Continuous production performance improving method for intelligent elastic sheet electroplating production line

Abstract

The invention relates to a method for improving continuous production performance of an intelligent elastic sheet electroplating production line in the technical field of production of precise hardware elastic sheets, which comprises the following steps: acquiring a real-time completion signal from stamping equipment, judging whether a stamping elastic sheet enters a transmission preparation state or not, and if the stamping signal is positive, judging whether the stamping elastic sheet enters the transmission preparation state; if so, directly conveying the elastic sheet to an electroplating unit through a closed conveyor belt system to obtain a material flow without an exposed environment; a completion signal is obtained from the electroplating unit, the electroplated elastic sheet is transferred to a detection station through a multi-axis mechanical arm system, the surface and the thickness of the elastic sheet are scanned through the high-precision visual identification technology, whether an electroplated layer is uniform or not is judged, and qualified electroplated elastic sheet data are obtained; the method comprises the following steps: acquiring data of a production line in synchronous operation, analyzing real-time power consumption of each unit through an energy consumption monitoring system, judging whether a high-energy-consumption node exists or not, and if so, redistributing electroplating and transmission power through a load balancing algorithm to obtain energy-saving production line configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of production of precision hardware shrapnel, and in particular to a method for improving the continuous production performance of an intelligent shrapnel electroplating production line. Background Art

[0002] Research on integrated continuous production lines for stamping and electroplating springs is crucial to the modern electronics manufacturing industry, directly impacting the performance and reliability of signal transmission components in consumer electronics products such as mobile phones, tablets, and Bluetooth headsets. As a core component for antenna signal transmission and conductive contact, the production efficiency and quality of springs are crucial to the competitiveness of end products.

[0003] However, in traditional production methods, the stamping and electroplating processes are separated from each other, resulting in a long turnover time for materials between processes, which not only reduces production efficiency, but also makes the surface easily oxidized or contaminated due to exposure to the air, affecting the stability of the shrapnel performance. This separate production model can no longer meet the current needs of rapid iteration and high quality of electronic products. The limitations of existing solutions are mainly reflected in the inefficiency of process connection and the lack of surface quality control. Traditional processes rely on manual or mechanical handling to complete the material transfer between stamping and electroplating, which is time-consuming. In addition, during the handling process, the surface of the shrapnel is easily affected by the external environment, resulting in oxidation or contamination, which reduces the quality of subsequent electroplating.

[0004] Furthermore, traditional electroplating equipment is typically designed for a single specification, making it difficult to quickly adapt to the production needs of different types of shrapnel. This leads to lengthy line changeover times, limiting the flexibility and versatility of the production line. The core challenges lie in achieving a seamless integration between stamping and electroplating processes, improving the adaptability of electroplating equipment to multiple types of shrapnel, and optimizing production control to ensure a balance between efficiency and quality. Material exposure issues caused by process separation increase the risk of surface contamination, while the lack of modular design in electroplating equipment limits the ability to quickly change lines, making it difficult to meet the demands of small-batch production of multiple varieties.

[0005] At the same time, the lack of real-time monitoring and dynamic adjustment mechanisms during the production process makes it difficult to achieve an optimal balance between production capacity and energy consumption. Therefore, key issues in improving the performance of integrated continuous production lines for stamping and electroplating of springs include how to ensure contamination-free transfer of springs to the electroplating process immediately after stamping, how to design a flexible electroplating system to support rapid line changeovers, and how to optimize production cycle time through intelligent control. Summary of the Invention

[0006] The present invention provides a method for improving the continuous production performance of an intelligent shrapnel electroplating production line, which mainly includes:

[0007] Obtain real-time completion signals from the stamping equipment to determine whether the stamped springs have entered the transfer preparation state. If the stamping signal is positive, the springs are directly transported to the electroplating unit through the closed conveyor system to obtain a material flow without exposure to the environment;

[0008] The position data of the shrapnel transported by the conveyor system is obtained, and the surface condition of the shrapnel is scanned by an optical sensor to determine whether there are signs of oxidation or contamination on the surface. If an abnormality is detected, the shrapnel is decontaminated by a preset cleaning module to obtain a clean shrapnel surface;

[0009] Obtain processed shrapnel data from the cleaning module, load the target shrapnel type parameters through the configuration interface of the modular electroplating unit, and determine whether the parameters match the current production requirements. If so, start the electroplating liquid spraying and current control to obtain the adapted electroplating process flow;

[0010] Acquire the operating status data of the modular electroplating unit and use a real-time monitoring system to record the electroplating solution concentration and current fluctuations to determine whether the process parameters are within the preset threshold range. If the threshold is exceeded, the dynamic adjustment algorithm is used to optimize the liquid flow and current distribution to achieve stable electroplating quality;

[0011] Obtain completion signals from the electroplating unit, transfer the electroplated shrapnel to the inspection station via a multi-axis robotic arm system, and use high-precision visual recognition technology to scan the surface and thickness of the shrapnel to determine whether the electroplating layer is uniform, and obtain qualified electroplated shrapnel data;

[0012] Obtain inspection data from the visual recognition system and analyze the qualified rate and cycle time of electroplated shrapnel through the production control platform to determine whether production efficiency has reached the preset target. If it is lower than the target, the conveyor belt speed and electroplating unit configuration are adjusted through the cycle optimization algorithm to obtain the optimized production cycle.

[0013] Obtain optimized beat data from the production control platform, and use the data feedback mechanism to transmit the adjustment parameters back to the stamping and electroplating units to determine whether the units are operating synchronously. If not, the synchronization control algorithm is used to reallocate task priorities to achieve a coordinated production line operation status.

[0014] Acquire data from synchronously running production lines, analyze the real-time power consumption of each unit through the energy consumption monitoring system, and determine whether there are high-energy-consuming nodes. If so, redistribute the electroplating and transmission power through the load balancing algorithm to obtain an energy-saving production line configuration.

[0015] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0016] The present invention discloses a method for improving the continuous production performance of an intelligent shrapnel electroplating production line, in which the stamped shrapnel is directly transported to the electroplating unit through a closed conveyor belt, thereby realizing a material flow without an exposed environment. The present invention adopts an optical sensor to detect the surface state of the shrapnel, and performs cleaning treatment when an abnormality is found. The electroplating unit automatically configures the process flow according to the shrapnel parameters, and monitors the concentration and current of the electroplating solution in real time, and ensures the electroplating quality through a dynamic adjustment algorithm. The present invention also uses visual recognition technology to detect the electroplating effect, analyze production efficiency and optimize the beat, coordinate the synchronous operation of each unit, and achieve energy consumption balance. The system realizes the intelligent and refined control of the entire process of shrapnel electroplating production, improves production efficiency and product quality, reduces energy consumption, and has significant technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flowchart of a method for improving the continuous production performance of an intelligent shrapnel electroplating production line.

[0018] Figure 2 This is a schematic diagram of a method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to the present invention.

[0019] Figure 3 This is another schematic diagram of a method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to the present invention. DETAILED DESCRIPTION

[0020] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention.

[0021] like Figure 1-3 In this embodiment, a method for improving the continuous production performance of an intelligent spring electroplating production line may specifically include:

[0022] S101. Obtain a real-time completion signal from the stamping equipment to determine whether the stamped spring piece has entered a transfer preparation state. If the stamping signal is positive, the spring piece is directly transported to the electroplating unit through a closed conveyor belt system to obtain a material flow without exposure to the environment.

[0023] A real-time completion signal is obtained from the stamping equipment. A signal processing module analyzes the completion signal to determine whether it is positive, thereby determining whether the stamped spring has entered a transfer preparation state. Based on this determination, if the completion signal is positive, a preset control instruction activates the closed conveyor system, obtains the operating status of the conveyor system, and confirms that the stamped spring has entered the transfer process. The conveyor system's sensors obtain the position information of the stamped spring, and a positioning algorithm is used to track this position information in real time to obtain dynamic position data of the stamped spring within the conveyor system. The conveying speed of the stamped spring is extracted from this dynamic position data, and a speed analysis algorithm is used to determine whether the conveying speed is stable, thereby determining the stability of the stamped spring's transfer. Based on this stability determination, if the conveying speed is stable, the stamped spring is transported to the electroplating unit via the end interface of the conveyor system, and the receiving status of the electroplating unit is obtained to determine whether the stamped spring has entered the electroplating process. Material flow environment data is obtained from the electroplating unit, and an environmental monitoring algorithm is used to determine whether an exposed environment exists, thereby determining whether the material flow is free of exposed environments. According to the material flow determination result, if there is no exposure environment, the material flow information is stored through the data recording module to generate complete process data of the stamping spring from stamping to electroplating.

[0024] Specifically, in the stamping equipment, high-precision sensors are installed to monitor the stamping completion signal in real time. When the sensor detects that the stamping force reaches the set threshold (such as 5000N), the system automatically determines that the stamping shrapnel has been formed. At this time, the system sends instructions through the PLC controller to start the closed conveyor belt system. The conveyor belt runs at a constant speed (such as 0.5m / s) to ensure that the shrapnel is stably transported in an unexposed environment. The conveyor belt system is equipped with an infrared detection device to monitor the position of the shrapnel in real time. When it is detected that the shrapnel reaches the end of the conveyor belt, the system automatically triggers the electroplating unit start signal. The electroplating unit is based on preset parameters (such as current density 2A / dm 2 , electroplating time 120s) to ensure a uniform coating on the shrapnel surface. Throughout the entire process, the system uses a data acquisition module to record parameters at each stage in real time and optimizes process parameters using data analysis algorithms (such as the least squares method) to improve production efficiency and product quality. This automated process achieves a seamless transition from stamping to electroplating, ensuring a continuous and stable material flow.

[0025] S102. Obtain the position data of the shrapnel transported by the conveyor system, use an optical sensor to scan the surface state of the shrapnel, and determine whether there are traces of oxidation or contamination on the surface. If an abnormality is detected, the shrapnel is decontaminated by a preset cleaning module to obtain a clean shrapnel surface.

[0026] The conveyor belt system acquires the position data of the shrapnel, and a positioning algorithm is used to determine the coordinates of the shrapnel on the conveyor belt, obtaining the shrapnel position coordinates. Based on the shrapnel position coordinates, an optical sensor is controlled to scan the shrapnel surface, acquiring surface scan data and obtaining a shrapnel surface status image. Based on the shrapnel surface status image, an image processing algorithm is used to analyze surface features. If traces of oxidation or contamination are detected, a surface abnormality is determined to exist, obtaining an abnormality detection result. Based on the abnormality detection result, if a surface abnormality exists, a preset cleaning module is activated to decontaminate the shrapnel by spraying cleaning fluid, obtaining a preliminarily cleaned shrapnel. Based on the preliminarily cleaned shrapnel, an optical sensor is used to scan the surface again, acquiring secondary scan data and obtaining a secondary surface status image. Based on the secondary surface status image, an image processing algorithm is used to analyze surface features. If no traces of oxidation or contamination are detected, the shrapnel surface is determined to be clean, obtaining a cleaned shrapnel surface. Based on the cleaned shrapnel surface, its position data is acquired, and the shrapnel is conveyed to the next process via the conveyor belt system, obtaining a conveyance completion status.

[0027] Specifically, within the conveyor system, the shrapnel's position data is collected in real time by an encoder that outputs a pulse signal every 10 milliseconds. Combined with the conveyor speed of 0.5 m / s, the Kalman filter algorithm dynamically corrects the position, achieving a positioning accuracy of ±0.1 mm. An optical sensor uses a linear array CCD to scan the shrapnel surface at a resolution of 2000 DPI, with each pixel corresponding to an actual size of 5 microns. Surface feature values are extracted using HSV color space conversion. When the chromaticity (H) value exceeds the preset range (normal value 110-130 degrees) or the saturation (S) value falls below the threshold of 60, it is determined to be oxidized or contaminated. For abnormal areas, a morphological opening operation is used to eliminate noise, and the contaminated area percentage is calculated. If it exceeds 5%, the cleaning module is triggered. The cleaning module is positioned using a three-axis robotic arm. The nozzle sprays an alkaline cleaning agent with a pH of 8.5 at a pressure of 0.3 MPa. The spray time is linearly calculated based on the contaminated area (0.1 seconds per square millimeter). The cleaning process is then dried with 40°C hot air. A second scan verifies the cleaning effect, ensuring that the surface reflectivity has been restored to above 85%. During the entire process, the PLC controller synchronizes the status of each device through the Modbus protocol, and abnormal data is automatically recorded in the SQL database and a trend analysis report is generated.

[0028] S103. Obtain the processed shrapnel data from the cleaning module, load the target shrapnel type parameters through the configuration interface of the modular electroplating unit, and determine whether the parameters match the current production requirements. If they match, start the electroplating liquid spraying and current control to obtain an adapted electroplating process flow.

[0029] The processed shrapnel data is obtained from the cleaning module, and the shrapnel feature parameters are extracted using a data analysis algorithm to obtain a shrapnel feature set. The target shrapnel type parameters are loaded through the electroplating unit configuration interface, and a parameter comparison algorithm is used to determine whether the shrapnel feature set matches the target shrapnel type parameters to obtain a matching result. If the matching result is true, the target formula is obtained from the preset electroplating liquid formula library, and the electroplating liquid spraying parameters are determined using a formula analysis tool to obtain a spraying parameter set. According to the spraying parameter set, the working state of the spraying equipment is adjusted using a spraying control algorithm to obtain a spraying execution instruction. Through the current control module, a current optimization algorithm is used to adjust the current parameters according to the spraying execution instruction to obtain a current control instruction. According to the spraying execution instruction and the current control instruction, the electroplating equipment is started to perform the electroplating operation to obtain an electroplating process flow. Real-time data of the electroplating process flow is obtained from the electroplating equipment, and a data verification tool is used to determine whether the electroplating process flow meets the preset standards to obtain a process verification result.

[0030] Specifically, the processed shrapnel data is obtained from the cleaning module. Assuming that the surface cleanliness of the shrapnel reaches more than 95% and there are no visible stains, the target shrapnel type parameters are loaded through the configuration interface of the modular electroplating unit. For example, the shrapnel material is stainless steel, the thickness is 0.5 mm, and the target electroplating layer thickness is 10 microns. The system uses a matching algorithm to compare the loaded parameters with the current production requirements. If the shrapnel material, thickness and electroplating layer thickness are all within the allowable error range (±2%), the parameters are determined to match. Subsequently, the system starts the electroplating solution spraying and controls the spraying speed to 50 ml per minute to ensure that the electroplating solution evenly covers the surface of the shrapnel. At the same time, the current control system calculates and sets the current density to 2 amperes per square decimeter and the electroplating time to 5 minutes based on the composition of the electroplating solution and the surface area of the shrapnel to ensure that the electroplating layer thickness reaches the target value. Throughout the process, the system monitors the pH value and temperature of the electroplating solution in real time to ensure that the pH value is maintained between 4.5 and 5.5 and the temperature is controlled between 25°C and 30°C to ensure the quality of electroplating. Ultimately, the system generates an adapted electroplating process flow, including key parameters such as spraying speed, current density, and electroplating time, and records them in the database for subsequent production reference and optimization.

[0031] S104. Obtain the operating status data of the modular electroplating unit, use a real-time monitoring system to record the electroplating solution concentration and current fluctuations, and determine whether the process parameters are within the preset threshold range. If the threshold is exceeded, the liquid flow and current distribution are optimized through a dynamic adjustment algorithm to obtain stable electroplating quality.

[0032] The operating status data of the modular electroplating unit is obtained, and the plating solution concentration and current fluctuation data are recorded through a real-time monitoring system to obtain an original process parameter data set. The plating solution concentration and current fluctuation data are extracted from the original process parameter data set, and compared using a preset threshold database to determine whether the plating solution concentration and the current fluctuation data are within the preset threshold range, thereby obtaining a parameter status indicator. If the parameter status indicator indicates that the plating solution concentration exceeds the preset threshold, the current liquid flow distribution data is obtained through a liquid flow sensor, and the deviation of the liquid flow distribution data is analyzed using a support vector machine algorithm to obtain a liquid flow optimization adjustment parameter. If the parameter status indicator indicates that the current fluctuation data exceeds the preset threshold, the current current distribution data is obtained through a current sensor, and the deviation of the current distribution data is analyzed using a support vector machine algorithm to obtain a current optimization adjustment parameter. Based on the liquid flow optimization adjustment parameter, the liquid flow control module adjusts the plating solution circulation rate to obtain optimized liquid flow distribution data. Based on the current optimization adjustment parameter, the current control module adjusts the current distribution of the electroplating unit to obtain optimized current distribution data. The optimized liquid flow distribution data and the optimized current distribution data are collected by the real-time monitoring system, and compared with the preset threshold database to determine whether the electroplating quality is stable, thereby obtaining a stable electroplating quality state.

[0033] Specifically, during the operation of the modular electroplating unit, the real-time monitoring system collects plating solution concentration and current fluctuation data through sensors. For example, the plating solution concentration range is 200g / L to 250g / L, and the current fluctuation range is ±5A. The system uses the Kalman filter algorithm to process the data to eliminate noise interference and ensure the accuracy of the data. Through the preset threshold range, if the plating solution concentration exceeds 230g / L or the current fluctuation exceeds ±3A, the system will trigger the dynamic adjustment algorithm. The algorithm is based on the PID control principle. By adjusting the speed of the liquid flow pump and the output of the current source, the plating solution concentration is stabilized at 220g / L and the current fluctuation is controlled within ±2A. The system analyzes the adjusted data in real time to ensure the stability of the electroplating quality, such as the uniformity of the plating layer thickness reaches ±0.01mm and the surface roughness is less than 0.5μm. Through this closed-loop control, the modular electroplating unit can achieve efficient and stable production to meet the needs of high-quality electroplated products.

[0034] S105. Obtain the completion signal from the electroplating unit, transfer the electroplated shrapnel to the inspection station through the multi-axis robotic arm system, use high-precision visual recognition technology to scan the surface and thickness of the shrapnel, determine whether the electroplating layer is uniform, and obtain qualified electroplated shrapnel data.

[0035] A completion signal is obtained from the electroplating unit, and the signal processing module is used to analyze the content of the completion signal to determine the completion status of the electroplating process. The completion status is received by a multi-axis robotic arm control system, which generates motion instructions and controls the robotic arm to execute the shrapnel transfer, thereby positioning the shrapnel at the inspection station. High-precision visual recognition technology is used to perform a surface scan on the shrapnel positioned at the inspection station, obtaining surface image data and generating a first image set. An image processing algorithm is used to perform denoising and edge detection on the first image set to generate a second image set and determine the distribution of surface feature points. If the distribution of feature points in the second image set meets a preset uniformity threshold, a deep learning algorithm is used to analyze the uniformity of the electroplated layer on the second image set to obtain a uniformity score. High-precision visual recognition technology is used to perform a thickness scan on the shrapnel to obtain thickness data and generate a thickness distribution dataset. If the value in the thickness distribution dataset is within the preset thickness threshold, the uniformity score and the thickness distribution dataset are combined to determine whether the shrapnel electroplating layer is qualified, and qualified electroplated shrapnel data is generated.

[0036] Specifically, after obtaining the completion signal from the electroplating unit, the multi-axis robotic arm system accurately transfers the electroplated shrapnel from the electroplating tank to the inspection station through a preset path planning algorithm. The motion trajectory of the robotic arm adopts closed-loop control based on the PID control algorithm to ensure that the positioning accuracy is within the range of ±0.1 mm. At the inspection station, the high-precision visual recognition system is started, and a 5-megapixel industrial camera is used to scan the surface of the shrapnel and collect image data. The image processing algorithm uses edge detection and grayscale analysis technology based on OpenCV to judge the uniformity of the electroplating layer by calculating the standard deviation of the surface grayscale value. It is judged to be uniform when the standard deviation is less than 0.05. At the same time, the laser thickness gauge measures the thickness of the shrapnel with an accuracy of 0.01 mm and makes a judgment based on the preset thickness range (0.1 mm to 0.15 mm). All test data is transmitted to the MES system in real time through the PLC system to generate qualified electroplated shrapnel data and automatically record it in the database for subsequent production analysis and traceability.

[0037] S106. Obtain the detection data of the visual recognition system, analyze the qualified rate and cycle time of the electroplated shrapnel through the production control platform, and determine whether the production efficiency has reached the preset target. If it is lower than the target, adjust the conveyor belt speed and electroplating unit configuration through the cycle optimization algorithm to obtain the optimized production cycle.

[0038] The detection data generated by the visual recognition system is obtained, and the surface defect characteristics and dimensional deviation data of the electroplated shrapnel are extracted to obtain an initial detection data set. The initial detection data set is statistically analyzed through the production control platform, and the qualified rate and cycle time of the electroplated shrapnel are calculated to obtain a production efficiency index. If the production efficiency index is lower than the preset target, the support vector machine algorithm is used to classify the production parameters according to the deviation of the qualified rate and cycle time to determine the key influencing factors. Based on the key influencing factors, the current values of the conveyor belt speed and the electroplating unit configuration are extracted to obtain a parameter set to be optimized. A genetic algorithm is used to iteratively optimize the conveyor belt speed and the electroplating unit configuration in the parameter set to be optimized to obtain an optimized production parameter combination. The optimized production parameter combination is applied to the electroplating production line through the production control platform, and the cycle time is updated to obtain an optimized production cycle. New detection data under the optimized production cycle is obtained, and the statistical analysis is repeated to determine whether the production efficiency has reached the preset target and obtain the final production status.

[0039] Specifically, the visual recognition system uses a high-precision industrial camera to capture images of electroplated shrapnel at a rate of 30 frames per second, and uses the YOLOv5 algorithm to detect surface defects of the shrapnel in real time, with a detection accuracy of 99.2%. When pits with a diameter of more than 0.1mm or a thickness deviation of more than ±0.05mm are detected on the surface of the shrapnel, it is marked as unqualified. The production control platform summarizes the inspection data every 5 minutes. Through statistical analysis, it is found that the qualified rate of the current batch of 5,000 shrapnel is 92.3%, which is lower than the preset target value of 95%. The platform calls the beat time analysis module and calculates that the current average beat time is 8.6 seconds / piece, which is different from the target beat of 8.0 seconds / piece. The system automatically triggers the beat optimization algorithm and uses the genetic algorithm to optimize the conveyor belt speed. Under the constraints (the plating solution temperature is maintained at 55±2℃ and the current density is 15A / dm 2 ), after 50 iterations of calculation, the conveyor belt speed was increased from 1.2m / s to 1.35m / s, and the spacing between plating cells was shortened from 0.8m to 0.72m. After optimization, simulations showed a reduction in cycle time to 7.9 seconds per piece, and an estimated yield rate of 95.6%. The system automatically sent the optimized parameters to the PLC for execution and continuously monitored production data from three subsequent batches to verify the optimization results.

[0040] S107. Obtain optimized beat data from the production control platform, and use the data feedback mechanism to transmit the adjustment parameters back to the stamping and electroplating units to determine whether the units are running synchronously. If not, reallocate task priorities through the synchronization control algorithm to obtain a coordinated production line operation status.

[0041] Obtain optimized beat data from the production control platform, analyze the operating beat value of each unit in the data, and obtain the beat sequence of each unit. According to the beat sequence, calculate the beat difference between the stamping unit and the electroplating unit. If the difference exceeds the preset threshold, it is judged to be out of sync, and a synchronization evaluation result is obtained. According to the synchronization evaluation result, generate parameter adjustment instructions for the stamping unit and the electroplating unit. Adopt a synchronous control algorithm, recalculate the task priority according to the parameter adjustment instruction, and obtain an optimized task allocation sequence. According to the task allocation sequence, update the operating parameters of the stamping unit and the electroplating unit, and obtain the updated unit operation status. Collect the operation data corresponding to the updated unit operation status through the production control platform, and analyze the beat consistency of each unit. Then extract the beat deviation data, iteratively adjust the task priority, and obtain the final synchronous operation result.

[0042] Specifically, the production control platform collects real-time cycle data from the stamping unit via the OPC UA protocol. For example, if the current stamping cycle is 12.3 seconds and the plating unit cycle is 14.1 seconds, a sliding window algorithm is used to calculate the standard deviations of the last 30 cycles, which are 0.8 seconds and 1.2 seconds, respectively. When the cycle difference between the two units exceeds a preset threshold of 1.5 seconds, an adjustment parameter is generated based on the PID control algorithm: the pressure valve opening of the stamping unit is reduced from 65% to 58%, and the plating tank temperature setpoint is increased from 45°C to 48°C via the Modbus TCP protocol. The synchronization control module uses a modified Hungarian algorithm for task redistribution, calculating that shifting 20% of the production capacity of stamping press No. 3 to plating tank No. 5 can reduce the overall cycle difference to 0.3 seconds. The system monitors the status of the adjusted equipment in real time, showing that the stamping unit current fluctuation range has been reduced from ±5A to ±2A, and the pH value of the plating unit has stabilized at 7.2±0.1. A Kalman filter algorithm predicts that the synchronization error for the next cycle will be within 0.5 seconds. When an unexpected downtime occurs, a dynamic scheduling model based on reinforcement learning generates a new production plan within 300 milliseconds. For example, it reassigns order B-203, originally scheduled for press No. 4, to press No. 2 and simultaneously increases the plating solution circulation pump frequency from 35Hz to 42Hz to compensate for the lost production capacity. All adjustment parameters are encrypted and transmitted via the Industrial Internet of Things gateway. The MES system records the timestamp, operator ID, and equipment response curve of each adjustment, forming a closed-loop control link.

[0043] S108. Obtain data from synchronously running production lines, analyze the real-time power consumption of each unit through the energy consumption monitoring system, and determine whether there are high-energy-consuming nodes. If so, redistribute the electroplating and transmission power through the load balancing algorithm to obtain an energy-saving production line configuration.

[0044] Real-time operation data of the production line is obtained, and the power and operation status of each unit are collected through sensors to obtain a set of original data. The energy consumption monitoring system is used to perform data analysis on the original data set, calculate the real-time power consumption of each unit, and obtain the unit power consumption distribution. If there is a unit in the unit power consumption distribution whose power consumption exceeds a preset threshold, it is determined to be a high-energy-consuming node, and a list of high-energy-consuming nodes is obtained. For the list of high-energy-consuming nodes, a load balancing algorithm is used to optimize the power distribution of the electroplating unit and the transmission unit to obtain a preliminary power adjustment plan. According to the preliminary power adjustment plan, the adjusted power consumption distribution is simulated to determine whether the power consumption of all units is lower than the preset threshold, and a verified power distribution plan is obtained. The verified power distribution plan is implemented through the production line control system, the actual power of the electroplating unit and the transmission unit is adjusted, and an energy-saving production line configuration is obtained. The operation data of the energy-saving production line configuration is obtained, and the real-time power consumption is analyzed by the energy consumption monitoring system to determine whether the energy-saving configuration is stable, and the final optimized configuration is obtained.

[0045] Specifically, the energy consumption monitoring system collects real-time power consumption data from each unit of the production line. For example, the electroplating unit consumes 12.5 kilowatts, the conveying unit consumes 8.3 kilowatts, and the cleaning unit consumes 6.7 kilowatts. The system uses a K-means clustering algorithm to perform cluster analysis on this data, classifying the power consumption data into three categories: high, medium, and low. The electroplating unit is identified as a high-energy-consuming node. To optimize energy consumption, the system uses a load balancing algorithm to redistribute power between the electroplating and conveying units based on the workload and power consumption characteristics of each unit.

[0046] For example, the power of the electroplating unit was reduced from 12.5 kW to 10.8 kW, while the power of the conveyor unit was increased from 8.3 kW to 9.2 kW to ensure that the overall efficiency of the production line was not affected. Through this dynamic adjustment, the system can achieve an energy-efficient production line configuration, with an estimated overall energy consumption reduction of 15%. At the same time, the system continuously monitors the adjusted power consumption data to ensure the effectiveness of the load balancing algorithm and conducts further optimization when necessary.

[0047] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for improving the continuous production performance of an intelligent shrapnel electroplating production line, characterized in that: The method comprises the following steps: S101, obtaining a real-time completion signal from the stamping equipment to determine whether the stamped spring piece has entered a transfer preparation state. If the stamping signal is positive, the spring piece is directly transported to the electroplating unit via a closed conveyor system to obtain a material flow without exposure to the environment; S102, obtaining position data of the shrapnel conveyed by the conveyor system, scanning the surface condition of the shrapnel using an optical sensor to determine whether there are traces of oxidation or contamination on the surface, and if an abnormality is detected, performing a decontamination treatment on the shrapnel using a preset cleaning module to obtain a clean shrapnel surface; S103, obtaining the processed shrapnel data from the cleaning module, loading the target shrapnel type parameters through the configuration interface of the modular electroplating unit, and determining whether the parameters match the current production requirements. If so, starting the electroplating solution spraying and current control to obtain an adapted electroplating process flow; S104, obtaining operational status data of the modular electroplating unit, using a real-time monitoring system to record electroplating solution concentration and current fluctuations, and determining whether process parameters are within a preset threshold range. If the threshold is exceeded, a dynamic adjustment algorithm is used to optimize the liquid flow and current distribution to achieve stable electroplating quality; S105. Obtain a completion signal from the electroplating unit, transfer the electroplated shrapnel to the inspection station via a multi-axis robotic arm system, and use high-precision visual recognition technology to scan the surface and thickness of the shrapnel to determine whether the electroplating layer is uniform, thereby obtaining qualified electroplated shrapnel data. S106. Obtaining detection data from the visual recognition system, analyzing the qualified rate and cycle time of the electroplated shrapnel through the production control platform, and determining whether the production efficiency has reached the preset target. If it is lower than the target, adjusting the conveyor belt speed and electroplating unit configuration through the cycle optimization algorithm to obtain an optimized production cycle; S107. Obtain optimized beat data from the production control platform, and use a data feedback mechanism to transmit the adjusted parameters back to the stamping and electroplating units to determine whether the units are operating synchronously. If not, reallocate task priorities through a synchronization control algorithm to achieve a coordinated production line operation status. S108. Obtain data from synchronously running production lines, analyze the real-time power consumption of each unit through the energy consumption monitoring system, and determine whether there are high-energy-consuming nodes. If so, redistribute the electroplating and transmission power through the load balancing algorithm to obtain an energy-saving production line configuration.

2. The method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to claim 1, characterized in that: The S101 includes: Acquire a real-time completion signal from the stamping equipment, analyze the completion signal using a signal processing module, determine whether the completion signal is positive, and obtain a determination result of whether the stamping spring piece has entered a transfer preparation state; According to the determination result, if the completion signal is positive, the closed conveyor system is activated through a preset control instruction, the operating status of the conveyor system is obtained, and the stamped spring piece is determined to enter the conveying process; Acquiring the position information of the stamped spring piece through the sensor of the conveyor system, and tracking the position information in real time using a positioning algorithm to obtain the dynamic position data of the stamped spring piece in the conveyor system; Extracting the conveying speed of the stamped spring piece from the dynamic position data, and using a speed analysis algorithm to determine whether the conveying speed is stable, thereby obtaining a stability determination result of the conveying of the stamped spring piece; According to the stability determination result, if the conveying speed is stable, the stamped spring piece is conveyed to the electroplating unit through the end interface of the conveyor system, the receiving state of the electroplating unit is obtained, and it is determined that the stamped spring piece enters the electroplating process; Acquiring material flow environment data from the electroplating unit, using an environmental monitoring algorithm to determine whether an exposed environment exists, and obtaining a material flow determination result indicating no exposed environment exists; According to the material flow determination result, if there is no exposure environment, the material flow information is stored through the data recording module to generate complete process data of the stamping spring from stamping to electroplating.

3. The method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to claim 1, characterized in that: The S102 includes: The position data of the shrapnel is obtained through the conveyor belt system, and the coordinates of the shrapnel on the conveyor belt are determined by using a positioning algorithm to obtain the coordinates of the shrapnel position; According to the position coordinates of the shrapnel, the optical sensor is controlled to scan the surface of the shrapnel, and the surface scanning data is obtained to obtain the surface state image of the shrapnel; An image processing algorithm is used to analyze the surface characteristics of the shrapnel surface state image. If traces of oxidation or contamination are detected, it is determined that a surface abnormality exists, and an abnormality detection result is obtained. According to the abnormality detection result, if there is a surface abnormality, the preset cleaning module is activated to perform a decontamination treatment on the shrapnel by spraying a cleaning liquid to obtain a preliminary clean shrapnel; For the preliminary cleaning shrapnel, the optical sensor is used to scan the surface again to obtain secondary scanning data and obtain a secondary surface state image; Analyzing the surface features using an image processing algorithm based on the secondary surface state image, and if no traces of oxidation or contamination are detected, determining that the shrapnel surface is clean, thereby obtaining a clean shrapnel surface; According to the surface of the cleaning shrapnel, its position data is obtained, and it is transported to the next process through the conveyor system to obtain a transport completion state.

4. A method for improving the continuous production performance of an intelligent spring electroplating production line according to any one of claims 1 to 3, characterized in that: The S103 includes: Obtain processed shrapnel data from the cleaning module, extract shrapnel feature parameters using a data analysis algorithm, and obtain a shrapnel feature set; Load the target shrapnel type parameters through the electroplating unit configuration interface, use the parameter comparison algorithm to determine whether the shrapnel feature set matches the target shrapnel type parameters, and obtain a matching result; If the matching result is true, the target formula is obtained from a preset electroplating solution formula library, and the electroplating solution spraying parameters are determined using a formula parsing tool to obtain a spraying parameter set; According to the spray parameter set, a spray control algorithm is used to adjust the working state of the spray equipment to obtain a spray execution instruction; By using a current optimization algorithm through a current control module, the current parameters are adjusted according to the spray execution instruction to obtain a current control instruction; According to the spray execution instruction and the current control instruction, the electroplating equipment is started to perform the electroplating operation to obtain the electroplating process flow; Real-time data of the electroplating process flow is obtained from the electroplating equipment, and a data verification tool is used to determine whether the electroplating process flow meets the preset standards to obtain a process verification result.

5. A method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to any one of claims 1 to 3, characterized in that: The S104 includes: Obtain the operating status data of the modular electroplating unit, record the electroplating solution concentration and current fluctuation data through the real-time monitoring system, and obtain the original process parameter data set; Extracting plating solution concentration and current fluctuation data from the original process parameter data set, comparing them with a preset threshold database, determining whether the plating solution concentration and the current fluctuation data are within a preset threshold range, and obtaining a parameter status identifier; If the parameter status indicator shows that the concentration of the electroplating solution exceeds a preset threshold, current liquid flow distribution data is obtained through a liquid flow sensor, and a support vector machine algorithm is used to analyze the deviation of the liquid flow distribution data to obtain liquid flow optimization adjustment parameters; If the parameter status indicator shows that the current fluctuation data exceeds a preset threshold, current current distribution data is obtained through a current sensor, and a support vector machine algorithm is used to analyze the deviation of the current distribution data to obtain a current optimization adjustment parameter; According to the liquid flow optimization adjustment parameters, the electroplating solution circulation rate is adjusted by the liquid flow control module to obtain optimized liquid flow distribution data; According to the current optimization adjustment parameters, the current distribution of the electroplating unit is adjusted by the current control module to obtain optimized current distribution data; The optimized liquid flow distribution data and the optimized current distribution data are collected by the real-time monitoring system, and compared with the preset threshold database to determine whether the electroplating quality is stable, thereby obtaining a stable electroplating quality state.

6. A method for improving the continuous production performance of an intelligent spring electroplating production line according to any one of claims 1 to 3, characterized in that: The step S105 includes: Obtaining a completion signal from the electroplating unit, and using a signal processing module to analyze the content of the completion signal to determine the completion status of the electroplating process; The multi-axis robotic arm control system receives the completion status, generates motion instructions, and controls the robotic arm to execute the spring fragment transfer, thereby obtaining the spring fragment positioned at the inspection station; Using high-precision visual recognition technology to perform surface scanning on the shrapnel positioned at the detection station, obtaining surface image data, and generating a first image set; Performing denoising and edge detection on the first image set using an image processing algorithm to generate a second image set and determine the distribution of surface feature points; If the distribution of feature points in the second image set meets a preset uniformity threshold, a deep learning algorithm is used to perform electroplating layer uniformity analysis on the second image set to obtain a uniformity score; Using high-precision visual recognition technology to scan the thickness of the shrapnel, obtain thickness data, and generate a thickness distribution data set; If the value in the thickness distribution data set is within a preset thickness threshold range, the uniformity score and the thickness distribution data set are combined to determine that the electroplated layer of the shrapnel is qualified, and qualified electroplated shrapnel data is generated.

7. A method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to any one of claims 1 to 3, characterized in that: The S106 includes: Obtain the inspection data generated by the visual recognition system, extract the surface defect characteristics and dimensional deviation data of the electroplated shrapnel, and obtain the initial inspection data set; Performing statistical analysis on the initial test data set through the production control platform, calculating the qualified rate and cycle time of the electroplated shrapnel, and obtaining the production efficiency index; If the production efficiency index is lower than the preset target, the production parameters are classified using a support vector machine algorithm based on the deviation between the qualified rate and the cycle time to determine the key influencing factors; Based on the key influencing factors, extract the current values of the conveyor belt speed and the electroplating unit configuration to obtain a parameter set to be optimized; Iteratively optimizing the conveyor belt speed and electroplating unit configuration in the set of parameters to be optimized using a genetic algorithm to obtain an optimized production parameter combination; Applying the optimized production parameter combination to the electroplating production line through the production control platform, updating the takt time, and obtaining an optimized production takt; Obtain new test data under the optimized production rhythm, repeat statistical analysis, determine whether the production efficiency reaches the preset target, and obtain the final production status.

8. A method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to any one of claims 1 to 3, characterized in that: The S107 includes: Obtain optimized beat data from the production control platform, analyze the operating beat value of each unit in the data, and obtain the beat sequence of each unit; Calculating the beat difference between the stamping unit and the electroplating unit according to the beat sequence; if the difference exceeds a preset threshold, determining that they are out of sync, and obtaining a synchronization evaluation result; generating parameter adjustment instructions for the stamping unit and the electroplating unit according to the synchronization evaluation result; Using a synchronous control algorithm, recalculating the task priorities according to the parameter adjustment instructions to obtain an optimized task allocation sequence; According to the task allocation sequence, the operating parameters of the stamping unit and the electroplating unit are updated to obtain the updated unit operating status; Collect the operation data corresponding to the updated unit operation status through the production control platform and analyze the beat consistency of each unit; Then, the beat deviation data is extracted, the task priority is iteratively adjusted, and the final synchronous operation result is obtained.

9. A method for improving the continuous production performance of an intelligent shrapnel electroplating production line according to any one of claims 1 to 3, characterized in that: The S108 includes: Obtain real-time operation data of the production line, collect the power and operating status of each unit through sensors, and obtain the original data set; Performing data analysis on the raw data set through an energy consumption monitoring system, calculating the real-time power consumption of each unit, and obtaining a unit power consumption distribution; If there is a unit in the unit power consumption distribution whose power consumption exceeds a preset threshold, it is determined to be a high-energy-consuming node, and a high-energy-consuming node list is obtained; Based on the list of high-energy-consuming nodes, a load balancing algorithm is used to optimize the power distribution of the electroplating unit and the transmission unit to obtain a preliminary power adjustment plan; According to the preliminary power adjustment plan, simulate the adjusted power consumption distribution, determine whether the power consumption of all units is lower than a preset threshold, and obtain a verified power allocation plan; Implementing the verified power allocation scheme through the production line control system, adjusting the actual power of the electroplating unit and the conveying unit, and obtaining an energy-saving production line configuration; The operation data of the energy-saving production line configuration is obtained, and the real-time power consumption is analyzed through the energy consumption monitoring system to determine whether the energy-saving configuration is stable, so as to obtain the final optimized configuration.

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