest compatibility score on similar aluminum alloy plates, thus being identified as the target abrasive material. S43: The model, grit size, and replacement cycle information of the target abrasive material are incorporated into the parameter control instruction set. The replacement cycle is determined based on the wear rate and historical usage data of the abrasive material. For example, the replacement cycle for 120-mesh alumina abrasive material on aluminum alloy plates is set to process 50 plates or use for 8 hours, forming a complete abrasive material control instruction. This ensures that subsequent abrasive material replacement and use can accurately match processing requirements, avoiding brushed texture defects or material waste caused by improper abrasive material selection.

[0022] In step S5, after receiving the abrasive material control command, the local controller of the wire drawing equipment starts the automatic abrasive material replacement system. This system includes a material identification unit, an automatic disassembly unit, and a precision installation unit. S51: The material identification unit uses image recognition technology to confirm the model of the currently installed abrasive material, photographs the markings and appearance features of the abrasive material, and compares them with the image template of the target abrasive material. The identification accuracy reaches 99%, quickly determining whether the current abrasive material meets the requirements. S52: If the model does not match, the automatic disassembly unit starts the robotic arm to perform the disassembly operation. The robotic arm is equipped with a clamping device at its end, with the clamping force controlled at 50-80N to avoid damaging the abrasive material installation structure. The old abrasive material is then transferred to the waste recycling bin, which is equipped with a classified storage function. Used abrasive materials of different types are stored separately for easy recycling. S53: The precision installation unit picks up the target abrasive material from the material storage bin. The storage bin is equipped with a temperature and humidity control device to keep the storage environment dry and clean. The installation position is calibrated by a vision positioning system with a positioning accuracy of 0.01mm to ensure that the coaxiality of the abrasive material and the drawing roller meets the requirements. After installation, pressure testing and concentricity detection are initiated. The pressure test uses a pressure sensor to detect the contact pressure of the abrasive material after installation, ensuring that the pressure is uniform and within the range of 20-30N. The concentricity detection uses a laser diameter gauge to measure the radial runout of the abrasive material during rotation, and the runout is controlled within 0.02mm to ensure the installation quality of the abrasive material and avoid uneven drawing texture caused by installation deviation.

[0023] In step S7, during the formal wire drawing process, the real-time monitoring module continuously collects wear data of the grinding material. This module measures the thickness change of the grinding material through a laser displacement sensor, collecting data every 30 seconds and calculating the wear amount. When the wear amount reaches the replacement cycle threshold, such as when the grinding material thickness decreases by 0.5mm or the wear amount accounts for 20% of the initial thickness, an early warning is automatically triggered. The warning information is displayed on the equipment operation interface, and an SMS reminder is sent to the staff to ensure that the staff replenishes the grinding material in time, avoiding a decrease in wire drawing effect due to excessive wear of the grinding material and ensuring the continuity of the production process.

[0024] In step S3, the central processing unit constructs a coupled analysis model of material hardness, thickness, and surface roughness when analyzing the test data. This model is based on elastoplastic mechanics and friction and wear theory, establishing a mathematical relationship between the three and clarifying the influence of surface roughness on wire drawing process parameters under different combinations of hardness and thickness. S31: The yield strength and elastic modulus of the metal sheet are determined through hardness test data. A Vickers hardness tester is used to test the hardness of the sheet, with a test load set to 500g and a holding time of 10 seconds. The yield strength and elastic modulus are calculated based on the hardness value. Quantities, such as the yield strength corresponding to a hardness of HV200 being approximately 600 MPa and the elastic modulus approximately 200 GPa; combining the thickness distribution map to determine the rigidity differences in different regions of the plate, with thicker plate regions exhibiting higher rigidity and thinner plate regions exhibiting lower rigidity; determining the microscopic undulation characteristics of the initial surface through surface roughness data, including undulation height and spacing, providing basic mechanical and geometric parameters for subsequent process parameter calculations; S32: inputting the above data into a preset process calculation model, which is based on elastoplastic mechanics theory and the principle of friction and wear, considering the plastic deformation of the plate and The friction effect of the abrasive material is used to calculate the optimal wire drawing speed and pressure for different regions of the plate. For example, the optimal wire drawing speed for high hardness and high rigidity regions is calculated to be 150 mm / s and the pressure to be 80 N, while the optimal wire drawing speed for low hardness and low rigidity regions is calculated to be 100 mm / s and the pressure to be 50 N. S33: The calculation results for different regions are integrated to generate a wire drawing speed gradient distribution scheme and a dynamic pressure adjustment scheme covering the entire plate. The upper limit and lower limit of the wire drawing speed and the adjustment rate of the transition region are determined. The upper limit is set as the calculated optimal value. The optimal speed is set at 1.2 times, and the lower limit is set at 0.8 times the calculated optimal speed. The adjustment rate in the transition area is controlled at 5mm / s² to avoid texture breakage caused by sudden speed changes. The baseline value, compensation value, and allowable fluctuation range of the wire drawing pressure are also set. The baseline value is the optimal pressure for the corresponding area, the compensation value is adjusted according to the thickness deviation, and the compensation pressure is 5N for every 0.1mm thickness deviation. The allowable fluctuation range is set to ±10% of the baseline value to ensure that the speed and pressure can be smoothly transitioned during the wire drawing process, adapt to the characteristics of different areas of the board, and improve the uniformity of the overall wire drawing effect.

[0025] In step S5, after receiving speed and pressure control commands, the local controller of the wire drawing equipment starts the dual closed-loop control system, which includes a speed control subsystem and a pressure control subsystem. S51: The speed control subsystem collects real-time rotational speed data of the wire drawing roller through an encoder. The encoder resolution is set to 1000 lines / revolution, and the sampling frequency is 100Hz. This data is compared with the speed gradient distribution scheme, and the output frequency of the drive motor is adjusted using a PID algorithm. The PID parameters are optimized through a self-tuning algorithm, with the proportional coefficient set to 0.5, the integral time set to 0.2s, and the derivative time set to 0.05s, achieving precise speed control. The control accuracy reaches ±1r / min, ensuring that the drawing speed meets the gradient distribution requirements; S52: The pressure control subsystem collects the contact pressure data between the drawing roller and the plate through a pressure sensor. The pressure sensor range is set to 0-200N, the accuracy is ±0.5N, and the sampling frequency is 50Hz. Combined with the dynamic pressure adjustment scheme, the contact force is adjusted in real time through a hydraulic or pneumatic adjustment mechanism. The hydraulic adjustment mechanism adopts servo valve control, and the pneumatic adjustment mechanism adopts proportional valve control. The response time is less than 0.1s, ensuring that the pressure is stable within the set range and the pressure fluctuation is controlled within ±1N, avoiding uneven drawing texture depth caused by pressure fluctuation.

[0026] During the formal wire drawing process in step S7, the central processing unit receives speed and pressure data in real time through edge computing nodes. The data transmission delay is controlled within 100ms, and an adjustment curve is generated every 5 seconds to intuitively display the changing trends of speed and pressure. If the data deviates beyond the allowable fluctuation range, such as the speed exceeding the upper limit or the pressure falling below the lower limit, a correction command is immediately sent to adjust the motor output frequency of the speed control subsystem or the hydraulic / pneumatic parameters of the pressure control subsystem. The correction response time is less than 1 second, quickly correcting parameter deviations and ensuring the stability and consistency of the formal wire drawing process.

[0027] After the formal wire drawing process is completed in step S7, the wire drawing-electroplating composite process is initiated, specifically including: S8: Multi-stage pretreatment of the wire-drawn metal sheet to remove contaminants and defects from the surface, creating favorable conditions for electroplating adhesion; S81: The first stage is alkaline degreasing treatment. The sheet is placed in a degreasing tank containing an alkaline degreasing agent composed of 5% sodium hydroxide, 3% sodium carbonate, 2% surfactant, and 90% water. Ultrasonic-assisted cleaning technology is used, where ultrasonic vibration at 28-40kHz generates microbubbles in the degreasing agent. The impact force generated when the bubbles burst enhances the penetrating power of the degreasing agent, quickly removing surface oil and wire drawing debris. The treatment time depends on the residual oil. The treatment time is dynamically adjusted. A visual inspection system is used to observe the surface of the sheet metal. When there is no obvious oil contamination, the treatment time is 5-8 minutes; when there is more oil contamination, the time is extended to 10-12 minutes. Compared to traditional immersion degreasing, this improves efficiency by 40% and can penetrate deep into the crevices of the brushed texture to remove contaminants. S82: The second stage is acidic oxide film removal treatment. An acidic solution suitable for the material is selected. For aluminum alloy sheets, a mixed solution of 10% phosphoric acid, 5% nitric acid, and 85% water is used; for stainless steel sheets, a solution of 15% hydrochloric acid and 85% water is used. A temperature control system maintains the solution temperature between 40-60℃. This temperature range accelerates the dissolution of the oxide film while preventing excessive corrosion of the sheet surface due to excessive temperature. During the treatment, a concentration sensor monitors the solution concentration in real time. The temperature sensor uses an electrode method for measurement with an accuracy of ±0.1%. When the concentration falls below a threshold, it automatically replenishes the original solution; for example, if the phosphoric acid concentration is below 8%, concentrated phosphoric acid is added to ensure stable oxide film removal and prevent oxide film residue due to insufficient concentration. S83: The third stage is activation treatment. An activator is sprayed onto the surface of the board using a mixture of 3% zinc chloride, 2% ammonium chloride, and 95% water. The spray pressure is set to 0.3 MPa, and the spray time is 2-3 minutes, forming a uniform active adsorption layer on the surface, improving the adhesion of subsequent electroplating layers. After activation, the surface is rinsed with pure water (resistivity greater than 15 MΩ·cm) for 3-5 minutes to remove residual activator and prevent oxidation. Residual chemical agents can cause defects such as pinholes and bubbles in the electroplating layer. S84: The fourth stage is drying treatment. The board is sent into a hot air circulating drying oven. The drying oven uses a hot air circulation system with a wind speed set to 2m / s. A gradient heating method is used, gradually increasing the temperature from room temperature to 40-50℃ and holding it for 3-5 minutes to remove free moisture from the surface. Then, the temperature is increased to 80-120℃ and held for 8-10 minutes to completely remove adsorbed moisture from the surface, avoiding deformation of the board due to excessive temperature difference. This method is especially suitable for thin boards with a thickness of less than 1mm. After drying, the surface moisture content is detected by a humidity sensor with an accuracy of ±1%, ensuring that the moisture content is below 0.5%. This provides a dry and clean surface for the subsequent electroplating process and improves the bonding strength between the electroplating layer and the substrate.

[0028] S9: Initiate the electroplating process parameter optimization process to ensure that the electroplating layer performance meets the requirements and adapts to the surface state after wire drawing; S91: The central processing unit retrieves the corresponding basic electroplating parameters from the electroplating process database based on the material type of the metal sheet and the microstructure of the surface after wire drawing. The electroplating process database stores mature process parameters for different materials and surface states, including electroplating solution composition, temperature, and current density. For example, when electroplating nickel onto copper alloy sheets, the basic electroplating solution composition is 200g / L nickel sulfate, 40g / L nickel chloride, and 30g / L boric acid, with the temperature controlled at 50℃ and the current density set at 1.5A / dm²; S92: Combining the surface state data of the pretreated sheet, including surface cleanliness and active adsorption layer thickness, the deposition rate of the electroplating layer is predicted using a numerical simulation algorithm. To assess the deposition rate and uniformity, this algorithm, based on Faraday's law of electrolysis and diffusion theory, simulates the deposition process of electroplating layers on the brushed texture surface under different parameters. If the simulation results show that the electroplating layer is too thin in the texture depressions, the current density is appropriately increased by 0.2-0.3 A / dm². If the surface electroplating layer thickness deviation is too large, the electroplating solution temperature is adjusted by ±2-3℃ to optimize the basic electroplating parameters and ensure that the electroplating layer uniformly covers the brushed surface. S93: The optimized electroplating parameters are transmitted to the control system of the electroplating equipment, and the electroplating layer thickness requirements are specified. The thickness of the decorative electroplating layer is set to 8-12 μm, and the thickness of the functional anti-corrosion electroplating layer is set to 15-20 μm, forming a personalized electroplating process plan. This provides clear guidance for subsequent electroplating operations and avoids electroplating quality problems caused by ambiguous parameters.

[0029] S10: Perform electroplating operations and quality monitoring, ensuring electroplating quality through standardized operations and real-time monitoring; S101: Fix the pre-treated qualified boards using hangers made of highly conductive brass. The hangers and boards use elastic clamps at the contact points, with clamping force controlled at 30-50N to ensure tight contact without damaging the board surface. Place the fixed boards into the electroplating tank, setting the distance between the boards and electrodes to 10-15cm to avoid excessively high local current density due to insufficient spacing. Simultaneously, ensure the boards are completely submerged in the electroplating solution with no exposed areas to prevent localized unplated defects; S102: Start the electroplating equipment and execute the electroplating operation according to the customized electroplating process plan. The purity of the electroplating solution is maintained through a circulating filtration system. The filtration accuracy of the circulating filtration system is set to 5-10μm, and the filtration frequency is no less than 5 times per hour, effectively removing impurities and particles from the electroplating solution. The pH sensor monitors the acidity and alkalinity of the electroplating solution in real time. The pH sensor has a measurement range of 0-14 and an accuracy of ±0.01pH. For example, the pH value of the nickel plating solution is controlled at 3.8-4.2, and the pH value of the copper plating solution is controlled at 1.8-2.2. When the pH value exceeds the range, acidic or alkaline regulators are automatically added to correct it, ensuring the stability of the electroplating process parameters and avoiding problems such as spots and uneven thickness of the electroplating layer caused by impurities or pH fluctuations. The thickness deviation of the electroplating layer is controlled within ±0.5μm. S103: After electroplating, remove the sheet from the electroplating tank and rinse it in the first-stage pure water rinsing tank for 3-5 minutes. The resistivity of the pure water is greater than 15 MΩ·cm to remove residual electroplating solution from the surface. Then, transfer it to the second-stage pure water rinsing tank and rinse for 2-3 minutes to ensure that the residue is completely removed. Subsequently, passivation treatment is performed using an environmentally friendly passivating agent, such as a chromate passivating agent with a concentration of 2%-3%, for 1-2 minutes to form a dense protective film on the surface of the electroplated layer, improving corrosion resistance. Finally, a second drying is performed, with the drying temperature controlled at 60-80℃ and held at that temperature for 5-8 minutes to avoid high temperature damage to the passivation film. After the second drying, an adhesion test is performed to confirm the bonding strength between the electroplated layer and the brushed surface, ensuring that no coating peels off. The passivation treatment can increase the salt spray corrosion resistance of the sheet to more than 500 hours, ensuring the long-term performance of the electroplated layer.

[0030] After the formal wire drawing process is completed in step S7, the integrated wire drawing-anodizing process is started, which specifically includes: S8: carrying out fine pretreatment of the wire-drawn board, optimizing the surface condition of the board through multi-stage fine treatment to create a high-quality substrate for the formation of the anodized film; S81: the first stage is high-pressure water cleaning, using high-pressure pure water of 3-5MPa with a resistivity greater than 10MΩ・cm. The surface of the board is rinsed in all directions through an adjustable nozzle, with the nozzle moving speed set at 0.5-1m / min to ensure that the rinsing time for each area is not less than 2 seconds; the high-pressure water flow can powerfully remove metal debris embedded in the texture gaps during the wire drawing process. During the rinsing process, the surface of the board is captured in real time by a visual recognition system. If debris is still detected, the nozzle angle and moving speed are adjusted, and the rinsing is repeated. Compared with traditional brush cleaning, texture damage can be avoided, and the debris removal rate can reach more than 99.5%. S82: The second stage is chemical degreasing. The high-pressure cleaned panels are placed in a chemical degreasing tank, and an environmentally friendly degreasing agent is selected. This degreasing agent is composed of 4% nonionic surfactant, 2% alkaline additive, and 94% water. The nonionic surfactant is biodegradable, reducing environmental pollution. A combination of immersion and spraying is used. First, the panels are immersed for 5-7 minutes to allow the degreasing agent to fully penetrate into the texture gaps. Then, they are sprayed through the spray system for 3-4 minutes to accelerate the removal of oil stains. The treatment time is set according to the surface oil stains. The degreasing effect is confirmed by conductivity testing. The conductivity measurement accuracy is ±1μS / cm. If the conductivity of the cleaning solution is stable, the degreasing is considered qualified, ensuring thorough degreasing and preventing oil stains from affecting the bonding of the oxide film. S83: The third stage is alkaline washing treatment. The degreased plates are placed in an alkaline washing tank containing sodium hydroxide solution, equipped with a real-time temperature and concentration monitoring system. The temperature sensor controls the solution temperature at 50-60℃, and the concentration sensor checks the solution concentration every 2 minutes to ensure stability. The processing time is adjusted according to the plate material: 3-4 minutes for aluminum alloy plates and 2-3 minutes for magnesium alloy plates. The corrosion rate is monitored in real time by a corrosion rate sensor. When the corrosion rate exceeds the preset threshold, the plate is immediately removed from the alkaline washing tank to avoid excessive corrosion. This process removes the surface oxide layer and activates the surface, while precisely controlling the amount of corrosion to ensure that the plate thickness deviation is within ±0.02mm. S84: The fourth stage is neutralization treatment. The alkaline-washed boards are placed in a neutralization tank containing a dilute acid solution. The dilute acid reacts with the residual alkali on the board surface to neutralize the alkaline substances. The treatment time is set to 1-2 minutes. After treatment, the pH of the board surface is tested with pH test paper to ensure that the pH value reaches the neutral range of 6.5-7.5. If the pH value is too alkaline, the neutralization time is extended; if it is too acidic, it is rinsed with pure water to neutralize, so as to avoid uneven subsequent oxide film caused by residual alkali and ensure the surface condition is stable.S85: The fifth stage is fine polishing. The neutralized board is transferred to the polishing station. Using an ultra-fine polishing agent, the surface of the board is lightly polished with a soft polishing wheel. The polishing pressure is set to 0.1-0.2MPa and the polishing speed is set to 100-150r / min. The polishing process only removes the tiny burrs and protrusions on the brushed surface without changing the overall brushed texture, thus optimizing the surface micro-smoothness. After polishing, the surface roughness Ra is measured by a surface roughness meter to ensure that it is controlled within 0.1-0.2μm, thereby increasing the contact area between the oxide film and the substrate. At the same time, it avoids over-polishing, which would damage the brushed texture, thus laying a high-quality surface foundation for anodizing.

[0031] S9: Intelligent matching of anodizing process parameters ensures the oxide film performance meets standards through precise parameter matching. S91: The central processing unit selects suitable electrolyte types from the electrolyte database based on the metal sheet material, determining the basic components and concentration range of the electrolyte to ensure matching of electrolyte and material characteristics, providing a foundation for stable oxide film formation. S92: Combining surface roughness data after wire drawing, the oxide film growth model predicts the thickness and density of the oxide film under different oxidation voltages and times, generating multiple sets of candidate process parameters. This provides multiple options for subsequent parameter optimization, reducing the risk of relying on a single parameter. S93: The historical process database is accessed, comparing the candidate process parameters with oxidation effect data of similar sheets. Machine learning algorithms score the effect of each set of candidate parameters, with scoring indicators including thickness uniformity, density, and production efficiency. The optimal parameters with the highest comprehensive score are selected, improving accuracy by more than 60% compared to manual experience selection, ensuring optimal anodizing process results.

[0032] S10: Perform anodizing and film strengthening operations to improve oxide film performance through standardized operation and strengthening treatment; S101: Install the pre-treated qualified plates on the anode bracket. The bracket is made of pure aluminum, and the connection between the plates and the bracket is fastened with bolts to ensure good conductivity; Place the bracket into the oxidation tank containing the appropriate electrolyte, and install the cathode plate in the oxidation tank. The distance between the cathode plate and the anode plate is set to 8-12cm, and the distance is ensured to be uniform to avoid uneven oxide film thickness caused by local current density differences. Under normal circumstances, the oxide film thickness deviation can be controlled within ±0.8μm. S102: Start the anodizing power supply and adjust the voltage and current according to the optimal process parameters. During the oxidation process, the electrolyte temperature is kept uniform through the circulation system, and the temperature fluctuation is controlled within ±1℃. Every 10 minutes, a small amount of electrolyte is extracted through the sampling tube, and the changes in electrolyte composition are analyzed by chemical titration. If the sulfuric acid concentration in the sulfuric acid electrolyte is lower than 15%, concentrated sulfuric acid is automatically added to the set concentration range to ensure the stability of the electrolyte composition. At the same time, the oxidation current is monitored in real time through the current sensor. If the current fluctuates abnormally, the contact status between the plate and the bracket or the purity of the electrolyte is checked immediately. The oxidation process is continued only after the fault is eliminated. This can avoid oxide film performance defects caused by electrolyte composition or current fluctuations. Under normal circumstances, the oxide film density compliance rate can be increased to over 98%. S103: After oxidation, remove the plate from the oxidation tank and rinse it in pure water for 3-5 minutes to remove residual electrolyte on the surface. Then, perform a sealing process, selecting the appropriate process according to the type of oxide film: For ordinary aluminum alloy oxide films, use a high-temperature sealing process, immersing the plate in deionized water at 95-100℃ for 20-30 minutes, allowing Al2O3 in the oxide film pores to react with water to generate AlO(OH), thus sealing the pores; for special functional oxide films, use a low-temperature sealing process, immersing the plate in nickel salt sealing solution at 25-35℃ for 15-20 minutes, blocking the pores through chemical deposition. After sealing, conduct wear resistance and corrosion resistance tests to confirm that the performance indicators of the oxide film meet the standards. If they do not meet the standards, repeat the sealing process. High-temperature or low-temperature sealing can reduce the porosity of the oxide film to below 1%, and improve wear resistance and corrosion resistance by more than 50% and 80%, respectively. S104: After sealing the holes, the surface of the board is cleaned and dried. First, the water droplets attached to the surface are blown off with compressed air to avoid water droplet residue causing local corrosion. Then, the board is placed in a constant temperature drying oven for low-temperature drying. The drying temperature is set at 60-80℃ and kept at that temperature for 8-10 minutes to completely remove surface moisture. During the drying process, avoid excessive temperature to prevent damage to the sealing layer structure. Ensure the stable bonding between the oxide film and the brushed surface. This can effectively ensure the integrity of the oxide film and prevent moisture residue from affecting the bonding strength. Under normal circumstances, the bonding force between the oxide film and the substrate can reach more than 5MPa.

[0033] After the formal wire drawing process is completed in step S7, the wire drawing-spraying composite process is started, which specifically includes: S8: Implementing pre-treatment for cleanliness control of the sheet metal after wire drawing, removing surface impurities through multi-stage cleaning treatment to create conditions for coating adhesion; S81: The first stage is electrostatic dust removal. The electrostatic dust removal equipment is started, which includes a high-voltage electrostatic generator and an electrostatic adsorption device; the sheet metal enters the electrostatic dust removal area through the conveying system, with the conveying speed set to 0.5-1m / min. The high-voltage electrostatic field charges the tiny particles on the surface of the sheet metal, and the charged particles are adsorbed onto the grounded adsorption plate under the action of the electric field force; after dust removal, the number of particles on the surface is detected by a particle counter to ensure that the number of particles per square meter is less than 100. If the number of particles exceeds the standard, the electrostatic voltage is adjusted or the dust removal time is extended. Compared with traditional blowing dust removal, it can remove smaller particles and avoid pinhole defects in the coating caused by particles. Under normal circumstances, the particle removal rate can reach more than 99%. S82: The second stage is plasma cleaning. The electrostatically dust-removed boards are sent into the plasma cleaning chamber, where low-temperature plasma is used to treat the surface of the boards. High-energy particles in the plasma bombard the surface, removing organic contaminants and thin oxide layers. The treatment time is set according to the degree of surface contamination: 3-5 minutes for light contamination and 5-8 minutes for heavy contamination. After treatment, the surface wettability is tested using a contact angle meter to ensure that the water contact angle is below 30°, proving that the surface cleanliness and activity meet the standards. No chemical agents are required, making it green and environmentally friendly. At the same time, it can improve surface wettability, creating good conditions for coating adhesion. Compared with chemical cleaning, it can improve coating adhesion by more than 40%. S83: The third stage is surface activation, in which an activator is used to wipe the surface of the board. The activator can form a chemical bond with the metal surface, further enhancing the surface activity. The activation operation is carried out by manual wiping with a lint-free cloth or by automated spraying. The spraying pressure is set to 0.2MPa to ensure that the activator evenly covers the surface. After activation, the coating process must be carried out within 1 hour to avoid prolonged exposure of the surface to re-adsorb contaminants or form a new oxide layer. The silane coupling agent can build a bonding bridge between the metal, activator and coating. At the same time, the aging control can avoid the decay of the activation effect and ensure the stable coating adhesion quality.

[0034] S9: Customize the spraying process solution, designing a dedicated spraying solution based on usage requirements; S91: The central processing unit determines the performance requirements of the sprayed coating based on the usage scenario of the metal sheet: for outdoor decorative parts, weather resistance and corrosion resistance are required; for appliance casings, wear resistance and scratch resistance are required; for electronic equipment components, insulation is required, ensuring that the coating performance accurately matches the actual usage requirements and avoiding over-design or insufficient performance. S92: Select candidate materials that meet the performance requirements from the spraying material database, including resin type, pigment and filler ratio, and curing agent type; predict the coating performance of different materials through performance simulation software, such as simulating the weather resistance decay trend of fluorocarbon resin coatings and the wear resistance of acrylic resin coatings, and compare and select the material with the best overall performance, avoiding performance risks caused by material selection based solely on experience. Simulation can eliminate substandard materials in advance, improving the efficiency of solution design. S93: Based on the texture depth and density data of the brushed surface, determine the spraying thickness parameters: the dry film thickness is set to 60-80μm to ensure that the coating can completely cover the gaps in the brushed texture without obscuring the three-dimensional texture; the spraying angle and spray gun movement speed are calculated through fluid dynamics simulation to avoid coating accumulation or missed spraying in textured depressions due to improper spraying angle, thus ensuring the protective function of the coating while preserving the aesthetic texture of the brushed surface. S94: Based on the characteristics of the spraying material, determine the curing temperature, curing time, and curing atmosphere: fluorocarbon resin coatings are cured at high temperature, while acrylic resin coatings are cured at room temperature or low temperature; the selected spraying materials, determined spraying parameters, and curing parameters are integrated to form a complete spraying process plan. The plan also clearly defines quality inspection standards and operating procedures, providing comprehensive guidance for spraying operations and avoiding processing deviations caused by missing parameters or ambiguous standards.

[0035] S10: Perform spraying operations and coating quality control, ensuring coating quality through standardized operations and rigorous testing; S101: Fix the pre-treated qualified panels on the spraying worktable. The worktable is equipped with adjustable clamps, and the panel position is calibrated through a vision positioning system to ensure that the center of the panel is aligned with the center of the spray gun's movement trajectory; For irregularly shaped panels, customized clamps are used for fixing to prevent panel displacement during spraying, ensuring that the spray gun trajectory matches the panel surface and avoiding uneven coating thickness caused by positioning deviations. Under normal circumstances, the positioning accuracy can reach 99.9%. S102: Start the spraying equipment and adjust the atomization pressure, spray flow rate, and movement trajectory of the spray gun according to the customized process plan; adopt a multi-pass spraying method, usually divided into 2-3 passes, with each pass having a dry film thickness of 20-30μm. Flash drying is performed after each pass. Flash drying allows some of the solvent in the coating to evaporate, avoiding defects such as sagging and pinholes in subsequent spraying. Multi-pass spraying can improve the uniformity and density of the coating. Under normal circumstances, the coating sagging rate can be controlled below 0.5%. S103: After spraying, the panels are sent to the curing equipment and the curing operation is performed according to the set curing parameters: During high-temperature curing, the temperature distribution inside the oven is monitored in real time by a temperature sensor to ensure uniform temperature in all areas of the panels; during room-temperature curing, the ambient humidity is controlled to ≤65% to avoid excessive humidity causing the coating to whiten; during the curing process, the curing time is precisely controlled by a timer, with the deviation controlled within ±1 minute for high-temperature curing and within ±2 hours for room-temperature curing, to ensure sufficient curing and avoid low coating hardness and poor adhesion due to insufficient curing, or embrittlement due to over-curing. Under normal circumstances, the curing pass rate can reach over 98%. S104: After curing, a comprehensive quality inspection of the coating is carried out: the adhesion test uses the cross-cut test; the hardness test uses the pencil hardness test; the thickness test uses a non-destructive thickness gauge; the appearance inspection uses a combination of high-definition camera and manual visual inspection to check for defects such as runs, pinholes, bubbles, and color differences; all tests must meet the standards. If any unqualified items are found, the cause is analyzed, rework is carried out, and the test is repeated until qualified. Through strict testing, the coating quality can be ensured to be stable and reliable, meeting the usage requirements.

[0036] When the metal sheet has a complex curved surface structure, the layered inspection process in step S2 is optimized as follows: S21: Start the three-dimensional scanning system and use laser scanning technology to perform a full-area scan of the complex curved surface metal sheet. The scanning system is equipped with a 6-axis robot carrying a laser scanner with a laser wavelength of 650nm, a scanning line width of 0.1mm, and a scanning accuracy controlled within 0.01mm. The scanning speed is set to 50mm / s to ensure that every detail of the curved surface can be accurately captured. During the scanning process, the robot drives the scanner to move around the sheet at multiple angles to generate a three-dimensional point cloud model of the sheet. The point cloud density reaches more than 10 points per square millimeter. Compared with traditional contact measurement, it can quickly obtain complete curved surface data without damaging the surface of the sheet, which is especially suitable for the inspection of precision curved surface parts. S22: Data processing of the 3D point cloud model is performed. First, a Gaussian filtering algorithm is used to remove noise points. Then, a voxel mesh simplification algorithm is used to reduce the number of point clouds. Finally, an iterative nearest point algorithm is used to stitch the point clouds together to ensure seamless fusion of point clouds scanned from different angles and generate a complete 3D geometric model. Various curvature feature parameters, such as the radius of curvature, rate of change of curvature, and slope of the surface, are extracted through model analysis software. This provides a geometric basis for the subsequent formulation of differentiated wire drawing strategies. The error of the processed model can be controlled within 0.02mm, ensuring accurate extraction of feature parameters. S23: A detection path is defined on the 3D geometric model. Path planning follows the principle of "densified sampling in high-curvature areas and sparse sampling in low-curvature areas." The sampling point spacing is 5mm in high-curvature variation areas and 10-15mm in low-curvature variation areas. Material identification sensors, thickness measurement sensors, and surface imaging sensors are deployed along the path. The sensors move along the path via a robotic arm to perform targeted detection on different areas of the curved surface, acquiring material properties, thickness data, and surface condition information for each area. Compared to uniform detection across the entire area, this improves detection efficiency by more than 30%, while ensuring detailed data for key areas. S24: Curvature feature parameters are correlated with the detection data of each area. Through database association technology, each detection point simultaneously contains information such as radius of curvature, rate of curvature change, material hardness, thickness value, and surface roughness, forming a comprehensive dataset containing both geometric features and physical properties. The dataset uses a 3D coordinate index, allowing for quick lookup of all parameters at the corresponding location. This provides complete data support for subsequent surface adaptation analysis, avoiding misjudgments of process parameters caused by the separation of geometric and physical parameters. Step S3's multi-dimensional analysis engine adds a surface adaptation analysis module: S31: This module inputs the comprehensive dataset into the surface wire drawing process model. This model is based on differential geometry and robot kinematics theory to establish the mathematical relationship between surface curvature and the motion parameters of the wire drawing tool. It analyzes the influence of surface curvature changes on the motion trajectory of the wire drawing tool. For example, high curvature areas require a smoother tool trajectory to avoid texture distortion caused by abrupt changes in trajectory curvature. At the same time, it calculates the optimal tool contact angle for different curvature areas to ensure that the tool fits tightly to the surface. Compared with the planar wire drawing model, it can more accurately adapt to surface characteristics and reduce texture defects.S32: Combining material hardness and thickness distribution data, the drawing difficulty of different areas of the curved surface is determined. Areas with high curvature changes are rated as high drawing difficulty due to frequent tool trajectory adjustments; thin-walled areas are rated as medium-high drawing difficulty due to easy deformation; and low-curvature thick-walled areas are rated as low drawing difficulty. High-difficulty areas are highlighted, and differentiated drawing strategies are developed: high-curvature changes use a low-speed, segmented continuous drawing mode; thin-walled areas use low pressure and increased support. This differentiated strategy reduces processing risks in high-difficulty areas and ensures consistent overall drawing results. Step S4's adaptive parameter generation algorithm adds a trajectory planning function: S41: Based on the 3D geometric model and surface curvature feature parameters, a B-spline curve interpolation algorithm is used to generate the baseline motion trajectory of the drawing tool. The interpolation node spacing is dynamically adjusted according to curvature changes: 2-3mm for high-curvature areas and 5-10mm for low-curvature areas. Through trajectory smoothness verification, the trajectory's fit with the curved surface is ensured to be higher than 95%. Compared to straight-segment splicing trajectories, this reduces the impact between the tool and the curved surface, avoiding texture breakage. S42: Based on the differentiated wire drawing strategy, the baseline trajectory is optimized. In areas with high curvature changes, the radius of curvature and the step length of the trajectory are adjusted to make the tool movement smoother. In thin-walled areas, the spacing of the trajectory is adjusted to reduce the number of repeated wire drawing and avoid deformation of the sheet metal. The optimized trajectory meets both geometric adaptation and process requirements, which can improve the texture qualification rate of high-difficulty areas to over 90%. S43: The optimized trajectory data is integrated with the wire drawing speed and pressure parameters to generate a complete parameter control instruction set including trajectory coordinates, speed changes, and pressure adjustments. The instruction set adopts a real-time transmission format to ensure that the robot can parse and execute it in real time, avoiding delays caused by large amounts of data and ensuring a smooth wire drawing process. In step S5, after the multi-degree-of-freedom robotic arm equipped with the wire drawing equipment receives the parameter control instruction set, it starts the trajectory tracking system: S51: The motion controller parses the trajectory coordinate data, converts the three-dimensional coordinates into angle values ​​of each joint of the robotic arm, uses inverse kinematics algorithm to calculate the joint motion parameters, and generates motion commands for each joint of the robotic arm. The acceleration is controlled at 0.5-1 rad / s² to avoid impact caused by excessive joint movement and ensure smooth trajectory execution. S52: The vision guidance system is activated, and the position of the board is captured in real time by an industrial camera. The deviation between the actual position of the board and the model position is obtained every 50ms. The motion trajectory of the robotic arm is dynamically calibrated through a feedback control algorithm. The calibration response time is <100ms and the compensation deviation range is ±0.1mm. This can effectively offset the board installation error or the robotic arm motion error, ensuring that the deviation between the actual trajectory and the command trajectory is ≤0.05mm, thus improving the accuracy of curved surface drawing.In the formal wire drawing process of step S7, force feedback control technology is adopted. A force sensor is installed at the end of the robotic arm to collect the force data of the end of the robotic arm and the plate in real time, with a sampling frequency of 100Hz. When the force exceeds the set range, the movement speed and pressure are automatically adjusted. The adjustment process continues until the force returns to the set range. Force feedback control can avoid uneven wire drawing or plate damage due to excessive contact force in curved areas, and improve the wire drawing qualification rate of complex curved parts to more than 95%, which is 20%-30% higher than that without feedback control.

[0037] When the metal sheet is an irregularly shaped part, the method further includes the following steps: S8: Conduct customized design of the clamping scheme for the irregularly shaped part, and ensure the processing stability of the irregularly shaped part through exclusive clamping design; S81: Obtain the three-dimensional model and structural feature data of the irregularly shaped part, including the outline dimensions of the irregular shape, the size and position coordinates of the protrusions / recesses, and the distribution range and area of ​​the thin-walled region; The data is obtained through three-dimensional scanning or CAD model import to ensure that no structural features are omitted, providing accurate basis for the design of the clamping device and avoiding clamping instability due to missing features. S82: Based on structural feature data, a topology optimization algorithm is used to design the structure of the clamping device. The optimization objective is to "minimize clamping deformation and maximize clamping stability". The constraint is that the maximum deformation of the irregular part after clamping is <0.1mm. The device includes a positioning base, an elastic clamping component, and an auxiliary support component. The materials of each component are selected according to the stress requirements: the positioning base is made of high-strength cast iron to ensure rigidity; the elastic clamping component is made of aluminum alloy as the base and the surface is covered with elastic material; the auxiliary support component is made of stainless steel to ensure corrosion resistance. Topology optimization can reduce the weight of the clamping device by 20%-30% while meeting the strength requirements. S83: The positioning base is designed with positioning grooves according to the bottom contour of the irregular part. The gap between the positioning groove and the bottom of the irregular part is controlled at 0.05-0.1mm to ensure the initial positioning accuracy after the irregular part is placed. The elastic clamping component is made of silicone or polyurethane. The shape of the clamping head is designed according to the surface morphology of the irregular part, with a contact area ≥20mm² to avoid damaging the surface. The auxiliary support component is designed with support points for thin-walled areas and protruding parts. The distribution of support points follows the principle of "uniform force" with a spacing of 50-100mm. Each support point can provide 5-10N of support force to prevent deformation during wire drawing. Through customized design, the deformation of the irregular part after clamping can be controlled within 0.05mm. S84: The clamping scheme is simulated using finite element analysis software, simulating the maximum pressure and tension during wire drawing. The stress distribution cloud map of the irregular part is analyzed. Special attention is paid to stress concentration areas, ensuring the stress value in these areas is below the material's yield strength. If the stress exceeds the standard, the support point positions are adjusted or the number of supports is increased, and the simulation is repeated until the standard is met. Finite element analysis can identify clamping design defects in advance, avoiding deformation or damage during actual processing and reducing trial-and-error costs. S9: Precise clamping operations are performed on the irregular part, ensuring the clamping effect meets design requirements through standardized operations. S91: The customized clamping device is installed on the worktable of the wire drawing equipment. The levelness of the clamping device is calibrated using a level, with the level deviation controlled within 0.05mm / m. The flatness of the clamping device is calibrated using a dial indicator, with the flatness deviation controlled within 0.05mm. After calibration, bolt fastening devices are used to prevent displacement during processing. Basic calibration ensures the positioning accuracy of the clamping device itself, providing a guarantee for the subsequent positioning of the irregular part.S92: The irregularly shaped part is placed in the positioning slot of the positioning base using a hoisting or robotic arm handling method. A lifting device is used during handling to avoid collision damage. Key feature points of the irregularly shaped part are photographed using a vision positioning system and compared with their theoretical positions in the 3D model. If deviations exist, the position of the irregularly shaped part is adjusted using a fine-tuning device until the deviation is ≤0.1mm. Precise positioning ensures that the subsequent wire drawing trajectory is consistent with the design trajectory, avoiding processing deviations. S93: The elastic clamping component is activated, using a step-by-step pressurization method. First, 50% of the set pressure is applied via pneumatic drive and held for 10 seconds. The displacement sensor monitors the displacement change of the irregularly shaped part. If the displacement is within the allowable range, the remaining 50% pressure is applied, with the final pressure deviation controlled within ±5N. If the displacement exceeds the allowable range, pressurization is paused, the contact state between the clamping head and the irregularly shaped part is checked, the clamping head position is readjusted, and pressurization continues until the set pressure is reached and the displacement meets the requirements. Step-by-step pressurization avoids deformation or displacement of the irregularly shaped part due to excessive instantaneous pressure, ensuring stable clamping. S94: Activate the auxiliary support assembly. A micro-cylinder drives the support head to contact the thin-walled areas and protruding parts of the irregularly shaped part. The support head uses a spherical design to avoid stress concentration caused by point contact. Apply a preset support force. During the support process, a pressure sensor monitors the magnitude of the support force in real time, controlling fluctuations within ±1N. If the support force is abnormal, check if the support head is making good contact, adjust, and reapply the support force. The auxiliary support can effectively counteract the pressure during the wire drawing process, keeping the deformation of the thin-walled area within 0.03mm. S10: Perform wire drawing processing and dynamic monitoring of the irregularly shaped part. Monitoring throughout the process ensures processing quality. S101: Following steps S5-S6, complete the parameter adjustment and trial run test of the wire drawing equipment. The trial run area is selected from the non-critical structural areas of the irregularly shaped part, with a trial run area of ​​50×50mm². Test parameters include wire drawing speed and pressure. The trial run verifies the clamping stability and parameter compatibility. The trial run can detect clamping or parameter problems in advance, avoiding processing failures in critical areas. S102: After the trial run meets the standards, the formal wire drawing process is started. During the wire drawing process, the multi-dimensional monitoring system collects data in real time: clamping stability data, force data of the wire drawing tool, and quality data of the wire drawing surface. The data is transmitted to the central processing unit in real time via industrial Ethernet with a transmission delay of <200ms, ensuring that abnormalities can be detected in a timely manner. S103: If clamping looseness or abnormal wire drawing quality is detected, the central processing unit immediately issues a pause command, and the equipment stops running; the cause of the abnormality is analyzed: if it is a clamping problem, the clamping parameters are readjusted; if it is a wire drawing parameter problem, the parameters are optimized; after the problem is solved, the wire drawing process is restarted, and the trial run test is repeated. After confirming that it is normal, processing continues until all areas of the irregular part are wire drawn. The dynamic monitoring and abnormal handling mechanism can increase the wire drawing qualification rate of irregular parts to over 92%, which is about 25% higher than the no-monitoring mode.

[0038] After completing the composite process of wire drawing, electroplating, anodizing, or spraying, a comprehensive quality inspection and process optimization workflow is initiated, specifically including: S11: Constructing a multi-level quality inspection system, which includes a basic inspection layer, a depth inspection layer, and a reliability inspection layer. Each inspection layer shares information and works collaboratively through data interfaces. The basic layer focuses on appearance and basic performance, the depth layer focuses on microscopic characteristics, and the reliability layer focuses on long-term performance, achieving a comprehensive evaluation of the composite coating and avoiding quality misjudgments caused by a single inspection dimension. S12: Executing the inspection operation of the basic inspection layer to quickly determine the basic quality of the coating; S121: The appearance inspection module uses a high-definition industrial camera and image analysis software to capture the entire surface of the metal sheet. During the shooting, multi-angle light sources are used to eliminate the influence of shadows. After generating a surface image dataset, image segmentation and feature extraction algorithms are used to identify whether the coating has appearance defects such as bubbles, cracks, pinholes, and color differences. The defect identification accuracy is ≥99%, which is more than 10 times more efficient than manual visual inspection and can avoid human error in detection. S122: The thickness detection module uses a non-destructive coating thickness gauge to measure the coating thickness at multiple points according to a preset grid path, with ≥100 measurement points per board. The measurement data is imported into data analysis software to generate a thickness distribution map, determining whether the coating thickness is uniform and meets design requirements. Thickness detection ensures that the coating's protective and decorative functions meet standards, avoiding early failure due to insufficient thickness. S123: The adhesion detection module selects either the cross-cut method or the pull-off method to test the coating's adhesion. The cross-cut method involves drawing a 10×10 grid on the coating surface using a cross-cutting device, applying 3M 600 tape, and then vertically peeling it off. The coating peeling is observed, and a rating is given based on the peeling area, requiring ≥1 level. The pull-off method involves attaching a test column to the coating surface using equipment, applying tension until the coating peels off, recording the tension value at peeling, and converting it into adhesion, requiring ≥3MPa. The adhesion test assesses the bonding strength between the coating and the substrate, preventing coating peeling during use. S13: Perform depth testing of the coating layer to analyze its internal quality. S131: The hardness testing module uses a microhardness tester to test the hardness of the coating cross-section. Test points are distributed on the surface, middle, and interface of the coating, with three tests performed at each point, and the average value is taken. The microhardness value of the coating is obtained, and the hardness distribution pattern is analyzed to determine the basic wear resistance performance. Hardness testing can predict the coating's service life; insufficient hardness will lead to decreased wear resistance. S132: The composition testing module uses an X-ray fluorescence spectrometer to analyze the elemental composition of the coating. The scanning time is 30-60 seconds to obtain the content percentage of each element. This confirms whether the coating composition is consistent with the design formula and whether there are any impurities. Composition testing ensures that the coating raw materials meet the requirements and avoids performance abnormalities caused by composition deviations.S133: The microstructure detection module uses a scanning electron microscope to observe the microscopic morphology and structure of the coating, and captures microscopic images of the coating cross-section and surface; it analyzes whether there are microscopic defects such as pores and delamination inside the coating, and evaluates the structural density of the coating; microstructure detection can reveal the intrinsic quality of the coating, and insufficient density will lead to a decrease in corrosion resistance. S14: Perform the reliability testing layer's testing operations, and verify the long-term performance of the coating through reliability testing; S141: The corrosion resistance testing module selects salt spray test, immersion test, or electrochemical corrosion test according to the board's usage scenario; the salt spray test uses a neutral salt spray chamber to conduct accelerated corrosion tests on the boards, regularly observes the corrosion status of the coating, and records the corrosion start time and corrosion degree; outdoor boards are required to have no obvious corrosion after 168 hours, and indoor boards are required to have no obvious corrosion after 96 hours; corrosion resistance testing can ensure the coating's protective ability in harsh environments. S142: The weathering resistance testing module uses a xenon lamp aging test chamber to simulate natural environmental factors such as sunlight, rain, and humidity to conduct aging tests on the panels. After the test, the changes in the appearance, adhesion, and hardness of the coating are detected to evaluate the coating's weathering performance. Outdoor decorative parts are required to meet performance standards after 1000 hours of aging to ensure that they do not fade or chalk after long-term use. S143: The abrasion resistance testing module uses a friction and wear testing machine to conduct friction and wear tests on the coating by setting different loads and friction cycles. The wear amount of the coating is calculated using the weighing method or surface profile measurement method to evaluate the coating's abrasion life. High-frequency used parts are required to have no exposed substrate after 2000 friction cycles to ensure that there are no obvious scratches after long-term use. S15: Conduct comprehensive analysis and process optimization of the test data to achieve continuous process improvement through data analysis. S151: The test data of each test layer is transmitted to the data analysis platform of the central processing unit. The platform uses multivariate statistical analysis algorithms to standardize the data and perform correlation analysis to identify key process parameters affecting coating quality. The accuracy rate of key parameter identification is ≥90%, providing a clear direction for subsequent optimization. S152: If the test results meet the preset quality standards, the process parameters and test data are stored in the process database. The database adopts a distributed storage architecture, supporting fast query and retrieval, and serves as a reference benchmark for subsequent processing of similar boards, improving the process stability of similar products. S153: If the test results do not meet the quality standards, a fault diagnosis algorithm is activated to analyze the correlation between non-conforming items and process parameters, determining the root cause of the quality problem; the fault diagnosis accuracy is ≥95%, avoiding blind parameter adjustments.S154: Based on the fault diagnosis results, generate targeted process optimization plans and adjust the corresponding process parameters; apply the optimization plan to the processing of the next batch of boards, and verify the effectiveness of the optimization plan by comparing the test data before and after optimization; if effective, update the optimization parameters to the process database; if ineffective, re-analyze the cause, forming a closed-loop process improvement mechanism of "detection-analysis-optimization-verification". Through this mechanism, the product qualification rate can be increased by 3%-5% per month, continuously improving the process level.

[0039] It also includes the steps of building an intelligent, flexible, and automated production line, specifically: S16: Planning the overall architecture of the production line, including a central control system, a logistics transmission system, a process execution system, a quality monitoring system, and a data management system. Each system achieves data interaction and collaborative control through the Industrial Internet, with a data transmission rate ≥100Mbps and latency ≤100ms. The production line layout adopts a U-shaped or linear structure, rationally arranging the positions of each system according to the process flow to reduce material transport distances and improve production efficiency, increasing space utilization by 20%-30% compared to traditional production lines. S17: Deploying the central control system, which serves as the core hub of the production line, uses an industrial computer equipped with a real-time operating system, including an intelligent scheduling module, a process management module, and an emergency handling module. S171: The intelligent scheduling module adopts a scheduling strategy combining genetic algorithms and particle swarm optimization algorithms, with 100-200 iterations and a convergence accuracy of 1e-5. Based on order requirements, equipment status, material inventory, and other information, it automatically generates the optimal production plan and task allocation scheme, rationally arranging the processing sequence of each board and equipment usage time, increasing equipment utilization to over 90% and shortening the production cycle by 15%-20%. S172: The process management module constructs a process parameter database and a process knowledge base. The database stores processing parameters, historical process data, and optimization schemes for various metal sheets. It can automatically call the corresponding process scheme based on the sheet type, with a response time of <1 second. It also supports manual adjustment of process parameters by staff through a human-machine interface, automatically recording parameter change logs after adjustments to ensure traceability and reduce human error. S173: The emergency handling module monitors the operating status of each system on the production line in real time, collecting parameters such as equipment temperature, pressure, and current through sensors. When equipment failure, material shortage, or quality abnormalities occur, an early warning mechanism is automatically triggered, alerting staff through system pop-ups. Simultaneously, an emergency handling plan is generated, suspending relevant processes in case of quality abnormalities to ensure the safe and stable operation of the production line, reducing downtime by more than 30%. S18: Deploy a logistics transmission system, which includes an automatic feeding unit, an intelligent conveying unit, and an automatic unloading unit; S181: The automatic feeding unit is equipped with a multi-degree-of-freedom robotic arm and a vision positioning system. The robotic arm picks up the metal plates to be processed from the material storage area, calibrates the position of the plates through vision positioning, and accurately places the plates on the conveyor belt with a placement deviation of ≤1mm; completes the automatic feeding operation with a feeding efficiency of ≥30 pieces / h, which is 50% more efficient than manual feeding and avoids surface contamination caused by human contact.S182: The intelligent conveying unit uses a magnetic levitation conveyor belt or a servo motor-driven conveyor belt with adjustable speed. Based on the central control system's scheduling instructions, it adjusts the conveyor belt's speed and transmission path to accurately deliver panels to each process station, with a positioning accuracy of ±5mm. During conveying, RFID tags or visual recognition technology are used to track the position and status of panels in real time, achieving 100% tracking accuracy and ensuring controllable material flow. S183: The automatic unloading unit is equipped with a classification and storage mechanism. Based on quality inspection results, qualified and unqualified panels are transported to their respective storage areas. Qualified panels are stored according to order numbers, while unqualified panels are marked with the reason and moved to the rework area or scrap area. Simultaneously, inventory data is automatically updated with a delay of <10 seconds, automating material management and reducing manual inventory work. S19: Deploy the process execution system, which includes equipment and auxiliary equipment corresponding to each process. Each piece of equipment is equipped with a local controller and a data acquisition module. S191: The local controller receives process parameter instructions from the central control system and controls the equipment to execute corresponding process operations through control algorithms. For example, the electroplating equipment performs electroplating parameter adjustments and electroplating operations, ensuring that the equipment operating parameters are consistent with the instructions, improving parameter control accuracy to ±1%. S192: The data acquisition module collects equipment operating parameters, process data, and board processing status data in real time, with a sampling frequency of 5-10Hz. This data is transmitted to the central control system and data management system via industrial Ethernet, achieving 100% data transmission accuracy. This provides complete data support for production monitoring and data analysis, enabling transparency in the production process. S20: Deploy the data management system, which adopts a cloud-edge collaborative architecture, including edge data processing nodes and a cloud data center. S201: The edge data processing nodes preprocess the real-time collected production data, with a processing latency of <1s, ensuring data accuracy and validity. Simultaneously, it enables local data storage and rapid retrieval, meeting real-time control requirements. S202: The cloud-based data center performs long-term storage and in-depth analysis of pre-processed production data. Through big data analytics algorithms and machine learning models, it uncovers patterns and trends in the production data, generating production reports, quality analysis reports, and process optimization suggestions. This provides data support for management decision-making, and data-driven optimization can improve production efficiency by 10%-15%. S203: The data management system possesses data security and traceability functions. Through data encryption and access control technologies, it ensures the security of production data. Simultaneously, it establishes a product traceability system. Through the unique identifier of each component, it allows tracing the production time, operators, equipment information, process parameters, and testing data of each component. The traceability response time is <3 seconds, meeting the needs of quality traceability and recall, and improving the level of product quality responsibility management.

[0040] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for surface wire drawing treatment of metal sheets, characterized in that, Includes the following steps: S1: Construct a communication architecture between the intelligent sensing system and the central processing unit, deploy a data preprocessing unit, and build a detection network through the sensor array to ensure the integrity and real-time performance of data acquisition; S2: Initiate the detection process of the intelligent sensing system to obtain information on the material properties, thickness distribution, and surface condition of the metal sheet, forming a complete detection dataset; S3: The detection dataset is transmitted to the central processing unit, which processes the data and extracts features, and analyzes the influence of each parameter on the wire drawing effect based on the preset model. S4: The central processing unit calls the parameter generation algorithm and combines the analysis results to generate a set of wire drawing parameter control instructions that include basic parameters, compensation parameters and emergency adjustment thresholds; S5: Transmit the parameter control instruction set to the control unit of the wire drawing equipment. After the control unit verifies that the instruction is correct, it starts the equipment preheating and performs the parameter adjustment operation. S6: After the parameters are adjusted, start the trial operation mode of the wire drawing equipment, conduct a small-scale wire drawing test on a designated area of ​​the metal sheet, and collect the wire drawing effect data of the test area; S7: The central processing unit receives the trial operation feedback data and compares it with the preset effect standard. If it meets the requirements, it issues a formal wire drawing command and the equipment enters the continuous wire drawing state. If it does not meet the requirements, it re-optimizes the parameters and repeats steps S5-S6 until the trial operation effect meets the standard.

2. The surface wire drawing treatment method for metal plates according to claim 1, characterized in that, In step S4, the grinding material adaptation database built in the central processing unit adopts a dynamic update architecture, which includes a basic adaptation module and a working condition correction module. The basic adaptation module stores the reference correlation between different materials, thicknesses, surface conditions and grinding material types, while the working condition correction module dynamically calibrates the reference correlation based on historical process data and environmental temperature and humidity parameters. S41: When generating the parameter control instruction set, the central processing unit first calls the basic adaptation module to match the current metal plate detection data with the benchmark correlation and initially screen out 3-5 candidate grinding materials. S42: Start the working condition correction module, input the current ambient temperature and humidity data and the historical process effect data of similar plates, and use a multi-factor regression algorithm to score the suitability of candidate grinding materials and select the target grinding material with the highest suitability. S43: Incorporate the information on the target grinding material's type, particle size, and replacement cycle into the parameter control instruction set to form a complete grinding material control instruction; In step S5, after receiving the grinding material control command, the local controller of the wire drawing equipment starts the automatic grinding material replacement system. This system includes a material identification unit, an automatic disassembly unit, and a precision installation unit. S51: The material identification unit uses image recognition technology to confirm the model of the currently installed grinding material and compares it with the target grinding material; S52: If the model does not match, the automatic disassembly unit will start the robotic arm to perform the disassembly operation and transfer the old grinding material to the waste recycling bin; S53: The precision installation unit picks up the target grinding material from the material storage bin, calibrates the installation position through the vision positioning system, and initiates pressure testing and concentricity detection after installation is completed; In step S7, during the formal wire drawing process, the real-time monitoring module continuously collects wear data of the grinding material. When the wear reaches the replacement cycle threshold, an early warning is automatically triggered to remind staff to replenish the grinding material in a timely manner.

3. The surface wire drawing treatment method for metal plates according to claim 1, characterized in that, In step S3, the central processing unit constructs a coupled analysis model of material hardness-thickness-surface roughness when analyzing the detection data; S31: Determine the yield strength and elastic modulus of the metal sheet by hardness test data, judge the rigidity difference in different areas of the sheet by combining the thickness distribution map, and determine the micro-undulation characteristics of the initial surface by surface roughness data. S32: Input the above data into the preset process calculation model. This model is based on the theory of elastic-plastic mechanics and the principle of friction and wear, and calculates the optimal wire drawing speed and pressure for different areas of the plate. S33: Integrate the calculation results of different regions to generate a wire drawing speed gradient distribution scheme and pressure dynamic adjustment scheme covering the entire plate, determine the upper limit, lower limit and adjustment rate of the wire drawing speed in the transition area, as well as the reference value, compensation value and allowable fluctuation range of the wire drawing pressure. In step S5, after receiving the speed and pressure control commands, the local controller of the wire drawing equipment starts the dual closed-loop control system, which includes a speed control subsystem and a pressure control subsystem. S51: The speed control subsystem collects the rotational speed data of the drawing roller in real time through the encoder, compares it with the speed gradient distribution scheme, and adjusts the output frequency of the drive motor through the PID algorithm to achieve precise control of the rotational speed. S52: The pressure control subsystem collects the contact pressure data between the wire drawing roller and the plate through a pressure sensor, and combines it with the dynamic pressure adjustment scheme to adjust the contact force in real time through a hydraulic or pneumatic pressure regulating mechanism to ensure that the pressure is stable within the set range. During the formal wire drawing process in step S7, the central processing unit receives speed and pressure data in real time through edge computing nodes and generates an adjustment curve every 5 seconds. If the data shows a deviation that exceeds the allowable fluctuation range, a correction command is immediately sent to adjust the speed and pressure parameters.

4. The surface wire drawing treatment method for metal plates according to claim 1, characterized in that, After the formal wire drawing process is completed in step S7, the wire drawing-electroplating composite process is started, which specifically includes: S8: performing multi-stage pretreatment on the wire-drawn metal sheet; S81: The first stage is alkaline degreasing treatment. The plates are placed in the degreasing tank and ultrasonic-assisted cleaning technology is used. The ultrasonic vibration of 28-40kHz enhances the penetration ability of the degreasing agent to remove surface oil and wire debris. The treatment time is dynamically adjusted according to the amount of oil residue. S82: The second stage is acidic oxide film removal treatment. An acidic solution suitable for the material is selected. The solution temperature is maintained at 40-60℃ through a temperature control system. The solution concentration is monitored in real time during the treatment process. When the concentration is lower than the threshold, the original solution is automatically replenished. S83: The third stage is the activation treatment, in which an activator is sprayed onto the surface of the board to form a uniform active adsorption layer. After activation, the board is rinsed with pure water to remove any residual activator. S84: The fourth stage is drying. The panels are sent into a hot air circulating drying oven and the temperature is gradually increased from room temperature to 80-120℃ using a gradient heating method to avoid deformation of the panels due to excessive temperature difference. After drying, the surface moisture content is detected by a humidity sensor to ensure that the moisture content is below 0.5%. S9: Initiate the electroplating process parameter optimization process; S91: The central processing unit retrieves the corresponding basic electroplating parameters from the electroplating process database based on the material type of the metal sheet and the surface microstructure after wire drawing, including the composition of the electroplating solution, temperature, and current density. S92: Combining the surface condition data of the pre-treated board, the deposition rate and uniformity of the electroplating layer are predicted by numerical simulation algorithm, and the basic electroplating parameters are optimized and adjusted. S93: Transmit the optimized electroplating parameters to the control system of the electroplating equipment to form a personalized electroplating process solution; S10: Perform electroplating operations and quality monitoring; S101: Fix the pre-treated qualified plates with hangers and place them in the electroplating tank to ensure good contact between the plates and the electrodes and avoid local abnormal current density. S102: Start the electroplating equipment and perform the electroplating operation according to the personalized electroplating process plan. During the process, the purity of the electroplating solution is maintained through a circulating filtration system, and the pH value of the electroplating solution is monitored in real time through a pH sensor to ensure parameter stability. S103: After electroplating, the plate is removed from the electroplating tank and then rinsed with pure water, passivated, and dried in sequence. The passivation treatment uses an environmentally friendly passivating agent to form a protective film on the surface of the electroplated layer. The temperature of the second drying is controlled at 60-80℃ to ensure the bonding strength between the electroplated layer and the brushed surface.

5. The surface brushing treatment method for metal plates according to claim 1, characterized in that, After the formal wire drawing process is completed in step S7, the integrated wire drawing-anodic oxidation process is started, which specifically includes: S8: Conduct refined pretreatment of the brushed sheet metal; S81: The first stage is high-pressure water cleaning, using 3-5MPa high-pressure pure water. The surface of the board is rinsed in all directions through an adjustable nozzle to remove metal debris left by wire drawing. The effect of debris removal is confirmed by a visual recognition system during the rinsing process. S82: The second stage is chemical degreasing, which uses environmentally friendly degreasing agents and combines soaking and spraying. The treatment time is set according to the surface oil stains. The degreasing effect is confirmed by conductivity testing after treatment. S83: The third stage is alkaline washing treatment. The plates are placed in an alkaline washing tank, which is equipped with a real-time temperature and concentration monitoring system. The alkaline concentration and treatment temperature are adjusted according to the material of the plates. The treatment time is controlled by a corrosion rate sensor to avoid excessive corrosion. S84: The fourth stage is neutralization treatment, which uses a dilute acid solution to neutralize the surface of the board and remove residual alkali. After neutralization, the surface acidity and alkalinity are tested with pH test paper to ensure that it reaches neutrality. S85: The fifth stage is fine polishing, which uses an ultra-fine particle polishing agent to lightly polish the surface of the board, optimize the surface micro-smoothness, and lay the foundation for anodizing. S9: Perform intelligent matching of anodizing process parameters; S91: The central processing unit selects a suitable electrolyte type from the electrolyte database based on the material of the metal plate and determines the basic components and concentration range of the electrolyte. S92: Combining the surface roughness data after wire drawing, the oxide film thickness and density under different oxidation voltages and times are predicted by the oxide film growth model, and multiple sets of candidate process parameters are generated. S93: Call the historical process database, compare the candidate process parameters with the oxidation effect data of similar plates, and use machine learning algorithms to select the optimal oxidation voltage, oxidation time and electrolyte temperature parameters. S10: Perform anodizing and film strengthening operations; S101: Install the pre-treated qualified plates on the anode bracket, ensuring good conductivity between the plates and the bracket. Place the bracket into the oxidation tank containing the appropriate electrolyte, and install the cathode plate at the same time, ensuring that the distance between the anode and cathode is uniform. S102: Start the anodic oxidation power supply, adjust the voltage and current according to the optimal process parameters, maintain the temperature of the electrolyte uniform through the circulation system during the oxidation process, monitor the changes in electrolyte composition through sampling and analysis, and replenish the consumed components in a timely manner; S103: After oxidation is completed, the plate is removed from the oxidation tank and sealed. High-temperature sealing or low-temperature sealing process is used. The appropriate sealing agent is selected according to the type of oxide film. After sealing, wear resistance test and corrosion resistance test are conducted to confirm that the performance indicators of the oxide film meet the standards. S104: After sealing the plate, clean and dry the surface. Blow away the surface moisture with compressed air, and then put it into a constant temperature drying oven for low-temperature drying to ensure the stable bonding between the oxide film and the brushed surface.

6. The surface brushing treatment method for metal plates according to claim 1, characterized in that, After the formal wire drawing process is completed in step S7, the wire drawing-spraying composite process is started, which specifically includes: S8: Implementing pretreatment to control the cleanliness of the sheet metal after wire drawing; S81: The first stage is electrostatic dust removal. The electrostatic dust removal equipment is started, and the tiny particles on the surface of the board are charged by a high-voltage electrostatic field. The charged particles are then removed by an adsorption device. After dust removal, the number of particles on the surface is detected by a particle counter to ensure that the number of particles per square meter is less than 100. S82: The second stage is plasma cleaning, which uses low-temperature plasma to treat the surface of the board. High-energy particles bombard the surface to remove organic contaminants and oxide layers, while improving the wettability of the surface. The treatment time is set according to the degree of surface contamination. S83: The third stage is surface activation, in which the surface of the board is wiped with an activator to further enhance the surface activity and lay the foundation for the adhesion of the spray coating. After activation, the board enters the spraying process within 1 hour to avoid surface contamination. S9: Customize the spraying process design; S91: The central processing unit determines the performance requirements of the spray coating based on the usage scenario of the metal sheet. S92: Select candidate materials that meet the performance requirements from the spraying material database, including resin type, pigment and filler ratio, and type of curing agent, and predict the coating performance of different materials using performance simulation software; S93: Combine the texture depth and density data of the brushed surface to determine the coating thickness parameters, ensuring that the coating can cover the texture without obscuring the brushed texture. At the same time, calculate the spraying angle and spray gun movement speed to avoid coating accumulation or missed spraying. S94: Based on the characteristics of the spraying material, determine the curing temperature, curing time, and curing atmosphere to form a complete spraying process plan; S10: Perform spraying operations and control coating quality; S101: Fix the pre-treated qualified panels on the spraying worktable and calibrate the position of the panels using a vision positioning system; S102: Start the spraying equipment, adjust the atomization pressure, spray flow and movement trajectory of the spray gun according to the customized process plan, adopt a multi-coat spraying method, and perform flash drying treatment after each coat to avoid coating sagging. S103: After the coating is completed, the panel is sent into the curing equipment and the curing operation is performed according to the set curing parameters. During the curing process, the curing temperature and time are monitored in real time to ensure that the curing is complete. S104: After curing, the coating is subjected to quality inspection, including adhesion testing, hardness testing, thickness testing, and appearance inspection.

7. The surface wire drawing treatment method for metal plates according to claim 1, characterized in that, When the metal sheet has a complex curved surface structure, the layer detection process in step S2 is optimized as follows: S21: Start the 3D scanning system and use laser scanning technology to perform a full-area scan on the complex curved metal plate. The scanning accuracy is controlled within 0.01mm, and a 3D point cloud model of the plate is generated. S22: Perform data processing on the 3D point cloud model, including denoising, simplification, and stitching, to generate a complete 3D geometric model. Extract various curvature feature parameters such as radius of curvature, rate of change of curvature, and slope of the surface in each region through model analysis software. S23: Set the detection path on the three-dimensional geometric model, and deploy material identification sensors, thickness measurement sensors and surface imaging sensors according to the path to perform targeted detection on different areas of the curved surface and obtain the material properties, thickness data and surface state information of each area; S24: Associate the curvature feature parameters with the detection data of each region to form a comprehensive dataset containing geometric features and physical properties; Step S3's multi-dimensional analysis engine adds a surface adaptation analysis module: S31: This module inputs the comprehensive dataset into the surface wire drawing process model, which is based on differential geometry and robot kinematics theory to analyze the influence of surface curvature changes on the motion trajectory of the wire drawing tool. S32: By combining material hardness and thickness distribution data, determine the difficulty of wire drawing in different areas of the curved surface, highlight areas with high curvature changes and thin-walled areas, and formulate differentiated wire drawing strategies. The adaptive parameter generation algorithm in step S4 adds trajectory planning functionality: S41: Based on the three-dimensional geometric model and surface curvature characteristic parameters, the B-spline curve interpolation algorithm is used to generate the reference motion trajectory of the wire drawing tool, ensuring that the trajectory fits the surface with a degree of greater than 95%. S42: Based on the differentiated wire drawing strategy, optimize the baseline trajectory, adjust the radius of curvature and movement step length of the trajectory in the high curvature change area, and adjust the spacing of the trajectory in the thin-walled area to avoid deformation of the sheet metal; S43: Integrate the optimized trajectory data with the wire drawing speed and pressure parameters to generate a complete parameter control instruction set including trajectory coordinates, speed changes, and pressure adjustments; In step S5, after receiving the parameter control instruction set, the multi-degree-of-freedom robotic arm equipped with the wire drawing equipment starts the trajectory tracking system: S51: The motion controller parses the trajectory coordinate data and generates motion commands for each joint of the robotic arm; S52: The vision guidance system is activated, and the position of the workpiece is captured in real time by an industrial camera to dynamically calibrate the motion trajectory of the robotic arm. In the formal wire drawing process of step S7, force feedback control technology is used to collect the end force data of the robotic arm in real time. When the force exceeds the set range, the movement speed and pressure are automatically adjusted to avoid uneven wire drawing or damage to the plate in the curved area.

8. The surface brushing treatment method for metal plates according to claim 1, characterized in that, When the metal sheet is an irregularly shaped part, the method further includes the following steps: S8: Conduct customized design of clamping solutions for irregularly shaped parts; S81: Obtain the 3D model and structural feature data of irregular parts, including irregular shape, size and location of protrusions / recesses, and distribution of thin-walled areas; S82: Based on structural feature data, the structure of the clamping device is designed using a topology optimization algorithm. The device includes a positioning base, an elastic clamping component, and an auxiliary support component. S83: The positioning base is designed with positioning grooves according to the bottom contour of the irregular part to ensure the initial positioning accuracy after the irregular part is placed; the elastic clamping component is made of silicone or polyurethane material, and the shape of the clamping head is designed according to the surface morphology of the irregular part to avoid damaging the surface; the auxiliary support component is designed with support points for thin-walled areas and protruding parts to prevent deformation during the wire drawing process. S84: The clamping scheme is simulated using finite element analysis software to verify the stress distribution of the irregular part after clamping, and to ensure that the stress value in the stress concentration area is lower than the yield strength of the material. S9: Perform precise clamping operations on irregularly shaped parts; S91: Install the customized clamping device on the worktable of the wire drawing equipment, and calibrate the levelness and flatness of the clamping device using a level and a dial indicator; S92: The irregularly shaped parts are placed in the positioning slot of the positioning base by means of hoisting or robotic arm handling. The initial positioning position is confirmed by the vision positioning system. If there is a deviation, it is fine-tuned. S93: Activate the elastic clamping component and use a step-by-step pressurization method. First, apply 50% of the set pressure and monitor the displacement change of the irregular part through the displacement sensor. If the displacement is within the allowable range, continue to apply the remaining 50% pressure. If the displacement exceeds the allowable range, stop pressurization, readjust the position of the clamping head, and then continue pressurization until the set pressure is reached and the displacement meets the requirements. S94: Activate the auxiliary support component, drive the support head to contact the thin-walled area and protrusion of the irregular part through the micro cylinder, apply the preset support force, and monitor the magnitude of the support force in real time through the pressure sensor during the support process to ensure that the support force is stable within the set range; S10: Perform wire drawing and dynamic monitoring of irregularly shaped parts; S101: Following the process of steps S5-S6, complete the parameter adjustment and trial operation test of the wire drawing equipment. The trial operation area should be the non-critical structural area of ​​the irregular part. S102: After the trial run meets the standards, the formal wire drawing process is started. During the wire drawing process, the multi-dimensional monitoring system collects data on the clamping stability of the irregular parts, the force data of the wire drawing tool, and the quality data of the wire drawing surface in real time. S103: If loose clamping or abnormal wire drawing quality is detected, the central processing unit immediately issues a pause command to analyze the cause of the abnormality. If it is a clamping problem, the clamping parameters are readjusted; if it is a wire drawing parameter problem, the parameters are optimized. After the problem is solved, the wire drawing process is restarted until all areas of the irregular part are wire drawn.

9. The surface brushing treatment method for metal plates according to claim 4, characterized in that, After completing the combined processes of wire drawing, electroplating, anodizing, or spraying, a comprehensive quality inspection and process optimization process is initiated, which specifically includes: S11: Construct a multi-level quality inspection system, which includes a basic inspection layer, a deep inspection layer, and a reliability inspection layer. Each inspection layer works together to achieve a comprehensive evaluation of the composite coating. S12: Perform the detection operation of the basic detection layer; S121: The appearance inspection module uses a high-definition industrial camera and image analysis software to capture the entire surface of the metal sheet, generate a surface image dataset, and identify appearance defects such as bubbles, cracks, pinholes, and color differences in the coating through image segmentation and feature extraction algorithms. S122: The thickness detection module uses a non-destructive coating thickness gauge to measure the coating thickness at multiple points according to a preset grid path, generate a thickness distribution map, and determine whether the coating thickness is uniform and meets the design requirements. S123: The adhesion testing module selects either the cross-cut method or the pull-off method according to the coating type to test the adhesion of the coating. The cross-cut method involves drawing a grid on the coating surface using a cross-cutting device, and then observing the coating peeling off after applying tape. The pull-off method involves applying a pulling force through the equipment and recording the pulling force value when the coating peels off, thereby evaluating the bonding strength between the coating and the metal substrate. S13: Perform the detection operation of the depth detection layer; S131: The hardness testing module uses a microhardness tester to test the hardness of the coating cross section, obtain the microhardness value of the coating, analyze the hardness distribution law of the coating, and determine the basic wear resistance performance of the coating; S132: The composition testing module uses an X-ray fluorescence spectrometer to analyze the elemental composition of the coating, confirm whether the coating composition is consistent with the design formula, and whether there are impurity elements. S133: The microstructure detection module uses a scanning electron microscope to observe the micromorphology and structure of the coating, analyze whether there are micro-defects such as pores and delamination inside the coating, and evaluate the structural compactness of the coating. S14: Perform the detection operation of the reliability detection layer; S141: The corrosion resistance testing module selects salt spray test, immersion test or electrochemical corrosion test according to the application scenario of the board to simulate harsh corrosion environment to accelerate corrosion test of the board, observe the corrosion of the coating regularly, and record the corrosion start time and corrosion degree. S142: The weather resistance testing module uses a xenon lamp aging test chamber to simulate natural environmental factors such as sunlight, rain, and humidity to conduct aging tests on the panels. After the test, the changes in the appearance, adhesion, and hardness of the coating are detected to evaluate the weather resistance performance of the coating. S143: The wear resistance testing module uses a friction and wear testing machine to set different loads and friction cycles to conduct friction and wear tests on the coating. The wear amount of the coating is calculated by weighing method or surface profile measurement method, and the wear resistance life of the coating is evaluated. S15: Conduct comprehensive analysis of testing data and optimize processes; S151: The detection data of each detection layer is transmitted to the data analysis platform of the central processing unit. The platform uses multivariate statistical analysis algorithms to standardize the data and perform correlation analysis to identify key process parameters that affect coating quality. S152: If the test results meet the preset quality standards, the process parameters and test data will be stored in the process database as a reference for the subsequent processing of similar panels. S153: If the test results do not meet the quality standards, activate the fault diagnosis algorithm, analyze the correlation between non-conforming items and process parameters, and determine the root cause of the quality problem; S154: Based on the fault diagnosis results, generate targeted process optimization plans, adjust the corresponding process parameters, apply the optimization plans to the processing of the next batch of boards, and verify the effectiveness of the optimization plans by comparing the test data before and after optimization, forming a closed-loop process improvement mechanism of "detection-analysis-optimization-verification".

10. The surface brushing treatment method for metal plates according to any one of claims 1-9, characterized in that, It also includes the steps of building intelligent, flexible, and automated production lines, specifically including: S16: Plan the overall architecture of the production line, including the central control system, logistics transmission system, process execution system, quality monitoring system and data management system, and realize data interaction and collaborative control through the industrial internet; S17: Deploy a central control system, which serves as the core hub of the production line and includes an intelligent scheduling module, a process management module, and an emergency response module; S171: The intelligent scheduling module adopts a scheduling strategy that combines genetic algorithm and particle swarm optimization algorithm. Based on order demand, equipment status and material inventory information, it automatically generates the optimal production plan and task allocation scheme, and reasonably arranges the processing order of each board and equipment usage time. S172: The process management module constructs a process parameter database and a process knowledge base, storing the processing process parameters, historical process data and optimization schemes for various metal plates. It can automatically call the corresponding process scheme according to the plate type, and also supports staff to manually adjust the process parameters. After adjustment, the parameter change log is automatically recorded. S173: The emergency response module monitors the operating status of each system on the production line in real time. When equipment failure, material shortage, or quality abnormality occurs, the early warning mechanism is automatically triggered to remind staff through audible and visual alarms and system pop-ups. At the same time, an emergency response plan is generated. When there is a material shortage, a material purchase request is triggered. When there is a quality abnormality, the relevant process is suspended to ensure the safe and stable operation of the production line. S18: Deploy a logistics transmission system, which includes an automatic feeding unit, an intelligent conveying unit, and an automatic unloading unit; S181: The automatic feeding unit is equipped with a multi-degree-of-freedom robotic arm and a vision positioning system. The robotic arm picks up the metal sheet to be processed from the material storage area, calibrates the position of the sheet through vision positioning, and accurately places the sheet on the conveyor belt to complete the automatic feeding operation. S182: The intelligent conveying unit uses a magnetic levitation conveyor belt or a servo motor to drive the conveyor belt. According to the scheduling instructions of the central control system, it adjusts the running speed and transmission path of the conveyor belt to accurately transport the board to each process station. During the conveying process, the position and status of the board are tracked in real time through RFID tags or visual recognition technology. S183: The automatic unloading unit is equipped with a classification and storage mechanism. According to the quality inspection results, qualified and unqualified boards are transported to the corresponding storage areas. Qualified boards are classified and stored according to the order number. Unqualified boards are marked with the reason and then transferred to the rework area or scrap area. At the same time, the inventory data is automatically updated. S19: Deploy the process execution system, which includes equipment and auxiliary equipment corresponding to each process. Each piece of equipment is equipped with a local controller and a data acquisition module. S191: The local controller receives process parameter instructions from the central control system and controls the equipment to execute the corresponding process operations. The electroplating equipment performs electroplating parameter adjustment and electroplating operations. S192: The data acquisition module collects the equipment's operating parameters, process data, and board processing status data in real time, and transmits them to the central control system and data management system via industrial Ethernet to provide data support for production monitoring and data analysis. S20: Deploy a data management system that adopts a cloud-edge collaborative architecture, including edge data processing nodes and a cloud data center; S201: Edge data processing nodes preprocess real-time acquired production data to ensure data accuracy and validity, while enabling local data storage and rapid retrieval to meet real-time control requirements; S202: The cloud data center performs long-term storage and in-depth analysis of pre-processed production data. Through big data analysis algorithms and machine learning models, it uncovers patterns and trends in the production data and generates production reports, quality analysis reports, and process optimization suggestions. S203: The data management system has data security and traceability functions. Through data encryption and access control technologies, it ensures the security of production data and establishes a product traceability system. Through the unique identifier of the board, the production time, operators, equipment information, process parameters, and test data of the board can be traced.

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