Control method of electroplating production line with optimized PLC (Programmable Logic Controller) program

Abstract

The invention relates to a control method for an electroplating production line with an optimized PLC program. A multi-dimensional data analysis model is adopted, key parameters such as electroplating time, hanger circulation speed and bath occupation condition are comprehensively considered, and a process time relevance matrix is constructed; self-adaptive rhythm adjustment is carried out in the PLC, and the transmission rhythm is dynamically adjusted to enable circulation among the process nodes to be balanced; a multi-channel data acquisition system is connected to the PLC end, and the temperature, the current density, the voltage fluctuation, the plating solution concentration and the pH value key variables of the plating solution are monitored; a Bayesian optimization algorithm is adopted, electroplating process parameters are calculated based on historical data and real-time data, and an output control value of a PLC is dynamically adjusted; carrying out sampling analysis on the current consumption mode of each electroplating bath by adopting a PLC (Programmable Logic Controller), and establishing a power demand prediction model; current output is distributed through a dynamic power distribution algorithm, so that power consumption is kept in an optimal state; and in combination with a peak load reduction strategy, power supply is optimized in low load.

Description

Technical Field

[0001] The present invention relates to a control method for an electroplating production line, specifically a control method for an electroplating production line with optimized PLC program. Background Art

[0002] At present, the control methods for electroplating production lines with optimized PLC programs have improved the production automation level to a certain extent, but there are still many deficiencies and drawbacks, which affect production efficiency, energy utilization rate, and overall intelligent level. First of all, the traditional PLC control mode still mainly relies on fixed rule settings and is difficult to accurately optimize in real time for complex production rhythms and current scheduling requirements. For example, on many electroplating production lines, the PLC system usually controls the operation rhythm of each process station according to preset time parameters, and these parameters are often set based on experience and are difficult to adapt to the actual demand changes of different batches of products. When the production rhythm fluctuates, such as changes in the transfer speed of the hanging tool, changes in the plating solution temperature, or current load fluctuations, the fixed rhythm control method may cause some process stations to be overloaded or have too long waiting times, thus affecting the overall production efficiency. In addition, the lag in rhythm adjustment is also a major drawback of the current PLC optimization methods. After the existing system detects uneven production rhythms, it usually relies on manual intervention or simple time adjustment strategies and is difficult to dynamically adapt to complex real-time state changes during the production process. For example, when the current supply for the nickel plating process is unstable or the electroplating time is extended due to changes in the plating solution composition, the existing PLC system often cannot actively adjust the upstream process rhythm, resulting in too long residence times of the hanging tools at the previous process stations and affecting the overall production rhythm. In contrast, if an intelligent lag compensation mechanism is adopted, it can calculate the average delay trend based on historical data and intelligently compensate the rhythm when an abnormality occurs to ensure the stability of the production process.

[0003] However, current PLC control systems generally lack the ability to predict and adaptively adjust the production rhythm. Therefore, in the face of unexpected situations, manual intervention is still required, which affects the automation level. Secondly, there are significant deficiencies in the current PLC optimization methods in terms of power distribution, mainly manifested in the rigid power distribution strategy, which cannot be dynamically adjusted according to the actual current demand. Many production lines still adopt a fixed current distribution method, that is, the current supply for each electroplating tank is set as a fixed value according to experience, without considering the real-time change of current demand. For example, during high-load periods, the power requirements of nickel plating and chrome plating processes may far exceed the set value, while during low-load periods, the current demand of these processes may drop significantly. However, due to the lack of intelligent power scheduling ability in the system, the power distribution is unbalanced, affecting the coating quality and causing energy waste. In addition, the existing PLC control systems lack the ability to predict the power demand prospectively, that is, it is still impossible to accurately predict how the power demand of each electroplating tank will change in the short cycle in the future. This means that when the load suddenly increases, the system is difficult to respond quickly, which may lead to insufficient power supply for some electroplating tanks, affecting the coating uniformity; while when the load decreases, the system may still operate at a high power, resulting in unnecessary energy consumption losses.

[0004] If the PLC system can establish a power demand prediction model based on the current consumption pattern, and combine short-term power prediction and long-term trend analysis to calculate the future power demand in advance, it can more accurately schedule the current supply to ensure that the power consumption is always in the optimal state. However, most current electroplating production lines still lack this function, resulting in low power utilization and increased production costs. In addition, the current PLC optimization methods lack the ability of intelligent data analysis and are difficult to dynamically adjust key process parameters during the production process. Although the PLC system can collect data such as electroplating solution temperature, current density, voltage fluctuation, and plating solution concentration, these data are usually only used for monitoring and alarm, and are not fully utilized for in-depth analysis and optimization decision-making. For example, in the actual production process, different plating types, part shapes, and electroplating tank load conditions will all affect the current consumption pattern, but the existing PLC systems fail to establish an accurate current consumption model and are difficult to adjust the current supply strategy according to the real-time production status. Summary of the Invention

[0005] The purpose of the present invention is to provide a control method for an electroplating production line with optimized PLC programs, so as to solve some of the drawbacks and deficiencies pointed out in the background technology.

[0006] The technical solutions adopted by the present invention to solve its above technical problems include the following steps:

[0007] S1. Intelligent beat control optimized based on process relevance:

[0008] S1.1. Adopt a multi-dimensional data analysis model, comprehensively consider key parameters such as electroplating time, fixture transfer speed, and liquid tank occupancy, and construct a process time correlation matrix;

[0009] S1.2. Run adaptive beat adjustment in the PLC to dynamically adjust the transfer rhythm to balance the transfer between process nodes;

[0010] S2. Dynamic regulation of electroplating process parameters:

[0011] S2.1. Connect a multi-channel data acquisition system to the PLC side to monitor key variables such as electroplating solution temperature, current density, voltage fluctuation, plating solution concentration, and pH value;

[0012] S2.2. Adopt the Bayesian optimization algorithm to calculate electroplating process parameters based on historical data and real-time data, and dynamically adjust the output control value of the PLC;

[0013] S3. Intelligent power distribution control:

[0014] S3.1. Use the PLC to sample and analyze the current consumption mode of each electroplating tank to establish a power demand prediction model;

[0015] S3.2. Allocate current output through a dynamic power distribution algorithm to keep power consumption in an optimal state;

[0016] S3.3. Combine the peak load reduction strategy to optimize power supply during low load;

[0017] S4. Adaptive electroplating process transfer optimization:

[0018] S4.1. Use the PLC to collect data on the status of electroplating tanks, the load of the conveyor chain, and the current production beat in real time to establish a dynamic path decision-making model;

[0019] S4.2. Adopt the reinforcement learning optimization algorithm to calculate the optimal transfer path of each fixture and dynamically adjust the transfer direction.

[0020] Furthermore, the intelligent beat control method based on process correlation optimization includes:

[0021] By establishing a process time correlation matrix, the time matching degree of different process stations is calculated in real time, making the beat adjustment more globally optimized; based on multiple key factors, including electroplating time T e 、fixture transfer speed V g 、liquid tank occupancy S l 、current process state P c , the factors are coupled with each other, affecting the cooperation relationship between process nodes; and to describe the time matching degree between process stations, a process time correlation matrix M c,t, the calculation method is as follows:

[0022]

[0023] Among them:

[0024] M c,t represents the process time correlation matrix, which is used to evaluate the time matching degree between each process station; α i represents the dynamic weight of each process station; T e,i represents the standard electroplating time of station i; β i represents the influence factor of the liquid tank occupancy on the convection beat; S l represents the influence factor of the liquid tank occupancy on the convection beat; S l,i represents the current liquid tank occupancy of station i, that is, the current load state of the tank body; V g,i represents the hanging tool transfer speed of station i, which represents the transfer speed of parts at this process station.

[0025] Furthermore, the intelligent beat control method based on process correlation optimization includes:

[0026] Adopt an adaptive beat adjustment algorithm to adjust the placement time T of the hanging tool according to real-time production data d , so as to balance the load between each process station; in this algorithm, the PLC continuously monitors the process state change rate and the influence of the hanging tool transfer rate on the tank occupancy rate to calculate the optimal placement time of the hanging tool:

[0027]

[0028] Among them:

[0029] T d represents the calculated optimal placement time, which makes the time for the hanging tool to enter the process station accurate; T opt represents the optimal beat time calculated based on the process time correlation matrix; λ represents the process state fluctuation influence factor, which controls the influence degree of the process state on the placement time; represents the dynamic change rate of process parameters during the production process, which represents the change degree of the process state per unit time; μ represents the adjustment factor reflecting the influence of the hanging tool transfer rate on the tank occupancy rate; represents the relationship between the hanging tool transfer rate and the change of liquid tank occupancy, which represents how the change of the tank body load affects the transfer speed of parts.

[0030] Furthermore, the intelligent beat control method based on process correlation optimization includes:

[0031] Adopt an intelligent lag compensation mechanism. When a beat delay occurs at a certain process station, adjust the beat of the upstream process to keep the overall production rhythm stable. The PLC calculates the average delay trend of a certain process station through historical data and performs intelligent compensation on the beat when an anomaly occurs. The compensation calculation is as follows:

[0032] ΔT adj =κ·∫0 T (ω1P c +ω2S l +ω3T e )dt

[0033] Where:

[0034] ΔT adj represents the calculated intelligent compensation time, enabling the production system to adaptively adjust the beat; κ represents the adaptive compensation factor, controlling the variation range of the compensation time; ω1, ω2, ω3 represent the weight coefficients, respectively controlling the influence degrees of the process state, tank occupancy, and electroplating time on the beat adjustment; P c represents the process state variable during the production process, including current, voltage, solution concentration, etc.; S l represents the tank occupancy, indicating the load level of the current tank; T e represents the electroplating time, i.e., the standard process time of this station.

[0035] Furthermore, the intelligent power distribution control method includes:

[0036] Based on the PLC integrated current consumption pattern analysis system, perform real-time sampling on the current consumption of each electroplating tank and establish a current consumption pattern to obtain the dynamic current demand characteristics under different process conditions. The PLC uses a multi-channel current acquisition module to real-time collect the current input I(t) of each electroplating tank and perform long-period data storage to form the current consumption pattern M i (t), and the calculation formula is as follows:

[0037]

[0038] Where:

[0039] M i (t) represents the current consumption pattern of electroplating tank i at time t, reflecting the current consumption trend of this tank in different time periods; U(T) represents the real-time current input of this electroplating tank, indicating the current provided by the power supply at time t; α i represents the current consumption weight factor, indicating the influence degrees of different plating types and process types on the current demand; β i represents the adjustment factor of the current change rate; represents the instantaneous change rate of current, which is used to identify the dynamic changes in current demand within a short period; T represents the data sampling period, that is, the time window for the PLC to calculate the current consumption pattern.

[0040] Further, the intelligent power distribution control method includes:

[0041] After obtaining the current consumption pattern, calculate the power demand of each electroplating tank within the future unit time based on the power demand prediction model, and dynamically adjust the power distribution through the intelligent optimization algorithm. The power demand prediction model adopts short-term power prediction and long-term trend analysis. By integrating the current consumption pattern, the power demand prediction value P f within the future time period T d (T f ) is obtained, and the calculation formula is as follows:

[0042]

[0043] Where:

[0044] P d (T f ) represents the current demand prediction value within the future time period T f and is used to estimate the power demand change of the electroplating tank; γ represents the dynamic adjustment coefficient; n represents the total number of electroplating tanks on the production line; M i (t) represents the current consumption pattern of this electroplating tank at the current time t; T f represents the prediction time window, that is, the duration for the PLC to perform power prediction; δ i represents the trend influence factor, which is used to amplify or suppress the influence of the current change trend in the prediction calculation; represents the change rate of the current consumption pattern, indicating the increasing or decreasing trend of the current demand per unit time.

[0045] Further, the intelligent power distribution control method includes:

[0046] After predicting the power demand, optimize the power supply scheduling based on the dynamic power distribution algorithm to keep the current supply of different electroplating tanks in the optimal state, and dynamically adjust the power output according to the real-time current demand; the power distribution adopts a priority scheduling mechanism, and based on the power priority W i of each electroplating tank, perform dynamic distribution, and the calculation method is as follows:

[0047]

[0048] Where:

[0049] I out represents the calculated optimal current output value; W iIndicates the power priority of the i-th electroplating tank, which is dynamically adjusted according to the current demand, plating process requirements, and power supply load; P d (T f ) represents the predicted power demand value; Represents the total power demand weight of all electroplating tanks, indicating the total sum of the power priorities of the entire production line.

[0050] The electroplating production line control method with optimized PLC program of the present invention realizes precise and energy-saving control of the electroplating production process through intelligent data analysis, adaptive regulation, and dynamic optimization. Its beneficial effects are mainly reflected in the following aspects:

[0051] By establishing a process time correlation matrix, calculating the time matching degree of each process station in real time, optimizing the production rhythm, and reducing the waiting time and production bottlenecks caused by rhythm mismatch. The adaptive rhythm adjustment algorithm accurately calculates the hanging tool placement time according to the dynamic changes of the production state, makes the production flow more balanced, avoids overloading or excessive waiting time at some process stations, and thus improves the overall production efficiency of the whole line. The intelligent power distribution control method is adopted, based on the analysis of the current consumption mode of the PLC and the power demand prediction model, the current demand of each electroplating tank is sampled and trend analyzed in real time, and the current supply is adjusted through the dynamic power distribution algorithm to ensure that the power consumption is maintained at the optimal state.

[0052] Through precise current scheduling and rhythm optimization, each process station can be maintained in the best working state, avoiding coating quality problems caused by current fluctuations, unstable rhythm, or uneven power supply. The reinforcement learning and dynamic optimization algorithms are adopted to enable the system to adapt to the production requirements of different batches and different plating types and automatically adjust the production parameters. For example, when it is detected that a certain process station has a delay, the PLC automatically calculates the intelligent compensation time and adjusts the upstream process rhythm to keep the overall production rhythm stable. This method enables the production line to flexibly respond to different process requirements and reduces the decline in production efficiency caused by unexpected situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Is the flowchart of the control method of the electroplating production line with optimized PLC program of the present invention.

[0054] Figure 2 Is the flowchart of the intelligent rhythm control method optimized based on process relevance of the present invention.

[0055] Figure 3 Is the flowchart of the intelligent power distribution control method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0056] The following will give a detailed description of the specific implementation manners of the present invention in conjunction with the drawings.

[0057] The control method for an electroplating production line with PLC program optimization constructs a multi-dimensional data analysis model to perform real-time calculation and optimization on the key parameters affecting electroplating production efficiency and quality, so as to ensure the balanced flow of the entire production line and improve production stability. First, this method comprehensively considers multiple key parameters, including variables such as electroplating time, fixture transfer speed, and liquid tank occupancy, and conducts multi-dimensional data analysis on these variables to establish a process time correlation matrix, which is used to calculate the time matching relationship between each process station, thereby identifying the bottleneck links affecting the production rhythm and optimizing the production beat. Electroplating time is one of the core factors affecting the overall production efficiency. Different process formulas, part shapes, and current densities will result in different electroplating times. Therefore, the PLC collects and analyzes real-time electroplating time data, establishes a processing time curve of the electroplating tank for parts, and optimizes electroplating parameters in combination with historical data to ensure that the processing time of each part in different electroplating tanks matches the beat requirements of the entire production process. The fixture transfer speed directly affects the movement rhythm of parts on the production line. To ensure smooth transfer between different process stations, the PLC monitors the real-time speed of the conveyor chain, analyzes the movement of each fixture in different time periods, and adjusts the fixture placement rhythm based on the production load to make the production beats of different process sections consistent.

[0058] The occupancy of the liquid tank determines the overall load status of the production line. If the liquid tank in a certain process section is occupied for a long time while the subsequent process section is idle, it may lead to unbalanced rhythm and affect production efficiency. Therefore, the PLC combines sensor data to calculate the load rate of each liquid tank and optimizes the transfer through intelligent algorithms to evenly distribute the production load and avoid the bottleneck process from affecting the overall production rhythm. On this basis, the method runs an adaptive rhythm adjustment algorithm in the PLC to dynamically adjust the conveying rhythm and achieve global production optimization. The core of this adaptive rhythm adjustment lies in the dynamic feedback of the real-time production status, enabling the production rhythm to be adjusted according to process requirements rather than adopting a fixed rhythm control mode. Specifically, when the production time of a certain process station is extended due to changes in electroplating solution concentration, current fluctuations, or process adjustments, the system automatically adjusts the rhythm of the subsequent process stations to match the previous process, avoiding part waiting or congestion caused by rhythm mismatch. On the other hand, if the previous process is completed ahead of schedule, the system can appropriately increase the transfer rhythm of the subsequent stations to improve overall production efficiency. This method makes the transfer between process stations more balanced by dynamically adjusting the transfer rhythm, thereby avoiding local overload or idleness of the production line, improving equipment utilization rate and ensuring stable coating quality. At the same time, due to the adaptive adjustment ability of this method, even if sudden process changes occur during production, such as temporarily adjusting the plating solution composition, changing the electroplating process, or a brief shutdown of a certain electroplating tank due to maintenance, the system can still automatically adjust the rhythm according to the new production parameters, keeping the entire production line running stably and maintaining the best production capacity configuration.

[0059] During the production process, various key process parameters are collected in real time through high-precision sensors, and the data is input into the PLC for analysis and processing to achieve intelligent control of the electroplating process. The temperature of the electroplating solution is an important factor affecting the uniformity and adhesion of the coating. Temperature fluctuations may cause changes in the deposition rate, an increase in internal stress in the coating, or an imbalance in the composition of the plating solution. Therefore, the PLC monitors the temperature changes in the electroplating tank in real time through a temperature sensor and automatically adjusts it in combination with heating or cooling devices to ensure that the temperature of the plating solution is maintained within the optimal range. The current density directly determines the growth rate and thickness distribution of the coating. Different plating types and part shapes have different requirements for the current density. Excessive current density may cause the coating to be rough or burned, while too low current density may result in a too thin coating or insufficient adhesion. Therefore, the PLC monitors the current density during the electroplating process in real time through a current sensor and a Hall sensor, and adjusts the power output in combination with the process requirements to ensure the stability and controllability of the electroplating process. Voltage fluctuations may be caused by changes in the grid load, fluctuations in the performance of power supply equipment, or poor conductivity. These factors may lead to instability of the electroplating current, which in turn affects the coating quality. Therefore, the PLC monitors the voltage fluctuations through a voltage acquisition module and triggers an automatic compensation mechanism when abnormalities occur, such as adjusting the output of the rectifier or optimizing the load distribution, to maintain a stable electroplating power supply. The concentration of the plating solution is one of the key factors determining the coating composition and growth rate. The concentration of metal ions, complexing agents, and additives in the plating solution all affect the coating quality. Therefore, the PLC monitors the concentration of the plating solution in real time through an on-line concentration detection device and precisely controls the composition of the plating solution in combination with an automatic replenishment system to ensure that the composition of the plating solution is always within the optimal range.

[0060] The pH value affects the coating uniformity and deposition mechanism. Different electroplating processes have different requirements for the pH value. Too high or too low pH value will affect the coating structure and deposition efficiency. Therefore, the PLC monitors the pH value of the plating solution in real time through a pH sensor and adjusts it in combination with an automatic acid-base compensation device to ensure that the pH value is maintained at the optimal level. To further optimize the electroplating process parameters, this method uses the Bayesian optimization algorithm to calculate the optimal electroplating process parameters based on historical data and real-time data, and dynamically adjusts the output control value of the PLC. The Bayesian optimization algorithm rapidly approaches the optimal process parameters without affecting production efficiency by continuously updating the prior probability distribution and posterior probability distribution. Its core idea is to construct a probability model based on historical process data and update it in combination with real-time data to predict the optimal parameter setting under the current production conditions. First, the PLC collects a large amount of historical production data, including data such as temperature, current density, voltage, plating solution concentration, pH value, etc. under different process conditions, and calculates the relationship between these parameters and the coating quality. On this basis, a Bayesian probability model is established to describe the influence of different process parameters on the coating quality.

[0061] Then, during the actual production process, the PLC updates the model by collecting real-time data and combining it with historical data, and uses methods such as Gaussian process regression to calculate the current optimal electroplating parameters. For example, it determines the current density, temperature setting, or pH adjustment range that is most suitable for the current environment, and uses the optimized parameters as the control output value of the PLC to achieve precise control of the electroplating process. In addition, this method can also make rapid adjustments in case of abnormal situations. For example, when a certain variable is detected to deviate from the normal range, the system will automatically calculate a new optimal set value and immediately adjust the output of the rectifier, current controller, or chemical compensation system to ensure the stability of the production process.

[0062] Real-time monitor the current input of each electroplating tank, and form the current consumption law based on long-term data accumulation to achieve intelligent power management. In traditional electroplating production, the power supply usually adopts a fixed power mode, resulting in some electroplating tanks may obtain excessive power allocation, while other electroplating tanks may affect the coating quality due to insufficient power. Therefore, this method first integrates current sensors through the PLC to perform high-frequency sampling on the current consumption mode of each electroplating tank. The collected data includes the instantaneous current value, current fluctuation range, current demand corresponding to the electroplating process stage, etc. All data are stored in the data storage module of the PLC to form a historical database for trend analysis. This data not only includes the impact of different plating types, plating solution formulas, and part types on current consumption, but also can extract typical load curves during the electroplating process through time series analysis. For example, some electroplating processes may require a large current at the initial stage, and the current demand will decrease after the coating tends to be stable. These characteristics are incorporated into the current consumption mode for subsequent power optimization. Based on this data, the PLC establishes a power demand prediction model. The core goal of this model is to calculate the power demand of each electroplating tank in the future period in advance, so that the current supply no longer depends on fixed settings, but is dynamically adjusted based on real-time demand. This model combines historical data and real-time monitoring data, and through data fitting and trend analysis, calculates the optimal current demand value of each electroplating tank and predicts the load changes in the future period, making the power distribution more forward-looking. For example, in short-term prediction, the PLC can judge whether the electroplating tank has entered the stable stage based on the current fluctuation situation to reduce the current supply and avoid energy waste, while in long-term prediction, the system will analyze the power demand patterns during the peak and trough periods of daily production to optimize the power supply scheduling in advance and improve the overall energy utilization rate. After obtaining the power demand prediction value, the system distributes the current output through a dynamic power distribution algorithm to keep the power consumption in the optimal state. This algorithm calculates the power priority of each electroplating tank based on the load demands of multiple electroplating tanks and dynamically adjusts the current output to make the power supply of each electroplating tank precisely match its actual demand, thereby avoiding unnecessary energy consumption.

[0063] Specifically, the PLC first calculates the current power demand value of each electroplating tank, combines the results of the prediction model to calculate the future load change trend, and then ranks the power priorities of all electroplating tanks, giving priority to meeting the current supply of electroplating types with high demand. For electroplating tanks with low load or tending to be stable, the power allocation is appropriately reduced, so as to optimize the overall power supply scheduling under the condition of constant total power. When the power demand of a certain electroplating tank decreases, the system can quickly allocate the remaining power to other electroplating tanks with higher power demand, ensuring that the current distribution of the entire production line is in the best balance state. In addition, this method combines the peak load reduction strategy to optimize the power supply during low load to further reduce energy consumption and improve the energy utilization efficiency of the system. Specifically, the core of the peak load reduction strategy is to identify the low load periods of electroplating production and appropriately adjust the power output, thereby reducing unnecessary energy losses. For example, during non-production peak periods or low electricity price periods at night, the system can appropriately reduce the overall power supply, avoiding maintaining too high a current output under low load conditions, thereby reducing operating costs. At the same time, when a specific electroplating tank operates at low load, the system can adopt an interleaved power supply mode, that is, reduce the number of simultaneously operating electroplating tanks, and concentrate the current on a small number of electroplating tanks to improve the energy efficiency ratio per unit time, thus optimizing the overall power supply. In addition, in some specific load fluctuation situations, the PLC can also, through the power buffer mechanism, adjust the power supply strategy in advance when detecting that the power demand is about to decrease, so as to reduce the impact of sudden load changes on the power grid and improve the power grid stability.

[0064] The PLC collects the electroplating tank status in real time, including data such as the usage of each electroplating tank, electroplating time, changes in plating solution composition, current density, temperature, etc., to determine whether a certain electroplating tank is currently in an available state or whether the electroplating process of the current workpiece is about to be completed, ensuring that the fixture does not enter an electroplating tank with an overly long waiting time and affecting the overall production efficiency. At the same time, the PLC monitors the load situation of the conveyor chain, including the distribution of fixtures, the load status of current transmission lines, and the transfer speed of fixtures on different conveying sections, to ensure the load balance of the conveyor chain, avoid overloading of some conveying paths while other paths are in a low utilization state. In addition, the system simultaneously collects current production rhythm data, including parameters such as the average processing time of each process section, the current number of work-in-progress, equipment utilization rate, etc., to analyze the rhythm of the entire production line and ensure that the transfer path of the fixture does not affect the consistency of the production rhythm. Based on the above real-time collected data, the system establishes a dynamic path decision-making model. The core of this model is to continuously adjust the optimal path according to the changes in the production state, rather than relying on fixed preset paths, thereby improving production flexibility and overall efficiency. Specifically, this model comprehensively considers the electroplating tank status, conveyor chain load, and production rhythm data, calculates the possible path options for each fixture in the current situation, and evaluates them based on factors such as waiting time, conveying distance, and electroplating tank occupancy of different paths, so as to select the optimal path to ensure the maximization of production efficiency.

[0065] To further improve the adaptability of path decision-making, this method adopts a reinforcement learning optimization algorithm. By continuously training the intelligent control strategy, the system can automatically learn the best transfer method in a complex production environment. The core idea of the reinforcement learning optimization algorithm is based on the state-action-reward mechanism. By continuously trying different path selections and rewarding or punishing according to their impact on production efficiency, the path decision-making ability of the system is continuously optimized. In practical applications, the PLC takes the current production state as the input of reinforcement learning, including electroplating tank occupancy, conveyor chain load, production rhythm, etc., and conducts simulation calculations for different transfer path options to evaluate the impact of each path plan on the overall production rhythm. In the initial stage of the system, random path selection may be adopted, but as the number of training times increases, the reinforcement learning algorithm will continuously adjust the strategy to make the fixture tend to choose a better path. For example, when the queuing time of a certain electroplating tank is relatively long, the system will tend to choose an electroplating tank with a lower load for production to reduce the waiting time and improve the overall production capacity.

[0066] Example 1:

[0067] A large electroplating processing factory, in order to improve production efficiency, reduce unnecessary waiting time, and optimize the overall production rhythm, the enterprise decides to adopt a control method for the electroplating production line optimized by the PLC program. Among them, the intelligent rhythm control method based on process relevance optimization becomes the key. The core of this method lies in establishing a process time relevance matrix, and by calculating the time matching degree of different process stations in real time, the rhythm adjustment has the ability of global optimization.

[0068] The enterprise has arranged sensors at different process stations to monitor the electroplating time T in real time e , the transfer speed V of the hanging tool g , the occupancy situation S of the liquid tank l and the current process state P c , and transmit the data to the PLC system for intelligent decision-making.

[0069] This electroplating factory has 10 electroplating production lines, and each production line contains multiple process stations, such as pretreatment (cleaning, activation), main electroplating, post-treatment (sealing, passivation, etc.). The typical production path of a certain product involves 5 main process stations:

[0070] Station 1: Cleaning (standard time T e,1 = 120s)

[0071] Station 2: Activation (standard time T e,2 = 90s)

[0072] Station 3: Nickel plating (standard time T e,3 = 300s)

[0073] Station 4: Chromium plating (standard time T e,4 = 200s)

[0074] Station 5: Sealing (standard time T e,5 = 180s)

[0075] Due to the fact that some processes (such as nickel plating) require a long time, and the transfer speed of the hanging tool is different between stations, and the occupancy of some liquid tanks may be relatively high, it may lead to problems such as waiting, bottlenecks, or overloads in the production line. Therefore, the PLC system calculates the process time relevance matrix M c,t to dynamically adjust the production rhythm and make the overall transfer rhythm more balanced.

[0076] Matrix calculation: The PLC sets the weight factor according to historical data and real-time monitoring data:

[0077] α i (the dynamic weight of each process station) is set in the range of [0.1, 1], and the larger the value, the greater the impact of the rhythm of this station on the overall production rhythm. βi (Influence factor of the liquid tank occupancy on the convection beat) The range is set at [0.05, 0.5]. The larger the value, the more serious the impact on the beat when the liquid tank at this station is full. l (Liquid tank occupancy), the value range is [0, 1], 1 when full, 0 when empty. l,i (Hanger transfer speed), the unit is mm / s, and the range is set at [10, 100] mm / s. g,i (Hanger transfer speed), the unit is mm / s, and the range is set at [10, 100] mm / s.

[0078] At a certain moment, the PLC monitors the specific parameters of each process station as follows:

[0079] site <![CDATA[T e,i (s)]]> <![CDATA[S l,i load]]> <![CDATA[V g,i mm / s]]> <![CDATA[α i > <![CDATA[β i > 1 120 0.8 80 0.8 0.2 2 90 0.5 60 0.7 0.15 3 300 0.9 40 1.0 0.3 4 200 0.6 50 0.9 0.2 5 180 0.4 70 0.6 0.1

[0080] Substitute into the calculation formula of the process time correlation matrix:

[0081]

[0082] Calculate the time matching degree of each station:

[0083] 1. Station 1:

[0084] 2. Station 2:

[0085] 3. Station 3:

[0086] 4. Station 4:

[0087] 5. Station 5:

[0088] Final calculation:

[0089] M c,t = 96.0016 + 63.0009 + 300.00675 + 180.00216 + 108.00057 = 747.012

[0090] Adjustment of intelligent beat optimization:

[0091] 1. Automatic beat adjustment: Since the load of Station 3 (nickel plating) is the highest and its beat affects the overall production, the PLC automatically reduces the early arrival rate of other stations to reduce the waiting time in front of this station. The transfer times of Station 1 and Station 2 are appropriately extended to avoid excessive hangers piling up in front of the nickel plating station. The processing time of Station 5 is slightly compressed to reduce the overall beat delay.

[0092] 2. Adjust the conveyor chain speed: Since the V of Station 3 g = 40 mm / s is slow, while the V of Station 5g = 70 mm / s, and the PLC adjusts the conveying speed of Station 3 to 50 mm / s, accelerating the rhythm of parts entering the next process.

[0093] 3. Optimization of the liquid tank occupancy: S at Station 3 l = 0.9 is too high. The PLC adjusts the priority so that some parts are assigned to other production lines with lower loads for nickel plating to reduce the waiting time at this station.

[0094] After optimizing the beat control of the electroplating production line in this embodiment, the production efficiency of the factory has increased by 13.2%, and the load of Station 3 has decreased by 17.5%. However, the management team still found that due to the large variation in the production batches of different parts, there are still fluctuations in the transfer time of the fixtures at each process station, and there are occasional short-term accumulations or waiting phenomena at some stations, affecting the production stability.

[0095] To further optimize the dynamic beat control of the electroplating production line, the factory decides to introduce an adaptive beat adjustment algorithm, using the PLC system to monitor the process state change rate and the influence of the fixture transfer rate on the tank occupancy rate to calculate the optimal placement time T of the fixture d , ensuring the load balance between each process station, making the transfer of parts on the production line more accurate, and avoiding unnecessary waiting and backlogs.

[0096] After further optimization, the factory makes beat adjustments for a new order, which includes two types of parts:

[0097] Type A parts (larger size, thicker coating, longer electroplating time), Type B parts (smaller size, thinner coating, shorter electroplating time). During the production process, Type A parts need to stay at the nickel plating station for T e,3 = 320 s, and the nickel plating time of Type B parts is only T e,3 = 200 s, which makes the load of the nickel plating station fluctuate greatly, and the transfer speeds of different batches of fixtures are different, affecting the overall production rhythm. To accurately adjust the placement time of each fixture, the system adopts an adaptive beat adjustment algorithm to calculate the optimal placement time T d :

[0098]

[0099] Parameter setting:

[0100] In the PLC system, the management team sets the following parameter ranges:

[0101] T opt (Optimal beat time calculated based on the process time correlation matrix):

[0102] The calculated ideal feeding time. In this case, for type A parts, T opt = 320 s, and for type B parts, T opt = 200 s.

[0103] λ (process state fluctuation influence factor):

[0104] The set range is [0.1, 1]. When the value is larger, it indicates that the change in the process state has a greater impact on the beat adjustment. In this example, λ is set to 0.6.

[0105] (Process state dynamic change rate):

[0106] By real-time monitoring of parameters such as current density, voltage, pH value, etc., calculate the change rate of the process state per unit time, and the range is set at [-0.02, 0.02] (a negative value indicates a load decrease, and a positive value indicates a load increase).

[0107] At high load,

[0108] At low load,

[0109] μ (adjustment factor for the influence of the fixture transfer rate on the tank occupancy rate):

[0110] The set range is [0.05, 0.5]. In this example, μ is set to 0.4.

[0111] (Relationship between the fixture transfer rate and the change in the tank occupancy):

[0112] Calculate the influence of the fixture speed on the tank occupancy per unit time, and the range is set at [-2, 2] mm / s,

[0113] For type A parts (High occupancy rate, lower fixture speed).

[0114] For type B parts (Low occupancy rate, higher fixture speed).

[0115] Calculate the optimal feeding time:

[0116] 1. For type A parts (higher load, nickel plating station full load)

[0117] T d = 320 + 0.6·0.015 + 0.4·(-1.2)

[0118] T d = 320 + 0.009 - 0.48 = 319.529 s

[0119] The system calculates that the optimal feeding time for Class A parts is T d = 319.53 s. Since the nickel plating station has a high load, the system automatically fine-tunes the feeding time and delays it slightly by 0.47 s to avoid excessive jigs waiting in front of the nickel plating station and improve production stability.

[0120] 2. Class B parts (low load, the nickel plating station can process quickly)

[0121] T d = 200 + 0.6·(-0.01) + 0.4·0.8

[0122] T d = 2000.006 + 0.32 = 200.314 s

[0123] The optimal feeding time for Class B parts is adjusted to T d = 200.31 s. Since the current load of the nickel plating station is low, the system automatically speeds up the jig feeding speed to make the parts enter the station as soon as possible and improve equipment utilization.

[0124] In this embodiment, in order to further optimize the stability of the production line, the factory decides to introduce an intelligent lag compensation mechanism into the PLC system, enabling the system to automatically detect the beat delay of the process station and adjust the beat of the upstream process to maintain the smooth operation of the overall production rhythm. On the optimized electroplating production line, a certain production batch of Class A parts enters the production process, and its main production processes include:

[0125] Station 1: Cleaning (T e,1 = 120 s)

[0126] Station 2: Activation (T e,2 = 90 s)

[0127] Station 3: Nickel plating (T e,3 = 320 s)

[0128] Station 4: Chromium plating (T e,4 = 200 s)

[0129] Station 5: Sealing (T e,5 = 180 s)

[0130] During the production process, the PLC system monitors that an abnormal delay occurs at Station 3 (nickel plating). The main reason is that the concentration of the electroplating solution decreases, resulting in a decrease in the current density and thus an extension of the electroplating time. The real-time data collected by the PLC is as follows:

[0131] Current current P c = 0.9 (standard value 1.0)

[0132] Tank load S l = 0.95 (full load)

[0133] Actual electroplating time T e = 340 s (20 s later than the standard time)

[0134] Since the nickel plating process is one of the most time-consuming processes on the production line, without adjustment, large-scale waiting will occur at subsequent process stations. To solve this problem, the PLC system uses an intelligent lag compensation mechanism to calculate the beat compensation time ΔT adj :

[0135] ΔT adj = κ·∫0 t (ω1Pc + ω2S l + ω3T e )dt

[0136] In the PLC system, the factory sets the following parameter ranges:

[0137] κ (adaptive compensation factor): Controls the variation range of the compensation time, and the range is set at [0.5, 1.5]. In this case, κ = 1.2 is taken to adapt to a large beat delay.

[0138] ω1, ω2, ω3 (weight coefficients):

[0139] ω1 (process state influence factor) range is set at [0.2, 0.8]. Currently, ω1 = 0.5 is set to moderately adjust the delay caused by current fluctuations. ω2 (tank occupancy influence factor) range is set at [0.3, 0.9]. Currently, ω2 = 0.7 because the nickel plating station is already close to full load. ω3 (electroplating time influence factor) range is set at [0.4, 1.0]. Currently, ω3 = 0.9 because the delay in nickel plating time most directly affects the beat.

[0140] Substitute into the formula to calculate the intelligent compensation time:

[0141] ΔT adj = 1.2·∫0 t (0.5×0.9 + 0.7×0.95 + 0.9×340)dt

[0142] Calculate the integral part:

[0143] (0.5×0.9)+(0.7×0.95)+(0.9×340)= 0.45 + 0.665 + 306 = 307.115

[0144] ΔT adj = 1.2×307.115 = 368.538 s

[0145] Adjustment of Intelligent Beat Compensation:

[0146] 1. Adjust the previous process beat: Since Station 3 (nickel plating) was delayed by 20 s, the ΔT calculated by the system adj = 368.538 s indicates that it is necessary to appropriately adjust the production beats of the previous processes (activation and cleaning) to avoid excessive accumulation of parts in front of the nickel plating station. The beat of Station 1 (cleaning) was adjusted from 120 s to 130 s to allow parts to enter the activation station slowly and avoid congestion at the nickel plating station. The beat of Station 2 (activation) was adjusted from 90 s to 100 s to further balance the production flow.

[0147] 2. Optimize the load of the conveyor chain: Since the load of Station 3 was too high, the system adjusted the conveyor chain speed to reduce the speed of parts flowing into Station 3 by 7.2%, thereby reducing the hanging tool waiting in front of the station.

[0148] 3. Balance the subsequent process flow: The time of Station 4 (chromium plating) was moderately shortened from 200 s to 190 s to make up for the delay of the nickel plating station and restore the normal beat of the entire production process as soon as possible.

[0149] Optimization results: Since the intelligent beat compensation adjusted the time of the previous processes, the waiting time of Station 3 (nickel plating) was reduced by 11.5%, avoiding the backlog of hanging tools in front of this station. By balancing the load of the conveyor chain, the number of hanging tools in front of Station 3 was reduced by 15.7%, improving the flow efficiency of parts throughout the production line. After adjustment, the beat difference between Station 4 and Station 5 was reduced, and the entire production process returned to a stable rhythm within 20 minutes, reducing production fluctuations caused by local beat imbalance.

[0150] This case fully demonstrates the practical application of the intelligent lag compensation mechanism in electroplating production. The PLC system is used to calculate the comprehensive influence of process state changes, tank occupancy, and electroplating time, and the previous process beats are adjusted through intelligent algorithms to keep the production rhythm stable even when abnormal delays occur. Compared with the traditional manual adjustment method, this method can automatically detect beat delays and dynamically adjust production parameters, making the production rhythm more adaptable, thereby reducing manual intervention, improving the automation level of the production line, and ultimately enhancing production efficiency and overall system stability.

[0151] Example 2:

[0152] During the electroplating process, the current demand at each process station varies with different process conditions. The traditional fixed-power supply mode is prone to power waste or local power shortage, thus affecting the coating quality and production stability. To further optimize energy utilization and improve the stability of the overall electroplating process, the factory decides to introduce an intelligent power distribution control method. By integrating a current consumption pattern analysis system into the PLC system, it samples the current consumption of each electroplating tank in real time and establishes a current consumption pattern to obtain the dynamic current demand characteristics under different process conditions.

[0153] The factory has five main electroplating production lines. Each production line includes multiple electroplating tanks, and the current demand of each electroplating tank varies dynamically according to factors such as coating type, part size, and plating solution concentration. For a specific production order, the electroplating production process involves the following key stations:

[0154] Station 1: Copper plating (T e,1 = 300 s)

[0155] Station 2: Nickel plating (T e,2 = 320 s)

[0156] Station 3: Chromium plating (T e,3 = 280 s)

[0157] Station 4: Sealing treatment (T e,4 = 200 s)

[0158] In the previous production process, the current supply for each electroplating tank was set based on empirical values. For example, the standard current for the copper plating tank was set at 200 A, the nickel plating tank at 250 A, and the chromium plating tank at 180 A. However, in the actual production process, due to factors such as part batches, fixture density, and changes in plating solution concentration, the current demand fluctuates greatly. To optimize the current distribution, the PLC system needs to model the current consumption pattern to achieve optimal power management under different production conditions. The PLC uses a multi-channel current acquisition module to collect the current input I(t) of each electroplating tank in real time and stores the long-term data to form the current consumption pattern M i (t), and the calculation formula is as follows:

[0159]

[0160] Where:

[0161] M i (t) represents the current consumption pattern of electroplating tank i at time t, reflecting the current consumption trend of this tank at different time periods. I(t) represents the real-time current input of this electroplating tank, with the unit of ampere (A). α iIt represents the current consumption weight factor, indicating the influence degree of different plating types and process types on the current demand. The value range is set at [0.5, 1.5]. The larger the value, the more sensitive the process is to the current demand. β i It represents the adjustment factor of the current change rate, controlling the influence degree of the instantaneous current fluctuation. The value range is set at [0.1, 0.8]. It represents the instantaneous change rate of the current, with the unit of A / s. T represents the data sampling period, usually set at 5 - 10 minutes to obtain the long-term trend.

[0162] The data collected by the PLC is as follows:

[0163]

[0164] Calculate the current consumption mode:

[0165] 1. Site 1 (copper plating):

[0166] M1(t) = ∫0 T [1.2×210 + 0.5×1.2]dt

[0167] M1(t) = ∫0 T [252 + 0.6]dt = ∫0 T 252.6dt = 252.6T

[0168] 2. Site 2 (nickel plating):

[0169] M2(t) = ∫0 T [1.3×260 + 0.6×1.5]dt

[0170] M2(t) = ∫0 T [338 + 0.9]dt = ∫0 T 338.9dt = 338.9T

[0171] 3. Site 3 (chromium plating):

[0172] M3(t) = ∫0 T [1.0×175 + 0.4×(-0.8)]dt

[0173] M3(t) = ∫0 T [175 - 0.32]dt = ∫0 T 174.68dt = 174.68T

[0174] 4. Site 4 (sealing):

[0175] M4(t) = ∫0 T [0.8×110 + 0.3×0.5]dt

[0176] M4(t) = ∫₀ T [88 + 0.15]dt = ∫₀ T 88.15dt = 88.15T

[0177] Based on the calculation results, the PLC identifies that the nickel plating station (Station 2) has the highest current consumption pattern (338.9T), followed by copper plating (252.6T), while the current demands of the chrome plating and sealing stations are relatively low. Therefore, the system dynamically adjusts the power distribution:

[0178] The current supply to the nickel plating station is increased by 5% (adjusted from 250A to 263A) to ensure uniform electroplating and improve production quality. The current supply to the copper plating station remains unchanged (210A). The current of the chrome plating station is moderately reduced by 2% (adjusted from 180A to 176A) due to the decreasing trend of its current consumption, reducing unnecessary energy consumption. The current of the sealing station is increased by 10A (raised from 100A to 110A) to speed up the sealing process and improve efficiency.

[0179] During the production process of this embodiment, due to the dynamic changes in the load conditions and current demands of different plating baths, there may still be problems of uneven power distribution, especially during peak production hours. The power demands of some plating baths surge, while those of others may decrease, resulting in local power waste or insufficient power supply. To solve this problem, the factory decides to adopt a power demand prediction model based on the established current consumption patterns, calculate the power demands of each electroplating bath in the future unit time in advance, and dynamically adjust the power distribution through an intelligent optimization algorithm to make the overall power distribution more accurate.

[0180] The factory is currently producing a batch of Class A parts, involving 4 main electroplating stations:

[0181] Station 1: Copper plating (T e,1 = 300s)

[0182] Station 2: Nickel plating (T e,2 = 320s)

[0183] Station 3: Chrome plating (T e,3 = 280s)

[0184] Station 4: Sealing treatment (T e,4 = 200s)

[0185] During the previous production process, the management team found that during peak production, due to a large number of fixtures entering the nickel plating and chrome plating stations simultaneously, the current demand would rise sharply within a short period, resulting in the power demands of these stations exceeding the planned supply. And during some low-load periods, the power utilization rate of some electroplating baths was significantly lower than the set value. Therefore, the system needs to predict the power demand in the future period based on real-time data and optimize the power distribution in advance.

[0186] In the PLC system, the power demand prediction model adopts short-term power prediction and long-term trend analysis. By integrating the current consumption pattern, the predicted value P of the power demand within the future time period T of the electroplating tank is obtained. f of the electroplating tank within the future time period T d (T f ):

[0187]

[0188] In the PLC system, the following parameters are set:

[0189] γ (dynamic adjustment coefficient): Controls the influence of the prediction result on the final power distribution. The range is set to [0.8, 1.5]. In this example, γ = 1.2. n (total number of electroplating tanks on the production line): In this example, n = 4 (copper plating, nickel plating, chromium plating, sealing). T f (prediction time window): Set to 10 minutes (600 s). δ i (trend influence factor): Controls the influence of the change in current consumption. The range is set to [0.3, 1.0]. In this example, it is set as follows:

[0190] Copper plating station: δ1 = 0.5

[0191] Nickel plating station: δ2 = 0.8

[0192] Chromium plating station: δ3 = 0.7

[0193] Sealing station: δ4 = 0.4

[0194] (change rate of current consumption pattern): Represents the increasing or decreasing trend of current demand per unit time, with the unit of A / s. The actual collected data is as follows:

[0195]

[0196] Calculate the predicted value of power demand according to the formula:

[0197] 1. Copper plating station (station 1):

[0198]

[0199] P d (T f )(1) = 1.2 · (300 + 1.6) = 1.2 · 301.6 = 361.92 A

[0200] 2. Nickel plating station (station 2):

[0201]

[0202] P d (T f )2 = 1.2·(366.67 + 4.48) = 1.2·371.15 = 445.38A

[0203] 3. Chrome plating station (Station 3)

[0204]

[0205] P d (T f )3 = 1.2·(283.33 + 2.87) = 1.2·286.2 = 343.44A

[0206] 4. Sealing station (Station 4)

[0207]

[0208] P d (T f )4 = 1.2·(200 + 1) = 1.2·201 = 241.2A

[0209] According to the calculation results, the system predicts that the power demand of the nickel plating station will be the highest (445.38A) in the next 10 minutes, followed by copper plating (361.92A), chrome plating (343.44A), and the power demand of the sealing station will be the lowest (241.2A). Based on this result, the PLC system dynamically adjusts the power distribution:

[0210] Although the overall power distribution has been optimized by the above method in this embodiment, there will still be local power waste or local power shortage in some periods. For example, during the production peak period, due to the sudden increase in the current demand of the nickel plating station, other electroplating tanks may not be able to obtain sufficient current supply due to their lower priority, thus affecting the production rhythm.

[0211] On the contrary, during the production low period, some electroplating tanks may obtain too much current, while the load of other stations is low, resulting in a reduction in power utilization efficiency. In order to further improve the accuracy of power management, the factory decides to optimize the power supply scheduling based on the dynamic power distribution algorithm, so that the current supply of different electroplating tanks is always maintained in the optimal state and dynamically adjusted according to the real-time current demand. This method calculates the power priority W of each electroplating tank through the priority scheduling mechanism i , and distributes the current supply according to its importance and demand changes.

[0212] Currently, the factory is running a new production batch, which involves four main electroplating stations:

[0213] Station 1: Copper plating (T e,1 = 300s)

[0214] Station 2: Nickel plating (T e,2 = 320 s)

[0215] Station 3: Chrome plating (T e,3 = 280 s)

[0216] Station 4: Sealing treatment (T e,4 = 200 s)

[0217] After the previous round of power demand prediction, the predicted power demand value for the next 10 minutes calculated by the PLC system is:

[0218] P d (T f ) = [361.92 A, 445.38 A, 343.44 A, 241.2 A]

[0219] Among them:

[0220] The nickel plating station has the highest power demand (445.38 A) and needs to obtain a higher-priority current supply;

[0221] The copper plating station has the second-highest power demand (361.92 A);

[0222] The chrome plating station has a stable power demand (343.44 A);

[0223] The sealing station has the lowest power demand (241.2 A).

[0224] To optimize power distribution, the PLC system adopts a dynamic power distribution algorithm to calculate the optimal current output value:

[0225]

[0226] In the PLC system, the following parameters are set:

[0227] W i (Power priority): Represents the process importance of the electroplating bath, the current load condition, and the trend of current demand change. The range is set at [0.5, 1.5]. The larger the value, the higher the priority. In this example, it is set as follows:

[0228] Copper plating station (Station 1): W1 = 1.2

[0229] Nickel plating station (Station 2): W2 = 1.5

[0230] Chrome plating station (Station 3): W3 = 1.1

[0231] Sealing station (Station 4): W4 = 0.9

[0232] (Total power priority):

[0233] W total = 1.2 + 1.5 + 1.1 + 0.9 = 4.7

[0234] Calculate the optimal current output and calculate the optimal current distribution for each electroplating tank according to the formula:

[0235] 1. Copper plating station (Station 1):

[0236]

[0237] 2. Nickel plating station (Station 2):

[0238]

[0239] 3. Chrome plating station (Station 3):

[0240]

[0241] 4. Sealing station (Station 4):

[0242]

[0243] Optimized power distribution strategy:

[0244] 1. Dynamically adjust the power supply of the nickel plating station: Since the power demand of the nickel plating station is the highest (445.38 A), the system adjusts the power supply to 142.35 A to ensure uniform plating and maintain the best electroplating efficiency. Since the priority of the nickel plating station is the highest (W2 = 1.5), the system ensures a more stable current supply during high load periods.

[0245] 2. Optimize the current supply of the copper plating station: The demand of the copper plating station is the second highest (361.92 A), and the system allocates 92.34 A to ensure a reasonable current supply when the demand increases.

[0246] 3. Load balancing of the chrome plating station: The demand of the chrome plating station is relatively stable (343.44 A), but in low load situations, the system adjusts the current to 80.37 A to reduce power waste.

[0247] 4. Power reduction of the sealing station: Since the demand of the sealing station is the lowest (241.2 A), the system dynamically adjusts its power to 46.18 A to reduce unnecessary power supply and improve energy utilization.

[0248] The overall power utilization rate is increased by 8.7%, avoiding the waste of electric energy caused by over-power supply or under-power supply. The plating uniformity is increased by 5.4%. Especially in the nickel plating process, due to more accurate power supply, the plating thickness is more stable. The production rhythm is smoother. Due to the power load balance, the deviation of electroplating time is reduced by 7.9%, avoiding production delays caused by uneven power scheduling. The local power is reduced by 10.3%. Especially the current supply of the closed site is reduced, making the overall power scheduling more reasonable.

[0249] This case demonstrates how to optimize the current distribution based on the power priority scheduling mechanism through the dynamic power distribution algorithm, making the power supply of the electroplating production line more accurate, stable and efficient. Compared with the traditional fixed power distribution method, this method can optimize the power supply scheduling according to the real-time changes of the production status, improve the power utilization rate, and ensure the production quality and rhythm stability at the same time. Through the intelligent management of the PLC system, not only the plating quality is improved, but also the energy efficiency of the whole production process is optimized, providing a solid foundation for the intelligent manufacturing of the factory.

Claims

1. The control method of the electroplating production line optimized by PLC program is characterized by The following steps are involved: S1. Intelligent beat control based on process correlation optimization: S1.

1. Use a multi-dimensional data analysis model to comprehensively consider the key parameters of electroplating time, rack flow speed, and tank occupancy to construct a process time correlation matrix; S1.2, run adaptive beat adjustment in PLC, dynamically adjust the transmission rhythm to balance the flow between each process node; S2. Dynamic control of electroplating process parameters: S2.

1. Connect a multi-channel data acquisition system to the PLC to monitor key variables such as plating solution temperature, current density, voltage fluctuation, plating solution concentration, and pH value; S2.2, using the Bayesian optimization algorithm, calculate the electroplating process parameters based on historical data and real-time data, and dynamically adjust the output control value of the PLC; S3, Intelligent power distribution control: S3.

1. Use PLC to sample and analyze the current consumption pattern of each electroplating tank and establish a power demand prediction model; S3.2, distribute the current output through the dynamic power allocation algorithm to keep the power consumption at the optimal state; S3.3, combined with peak load reduction strategy, optimize power supply at low load; S4, Adaptive electroplating process flow optimization: S4.

1. Use PLC to collect data on plating tank status, conveyor chain load, and current production cycle in real time to establish a dynamic path decision model; S4.

2. Use reinforcement learning optimization algorithm to calculate the optimal flow path of each hanger and dynamically adjust the transmission direction.

2. The control method of the electroplating production line optimized by PLC program according to claim 1 is characterized in that The intelligent beat control method based on process correlation optimization includes: By establishing a process time correlation matrix, the time matching degree of different process sites is calculated in real time, making the beat adjustment more globally optimized; based on multiple key factors, including the electroplating time T e , Hanger circulation speed V g , Tank occupancy S l , current process status P c , factors are coupled with each other, affecting the synergistic relationship between process nodes; and to describe the time matching between each process site, the process time correlation matrix M is introduced c,t , the calculation method is as follows: in: M c,t represents the process time correlation matrix, which is used to evaluate the time matching between each process station; α i Represents the dynamic weight of each process site; T e,i represents the standard electroplating time of site i; β i Indicates the tank occupancy S l Factors affecting the flow rate; S l,i Indicates the current tank occupancy of site i, that is, the current load status of the tank; V g,i It represents the rack turnover speed of station i, and the transmission speed of parts in this process station.

3. The control method of the electroplating production line optimized by PLC program according to claim 2 is characterized in that The intelligent beat control method based on process correlation optimization includes: Adopt adaptive beat adjustment algorithm to adjust the time T of the hanger according to real-time production data d , so that the load between each process site is balanced; in this algorithm, PLC continuously monitors the process state change rate The influence of the rack turnover rate on the slot occupancy rate Calculate the best time to place the hanger: in: T d It represents the best time calculated, which makes the time when the hanger enters the process station accurate; T opt It represents the optimal cycle time calculated based on the process time correlation matrix; λ represents the process state fluctuation influencing factor, which controls the influence of the process state on the release time; It indicates the dynamic change rate of process parameters in the production process, indicating the degree of change of process status per unit time; μ indicates the adjustment factor reflecting the influence of the hanger turnover rate on the slot occupancy rate; It shows the relationship between the hanger turnover rate and the change of tank occupancy, and how the change of tank load affects the turnover speed of parts.

4. The control method of the electroplating production line optimized by PLC program according to claim 3 is characterized in that The intelligent beat control method based on process correlation optimization includes: An intelligent lag compensation mechanism is adopted. When a beat delay is detected at a process site, the beat of the upstream process is adjusted to keep the overall production rhythm stable. The PLC calculates the average delay trend of a process site through historical data and intelligently compensates for the beat when an abnormality occurs.

5. The control method of the electroplating production line optimized by PLC program according to claim 1 is characterized in that The intelligent power distribution control method comprises: Based on the PLC integrated current consumption pattern analysis system, the current consumption of each electroplating tank is sampled in real time, and a current consumption pattern is established to obtain the dynamic current demand characteristics under different process conditions; the PLC uses a multi-channel current acquisition module to collect the current input I(t) of each electroplating tank in real time and store long-term data to form a current consumption pattern M. i (t), the calculation formula is as follows: in: M i (t) represents the current consumption pattern of electroplating tank i at time t, reflecting the current consumption trend of the tank in different time periods; I(T) represents the real-time current input of the electroplating tank, indicating the current provided by the power supply at time t; α i Represents the current consumption weight factor, indicating the influence of different plating types and process types on current demand; β i The adjustment factor that represents the rate of change of current; It represents the instantaneous rate of change of current, which is used to identify the dynamic changes of current demand in a short period of time; T represents the data sampling period, that is, the time window for PLC to calculate the current consumption mode.

6. The control method of the electroplating production line optimized by PLC program according to claim 5 is characterized in that The intelligent power distribution control method comprises: After obtaining the current consumption pattern, the power demand of each plating tank in the future unit time is calculated based on the power demand prediction model, and the power distribution is dynamically adjusted through an intelligent optimization algorithm. The power demand prediction model uses short-term power prediction and long-term trend analysis to obtain the power demand forecast value of the plating tank in the future time period through integral calculation of the current consumption pattern.

7. The control method of the electroplating production line optimized by PLC program according to claim 6 is characterized in that The intelligent power distribution control method comprises: After predicting the power demand, the power scheduling is optimized based on the dynamic power allocation algorithm to maintain the current supply of different electroplating tanks in the optimal state and dynamically adjust the power output according to the real-time current demand; the power allocation adopts a priority scheduling mechanism, with the power priority W of each electroplating tank as the priority. i Based on, dynamic allocation is performed, and the calculation method is as follows: in: I out Indicates the calculated optimal current output value; W i Indicates the power priority of the ith electroplating tank, which is dynamically adjusted according to the current current demand, plating process requirements and power load conditions; P d (T f ) represents the predicted power demand value; It represents the total power demand weight of all electroplating tanks and the sum of the power priorities of the entire production line.

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