Management and monitoring system for automatically adding dye to anode

Abstract

The invention discloses a management monitoring system for automatically adding dye to an anode, relates to the technical field of dye monitoring, and aims to analyze the influence of bubbles on current based on anode surface oxidation reaction data, obtain current density change, discover polarization phenomenon in time and send out a warning signal. Therefore, the coating quality problem caused by electrode polarization can be effectively reduced, and the qualified rate of products is improved. The prediction analysis module constructs a current fluctuation coefficient Dbxs by analyzing current fluctuation and a workpiece surface state in combination with the dynamic change of an electrolyte solution, and further calculates the consumption rate and the prediction concentration of the dye, and through the module, the system can predict the use condition of the dye in advance, so that excessive or insufficient dye addition is further avoided, and the service life of the system is prolonged. The cost is saved; and the dyeing uniformity is kept. The dye management and control module can intelligently judge the consumption condition of the dye and automatically send out a dye supplement instruction according to the result of the prediction and analysis module, and automation and accurate control over dye supplement are achieved through the module.

Description

Technical Field

[0001] The invention relates to the technical field of dye monitoring, in particular to an anode automatic dye adding management and monitoring system. Background Art

[0002] The automatic dye addition management and monitoring system for anodes belongs to the field of industrial automation and electrochemical surface treatment, especially in the control and optimization of anodizing and electroplating processes. This field is widely used in industries such as metal processing, surface protection and decorative treatment, involving the use of electrochemical reactions to form protective or decorative oxide films or coatings on the surface of metal workpieces. Anodizing is a technology commonly used for surface treatment of metals (especially aluminum and its alloys), which improves the corrosion resistance, hardness and appearance of metals by forming an oxide film on the anode surface. In the anodizing process, the addition of dyes is essential to achieve precise coloring and aesthetic effects.

[0003] In the traditional anodizing process, the anodizing process relies on manual or semi-automatic methods to monitor and manage the changes in the electrolyte solution, the surface state of the workpiece, and the concentration of the dye. This method often has problems such as delayed reaction and insufficient control accuracy, resulting in large fluctuations in the dye concentration, and the thickness of the coating or oxide layer and the dyeing effect are difficult to control uniformly. In addition, the generation of bubbles and electrode polarization phenomena are difficult to analyze in real time, and current fluctuations may affect the formation of the oxide film and the dyeing effect. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an anode automatic dye addition management and monitoring system, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an anode automatic dye addition management and monitoring system, including a data acquisition module, a data processing module, a polarization analysis module, a prediction analysis module and a dye control module;

[0006] The data acquisition module is used to install several groups of monitoring equipment in the anodizing tank in advance, and monitor the dynamic change data of the electrolyte solution in the anodizing tank, the surface state data of the relevant workpiece and the relevant oxidation reaction data of the anode surface in real time according to the several groups of monitoring equipment;

[0007] The data processing module is used to pre-process the dynamic change data of the electrolyte solution in the anodizing tank, the relevant workpiece surface state data and the relevant oxidation reaction data of the anode surface, and perform linear normalization processing on the pre-processed data so that the pre-processed data range falls within [0,1];

[0008] The polarization analysis module is used to analyze the influence of bubble generation on current during the anodic oxidation process based on the relevant oxidation reaction data on the anode surface, so as to obtain the current density Dmd under different bubble states, and preliminarily judge whether the electrode is polarized based on the current density Dmd value, and if so, issue a current fluctuation analysis instruction to the outside;

[0009] The prediction and analysis module is used to calculate the coating uniformity factor Gbyz in combination with the relevant workpiece surface state data after receiving the current fluctuation analysis instruction, and to construct the current fluctuation coefficient Dbxs in combination with the dynamic change data of the electrolyte solution in the anodizing tank, and to obtain the dye consumption rate Rhs(t0) according to the current fluctuation, and to estimate the dye predicted concentration value Rghs(t+Δt) in combination with the trained prediction model;

[0010] The dye control module is used to determine whether to issue a dye replenishment instruction based on the dye predicted concentration value Rghs(t+Δt) obtained in the prediction analysis module.

[0011] Preferably, the data acquisition module includes a deployment unit, a first monitoring unit, a second monitoring unit and a third monitoring unit;

[0012] The deployment unit is used to deploy several groups of monitoring equipment in the anodizing tank, wherein the several groups of monitoring equipment include a laser profiler, a three-dimensional scanner, a Hall effect current sensor, an X-ray fluorescence coating thickness meter, a laser displacement sensor, a temperature sensor and a spectrophotometer;

[0013] The first monitoring unit is used to monitor the dynamic change data of the electrolyte solution in the anodizing tank in real time using the monitoring equipment, and the dynamic change data of the electrolyte solution in the anodizing tank includes the electrode offset value Djpz, the dye concentration Rghs, the total surface area Zbmj of the electrode, the temperature Dwd of the electrolyte solution and the total volume Rtj of the electrolyte solution in the anodizing tank in each time period;

[0014] The second monitoring unit is used to monitor the relevant workpiece surface status data in real time using the monitoring equipment. The relevant workpiece surface status data includes the coating thickness Hdz of each part of the workpiece surface, and calculates the standard deviation Hdz of the coating thickness respectively through a statistical algorithm. σ And the average thickness of the coating Hdz avg ;

[0015] The third monitoring unit is used to monitor the relevant oxidation reaction data of the anode surface in real time using the monitoring device, and the relevant oxidation reaction data includes the current density Dlz when there is no bubble coverage, the maximum current density Dlz when there is no bubble coverage max And the electrode surface area covered by bubbles Dbmj.

[0016] Preferably, the data processing module includes a preprocessing unit and a standardization unit;

[0017] The pre-processing unit is used to detect and remove abnormal values ​​and duplicate values ​​in the dynamic change data of the electrolyte solution in the anodizing tank, the relevant workpiece surface state data and the relevant oxidation reaction data of the anode surface, and fill the missing data points by interpolation to generate the in-tank data set;

[0018] The standardization unit is used to normalize information of different dimensions in the in-slot data set to make them within the same range, and to extract features from the information in the in-slot data set.

[0019] Preferably, the polarization analysis module includes a preliminary analysis unit and a judgment unit;

[0020] The preliminary analysis unit is used to analyze the influence of the generation of bubbles on the current during the anodic oxidation process based on the relevant oxidation reaction data on the anode surface, so as to obtain the current density Dmd under different bubble states, which is specifically obtained in the following manner:

[0021]

[0022] Where Dlz max It is represented as the maximum current density when there is no bubble coverage, Dbmj is represented as the electrode surface area covered by bubbles, and Zbmj is represented as the total surface area of ​​the electrode. Expressed as bubble coverage.

[0023] Preferably, the judgment unit is used to pre-set a density threshold, and compare and analyze the current density Dmd under the corresponding bubble state with the density threshold to preliminarily judge whether the electrode is polarized. The specific judgment content is as follows:

[0024] If the current density Dmd in the corresponding bubble state exceeds the density threshold, it will be preliminarily judged that the electrode has a polarization risk and issue a current fluctuation analysis instruction;

[0025] If the current density Dmd under the corresponding bubble state does not exceed the density threshold, it can be preliminarily judged that there is no polarization risk of the electrode.

[0026] Preferably, the prediction and analysis module includes a coating analysis unit, a fluctuation analysis unit and a prediction unit;

[0027] The coating analysis unit is used to calculate the coating uniformity factor Gbyz in combination with relevant workpiece surface state data after receiving the current fluctuation analysis instruction, which is specifically obtained in the following manner:

[0028]

[0029] In the formula, Hdz σ Hdz is the standard deviation of the coating thickness, indicating the degree of fluctuation of the coating thickness; avg Expressed as the average thickness of the coating.

[0030] Preferably, the fluctuation analysis unit is used to construct a current fluctuation coefficient Dbxs according to the dynamic change data of the electrolyte solution in the anodizing tank and in combination with the coating uniformity factor Gbyz after linear normalization. The current fluctuation coefficient Dbxs is specifically obtained by the following formula:

[0031]

[0032] Wherein, Djpz represents the electrode offset value, Dwd represents the temperature of the electrolyte solution, α and β are weight values, and the specific values ​​of α and β are set by the user according to the situation, and C represents the correction constant.

[0033] Preferably, the prediction unit is used to calculate and obtain the dye consumption rate Rhs(t) according to the fluctuation of the current, and is specifically obtained in the following manner:

[0034]

[0035] Wherein, Dlz(t) represents the current value at the current moment, a1 and a2 are weight values, DWd(t) represents the current temperature value of the liquid, DWd(t0) represents the standard temperature value, and e represents the Euler number, which is approximately 2.71828.

[0036] Preferably, the initial model is constructed using convolutional neural network technology, and the initial model is trained and tested with the information in the data set in the tank, and the trained initial model is used as the state recognition model, and the characteristic information in the state recognition model is obtained respectively, and the state recognition model is trained and tested with the obtained characteristic information, and the trained state recognition model is used as the prediction model in combination with the current fluctuation analysis instruction sent out by the system. After training, the prediction model estimates the predicted concentration value Rghs(t+Δt) of the dye, which is obtained specifically in the following manner:

[0037]

[0038] Where Rghs(t) represents the dye concentration at the current moment, Rhs(t) represents the dye consumption rate, Dbxs represents the current fluctuation coefficient, and Δt represents the time interval.

[0039] Preferably, the dye control module compares the dye predicted concentration value Rghs(t+Δt) obtained in the prediction and analysis module with the target concentration range Mnd to determine whether to issue a dye replenishment instruction, specifically:

[0040] If the predicted dye concentration value Rghs(t+Δt) falls within the target concentration range Mnd, no dye replenishment instruction will be issued temporarily;

[0041] If the predicted dye concentration value Rghs(t+Δt) does not fall within the target concentration range Mnd, a dye replenishment instruction will be issued. According to the dye replenishment instruction, the dye replenishment pump will be automatically started to inject the dye into the anodizing tank according to the dye replenishment amount Rbz, wherein the dye replenishment amount Rbz is obtained by the following formula: Rbz=ΔRghs*Rtj; wherein ΔRghs represents the difference between the predicted dye concentration value and the middle value within the target range, and Rtj represents the total volume of the electrolyte solution in the anodizing tank.

[0042] The present invention provides an anode automatic dye addition management and monitoring system, which has the following beneficial effects:

[0043] (1) Through several groups of monitoring devices in the data acquisition module, the dynamic changes of the electrolyte solution, the surface state of the workpiece, and the oxidation reaction of the anode surface are monitored in real time, providing high-precision data support for subsequent analysis. In this way, the key changes in the anodizing process can be grasped in real time, reducing the error of human intervention. Through the polarization analysis module, based on the anode surface oxidation reaction data, the influence of bubbles on the current is analyzed, and the current density change is obtained. The polarization phenomenon can be discovered in time and a warning signal can be issued. This can effectively reduce the coating quality problems caused by electrode polarization and improve the product qualification rate. The prediction analysis module analyzes the current fluctuation and the surface state of the workpiece, combines the dynamic changes of the electrolyte solution, constructs the current fluctuation coefficient Dbxs, and then calculates the consumption rate and predicted concentration of the dye. Through this module, the system can predict the use of the dye in advance, further avoid excessive or insufficient dye addition, save costs and maintain dyeing uniformity. According to the results of the prediction analysis module, the dye control module can intelligently judge the consumption of the dye and automatically issue a dye replenishment instruction. Through this module, the automation and precise control of dye replenishment are realized, reducing the waste of dye and uneven dyeing. In short, the system further realizes the precise monitoring of the anodizing process and the automated management of dyes through the collaborative work of multiple modules, which not only improves production efficiency and reduces manual intervention, but also improves the quality of finished products and the economic benefits of dye use.

[0044] (2) Through the preliminary analysis unit, the system can analyze the impact of different bubble states on the current, especially the impact of bubble coverage on the current density, based on the relevant oxidation reaction data on the anode surface. By calculating the ratio of the bubble coverage surface area to the total electrode surface area, the current density under different states can be accurately estimated, providing data support for the subsequent judgment of the polarization state of the electrode. The judgment unit sets a threshold for current density. By comparing the current density under different bubble states with the preset threshold, it can timely determine whether there is a polarization risk on the anode surface, thus realizing the automation and intelligence of polarization detection, further avoiding the shortcomings of the traditional method that relies on manual judgment, and improving the control accuracy of the anodizing process.

[0045] (3) The prediction unit analyzes the dye consumption rate during the anodizing process by calculating the dye consumption rate. By combining temperature and current fluctuations with dye consumption, the system can dynamically adjust the dye addition rate to further avoid dye waste and ensure the uniformity and consistency of dyeing. The system uses convolutional neural network (CNN) technology to build an initial model and uses the data set in the anodizing tank for training and testing. Through the initial model after multiple training, the system can effectively extract key feature information in the process and accurately identify different states in the anodizing process. Combined with the current fluctuation analysis instruction, the system uses the trained initial model as a state recognition model for further optimization of the prediction. Through the training and testing of the state recognition model, the system can build an accurate prediction model. The prediction model can estimate the predicted concentration of the dye in real time based on the current fluctuation and related data. Through this intelligent prediction, the system can predict the dye consumption in advance and add dye in time according to actual needs. This data-based dye management method makes the entire anodizing and dyeing process more automated and intelligent, further reducing the necessity of manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a block diagram of an anode automatic dye addition management and monitoring system of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1, the present invention provides an anode automatic dye addition management and monitoring system, including a data acquisition module, a data processing module, a polarization analysis module, a prediction analysis module and a dye control module;

[0050] The data acquisition module is used to install several groups of monitoring equipment in the anodizing tank in advance, and monitor the dynamic change data of the electrolyte solution in the anodizing tank, the surface state data of the relevant workpiece and the relevant oxidation reaction data of the anode surface in real time according to the several groups of monitoring equipment;

[0051] The data processing module is used to pre-process the dynamic change data of the electrolyte solution in the anodizing tank, the relevant workpiece surface state data and the relevant oxidation reaction data of the anode surface, and perform linear normalization processing on the pre-processed data so that the pre-processed data range falls within [0,1];

[0052] The polarization analysis module is used to analyze the influence of bubble generation on current during the anodic oxidation process based on the relevant oxidation reaction data on the anode surface, so as to obtain the current density Dmd under different bubble states, and preliminarily judge whether the electrode is polarized based on the current density Dmd value, and if so, issue a current fluctuation analysis instruction to the outside;

[0053] The prediction and analysis module is used to calculate the coating uniformity factor Gbyz in combination with the relevant workpiece surface state data after receiving the current fluctuation analysis instruction, and to construct the current fluctuation coefficient Dbxs in combination with the dynamic change data of the electrolyte solution in the anodizing tank, and to obtain the dye consumption rate Rhs(t) according to the current fluctuation, and to estimate the dye predicted concentration value Rghs(t+Δt) in combination with the trained prediction model;

[0054] The dye control module is used to determine whether to issue a dye replenishment instruction based on the dye predicted concentration value Rghs(t+Δt) obtained in the prediction analysis module.

[0055] During the operation of this system, the system can collect relevant data of electrolyte solution, workpiece surface and anode surface in real time by installing several groups of monitoring equipment in the anodizing tank. After processing these data, the system can accurately grasp the electrolyte state, workpiece surface changes and oxidation reaction dynamics in the anodizing process, ensure that the process runs under relatively optimal conditions, and effectively avoid delays and errors caused by human monitoring. Through the data acquisition module, the system can monitor the dynamic changes of electrolyte solution in the anodizing tank, the state data of the workpiece surface and the oxidation reaction data of the anode surface in real time. This multi-level data acquisition ensures that the system has a multi-faceted understanding of the anodizing process, making the monitoring of the entire process more accurate, and further avoiding the problem of delayed response to process state changes in traditional systems. The data processing module normalizes all data to the set range through linear normalization, effectively solving the deviation problem caused by different data dimensions. This data preprocessing technology improves the accuracy of subsequent analysis modules and the overall calculation efficiency of the system, ensuring the uniformity and consistency between different data sources. Through the polarization analysis module, the system can analyze the influence of bubble generation on current based on the oxidation reaction data on the anode surface, and obtain the current density under different bubble states in real time. Through the fluctuation of current density, the system can quickly determine whether the electrode is polarized. Once the risk of polarization occurs, the system will automatically issue a current fluctuation analysis instruction, further avoiding the process instability caused by polarization, and helping to adjust the current in time to ensure the quality of the oxide film. The prediction analysis module analyzes the uniformity of the coating by calculating the coating uniformity factor Gbyz and combining it with the current fluctuation coefficient. In addition, the system can accurately predict the consumption rate of the dye according to the current fluctuation, and estimate the future dye concentration through the trained prediction model. This prediction capability further improves the automation level of the system, ensuring the maximum efficiency of dye use while reducing dye waste. The dye control module makes judgments based on the predicted dye concentration value and intelligently decides whether to issue a dye replenishment instruction. This automated dye management method effectively avoids the situation of untimely or excessive dye replenishment in traditional manual operations, ensures process stability, and improves the consistency of dyeing effects and production efficiency. In short, the system further improves the level of automated management of the anodizing process by integrating functions such as real-time monitoring, data preprocessing, polarization analysis, and intelligent prediction, ensuring the uniformity of the coating, the precise use of dyes, and the stability of the current, minimizing uncertainty and waste in the process, thereby improving production efficiency and product quality.

[0056] Example 2

[0057] Please refer to Figure 1 , specifically: the data acquisition module includes a deployment unit, a first monitoring unit, a second monitoring unit and a third monitoring unit;

[0058] The deployment unit is used to deploy several groups of monitoring equipment in the anodizing tank, wherein the several groups of monitoring equipment include a laser profiler, a three-dimensional scanner, a Hall effect current sensor, an X-ray fluorescence coating thickness meter, a laser displacement sensor, a temperature sensor and a spectrophotometer;

[0059] The first monitoring unit is used to monitor the dynamic change data of the electrolyte solution in the anodizing tank in real time using the monitoring equipment, and the dynamic change data of the electrolyte solution in the anodizing tank includes the electrode offset value Djpz, the dye concentration Rghs, the total surface area Zbmj of the electrode, the temperature Dwd of the electrolyte solution and the total volume Rtj of the electrolyte solution in the anodizing tank in each time period;

[0060] The second monitoring unit is used to monitor the relevant workpiece surface status data in real time using the monitoring equipment. The relevant workpiece surface status data includes the coating thickness Hdz of each part of the workpiece surface, and calculates the standard deviation Hdz of the coating thickness respectively through a statistical algorithm. σ And the average thickness of the coating Hdz avg ;

[0061] The third monitoring unit is used to monitor the relevant oxidation reaction data of the anode surface in real time using the monitoring device, and the relevant oxidation reaction data includes the current density Dlz when there is no bubble coverage, the maximum current density Dlz when there is no bubble coverage max And the electrode surface area covered by bubbles Dbmj.

[0062] The data processing module includes a preprocessing unit and a standardization unit;

[0063] The preprocessing unit is used to detect and remove abnormal values ​​and duplicate values ​​in the dynamic change data of the electrolyte solution in the anodizing tank, the relevant workpiece surface state data and the relevant oxidation reaction data of the anode surface, and fill the missing data points by interpolation to generate the data set in the tank, wherein the missing values ​​are filled by using the average value, interpolation method or regression model to fill the missing values. In the process of data cleaning, it is often necessary to detect and remove abnormal values. For example, the Z-score method can be used to detect abnormal values;

[0064] The standardization unit is used to normalize the information of different dimensions in the in-slot data set so that they are within the same range, to prevent the magnitude of certain features from being too large or too small and causing deviations to the model. For example, the magnitudes of current and temperature are different, and standardization can make them comparable on the same scale, and feature extraction can be performed on the information in the in-slot data set. Feature extraction can capture dynamic features by calculating the rate of change of variables.

[0065] In this embodiment, the first, second and third monitoring units deployed in the data acquisition module monitor the data of multiple dimensions such as the dynamic change of the electrolyte solution, the surface state of the workpiece and the anodic oxidation reaction in real time. By monitoring the key parameters such as the electrode offset value in the anodic oxidation tank, the dye concentration, the coating thickness on the surface of the workpiece, and the bubble coverage on the anode surface, a full range of dynamic monitoring of the anodic oxidation process is provided, especially the real-time statistics of the standard deviation and the average value of the coating thickness, which helps to detect the coating uniformity on the surface of the workpiece and improves the control ability of the final coating quality. The pre-processing unit in the data processing module detects and removes outliers and duplicates, effectively avoiding the influence of noise data on the system, thereby ensuring the accuracy of the monitoring data. For example, the Z-score method can efficiently identify outliers and process them. At the same time, the interpolation method or regression model is used to fill the missing data points, which ensures the continuity and integrity of the data and provides a complete data set for subsequent data analysis. The standardization unit normalizes the data of different dimensions to prevent the model deviation caused by the difference in the magnitude of the eigenvalues. This is particularly important. The data of different dimensions such as current density and temperature are compared on a unified scale, which can effectively improve the accuracy and performance of the model. In addition, through feature extraction, such as the calculation of the rate of change of variables, the system can effectively capture the dynamic characteristics of different working conditions during the anodizing process, which helps to gain a deeper understanding of the changes in the electrolyte solution and the surface coating of the workpiece, thereby optimizing the dye management strategy. Through data cleaning, normalization, and feature extraction, the system can more accurately predict the dye consumption rate, thereby realizing the automation and precision management of dye addition, which not only reduces the waste of dyes, but also ensures the uniformity of dyeing in the anodizing process, further improving the quality of the product. In short, this method further realizes the comprehensive monitoring of the anodizing process through the efficient collaboration of data acquisition and processing modules, improves the accuracy and consistency of the data, reduces misjudgments caused by abnormal data, significantly optimizes the intelligent management of dye consumption prediction and addition, and improves the production efficiency and quality control capabilities of the anodizing process.

[0066] Example 3

[0067] Please refer to Figure 1 , specifically: the polarization analysis module includes a preliminary analysis unit and a judgment unit;

[0068] The preliminary analysis unit is used to analyze the influence of the generation of bubbles on the current during the anodic oxidation process based on the relevant oxidation reaction data on the anode surface, so as to obtain the current density Dmd under different bubble states, which is specifically obtained in the following manner:

[0069]

[0070] Where Dlz maxIt is represented as the maximum current density when there is no bubble coverage, Dbmj is represented as the electrode surface area covered by bubbles, and Zbmj is represented as the total surface area of ​​the electrode. Expressed as bubble coverage.

[0071] The electrode surface area Dbmj covered by bubbles mentioned above can be detected by a laser profilometer to detect the surface profile, and the bubble coverage can be estimated by analyzing the surface changes;

[0072] The total surface area Zbmj of the electrode can be obtained by scanning the surface shape of the electrode with a three-dimensional scanner;

[0073] The current density Dlz can be monitored and calculated by a Hall effect current sensor and a shunt resistor.

[0074] The judgment unit is used to pre-set a density threshold, and compare and analyze the current density Dmd under the corresponding bubble state with the density threshold to preliminarily judge whether the electrode is polarized. The specific judgment content is as follows:

[0075] If the current density Dmd in the corresponding bubble state exceeds the density threshold, it will be preliminarily judged that the electrode has a polarization risk and issue a current fluctuation analysis instruction;

[0076] If the current density Dmd under the corresponding bubble state does not exceed the density threshold, it can be preliminarily judged that there is no polarization risk of the electrode.

[0077] In this embodiment, the preliminary analysis unit of the polarization analysis module is based on the oxidation reaction data of the anode surface, and uses the bubble coverage of the electrode to calculate the current density Dmd under different bubble states through a formula. This analysis method combines the electrode surface area covered by bubbles with the total surface area of ​​the electrode, accurately reflecting the influence of bubbles on the current distribution. This precise calculation method can provide reliable basic data for subsequent polarization judgment, which helps to reduce the influence of abnormal current fluctuations during the anodic oxidation process. Through the density threshold value pre-set by the judgment unit, the difference between the current density Dmd under different bubble states and the threshold value is compared in real time. The system can dynamically monitor whether the electrode has a polarization risk. When the current density exceeds the set threshold, the system can issue a current fluctuation analysis instruction in time. This automated judgment mechanism further reduces the error and time delay of manual detection, and ensures the real-time and accuracy of the anodic oxidation process. When the current density Dmd exceeds the density threshold, the system can preliminarily judge that the electrode has a polarization risk and quickly issue an early warning signal. Through this mechanism, the occurrence of polarization can be effectively prevented, and the problems of current instability, uneven coating or anode damage caused by electrode polarization can be further avoided, thereby improving the service life of the electrode and product quality during the production process. The judgment unit provides an automated polarization risk analysis and control function, which can not only make judgments on the polarization state of the electrode based on real-time data, but also take timely countermeasures. This automated risk control system reduces the need for manual intervention, ensures the stability and consistency of the anodizing process, and greatly improves the automation level of the process. The system can predict the polarization trend of the electrode in advance based on the change of current density under different bubble states and the set density threshold. Compared with the traditional manual monitoring method, the system can identify abnormal changes in current density earlier, which helps to take preventive measures before the polarization risk actually occurs, thereby reducing production stagnation or product defects caused by polarization and improving overall production efficiency. In short, this method realizes accurate analysis of current density and real-time monitoring of polarization risk through the preliminary analysis unit and judgment unit of the polarization analysis module, which not only improves the safety and stability of the anodizing process, but also reduces production failures caused by polarization, and improves the quality of finished products and production efficiency.

[0078] Example 4

[0079] Please refer to Figure 1 ,Specifically: the prediction and analysis module includes a coating analysis unit, a fluctuation analysis unit and a prediction unit;

[0080] The coating analysis unit is used to calculate the coating uniformity factor Gbyz in combination with relevant workpiece surface state data after receiving the current fluctuation analysis instruction, which is specifically obtained in the following manner:

[0081]

[0082] In the formula, Hdz σ Hdz is the standard deviation of the coating thickness, indicating the degree of fluctuation of the coating thickness; avg Expressed as the average thickness of the coating.

[0083] The coating thickness Hdz at various locations on the surface of the above-mentioned workpiece can be measured by an X-ray fluorescence coating thickness meter, an eddy current coating thickness meter, and a laser rangefinder.

[0084] The fluctuation analysis unit is used to construct a current fluctuation coefficient Dbxs according to the dynamic change data of the electrolyte solution in the anodizing tank and in combination with the coating uniformity factor Gbyz after linear normalization. The current fluctuation coefficient Dbxs is specifically obtained by the following formula:

[0085]

[0086] Wherein, Djpz represents the electrode offset value, Dwd represents the temperature of the electrolyte solution, α and β are weight values, and the specific values ​​of α and β are set by the user according to the situation, and C represents the correction constant.

[0087] The electrode offset value Djpz mentioned above can be used to monitor the displacement or offset state of the electrode during the process by a laser displacement sensor;

[0088] The temperature Dwd of the electrolyte solution can be monitored and obtained by a temperature sensor.

[0089] In this embodiment, through the coating analysis unit, the system can calculate the uniformity factor Gbyz of the coating after receiving the current fluctuation analysis instruction, combined with the surface state data of the workpiece, and evaluate the uniformity of the coating. Through this formula, the uniformity of the coating can be intuitively evaluated. The fluctuation analysis unit is based on the dynamic change data of the electrolyte solution in the anodizing tank, combined with the coating uniformity factor Gbyz, and constructs the current fluctuation coefficient Dbxs after linear normalization. Through this formula, the system can accurately reflect the influence of the state of the electrolyte solution on the fluctuation of the current. This analysis method can not only capture the fluctuation of the current, but also set the weight according to the actual working conditions, providing a more personalized and adaptive prediction. The system realizes the accurate prediction and control of the current fluctuation by constructing the current fluctuation coefficient Dbxs, combining the coating uniformity factor and the dynamic change data of the electrolyte solution. This method not only improves the prediction accuracy of the current fluctuation, but also adjusts the current parameters through advance analysis to avoid the influence of excessive current fluctuation on the anodizing and coating uniformity. Through the coordinated work of the coating analysis and fluctuation analysis units in the prediction and analysis module, the system can quickly respond and adjust the process parameters when current fluctuations are detected, realizing automated control and optimization of the anodizing process, reducing manual intervention, and improving production stability and efficiency. In short, this method not only realizes the accurate evaluation of the uniformity of the coating through the application of the prediction and analysis module, but also further optimizes the anodizing process through the prediction and control of the current fluctuation coefficient, improves the quality and production efficiency of the finished product, and provides users with flexible and customized control methods.

[0090] Example 5

[0091] Please refer to Figure 1 Specifically: the prediction unit is used to calculate and obtain the dye consumption rate Rhs(t) according to the fluctuation of the current, and is specifically obtained in the following manner:

[0092]

[0093] Wherein, Dlz(t) represents the current value at the current moment, a1 and a2 are weight values, DWd(t) represents the current temperature value of the liquid, DWd(t0) represents the standard temperature value, and e represents the Euler number, which is approximately 2.71828.

[0094] The above current value can be monitored and obtained through a current sensor, a shunt resistor and a current transmitter;

[0095] The initial model is constructed using convolutional neural network technology, and the initial model is trained and tested with the information in the tank data set. The trained initial model is used as the state recognition model, and the characteristic information in the state recognition model is obtained respectively. The state recognition model is trained and tested with the obtained characteristic information. Combined with the current fluctuation analysis instruction sent out by the system, the trained state recognition model is used as the prediction model. After training, the prediction model estimates the dye prediction concentration value Rghs(t+Δt), which is obtained in the following way:

[0096]

[0097] Where Rghs(t) represents the dye concentration at the current moment, Rhs(t) represents the dye consumption rate, Dbxs represents the current fluctuation coefficient, and Δt represents the time interval.

[0098] The dye concentration Rghs mentioned above can be monitored and obtained by a spectrophotometer and an online colorimeter.

[0099] In this embodiment, through the prediction unit, the system calculates the consumption rate of the dye according to the current fluctuation. This formula can accurately capture the influence of current fluctuation and liquid temperature on dye consumption. Through the setting of weight values, the system can flexibly adjust the influence weights of current and temperature, realize real-time monitoring of the dye consumption rate, and thus optimize the dye replenishment decision. The prediction unit uses convolutional neural network (CNN) technology to build an initial model, and uses the information in the data set in the tank to train and test the model. This deep learning technology can capture complex nonlinear relationships, effectively identify different state information in the anodizing tank, and improve the self-learning ability and prediction accuracy of the system. The feature information of the data in the tank is extracted through the convolutional neural network, and the state recognition model is trained and optimized using this information. The optimization of the state recognition model can better capture the influence of current fluctuation and temperature change on dye consumption, and provide more accurate feature input for the subsequent prediction model. This process improves the learning and adaptability of the system as much as possible, so that it can cope with complex process environment changes. The trained state recognition model is used as a prediction model to predict the concentration value of the dye. Through this formula, the system can dynamically predict the change of dye concentration. Combined with the current fluctuation coefficient and consumption rate, the system can accurately estimate the consumption trend of the dye. This real-time prediction capability can help the system predict the use of the dye in advance, avoid insufficient or excessive addition of dye, and ensure the stability of the process. In short, this method realizes the dynamic prediction and management of dye concentration by accurately calculating the current fluctuation and dye consumption rate in the prediction unit, combined with the deep learning technology of convolutional neural network. The system can adjust the dye replenishment in real time, improve the automation and intelligence level of production, further reduce dye waste, and ensure the high efficiency and high quality of the anodizing process.

[0100] Example 6

[0101] Please refer to Figure 1 Specifically: the dye control module compares the dye prediction concentration value Rghs9t+Δt) obtained in the prediction analysis module with the target concentration range Mnd to determine whether to issue a dye replenishment instruction. The specific content is:

[0102] If the predicted dye concentration value Rghs(t+Δt) falls within the target concentration range Mnd, no dye replenishment instruction will be issued temporarily;

[0103] If the predicted dye concentration value Rghs(t+Δt) does not fall within the target concentration range Mnd, a dye replenishment instruction will be issued. According to the dye replenishment instruction, the dye replenishment pump will be automatically started to inject the dye into the anodizing tank according to the dye replenishment amount Rbz, wherein the dye replenishment amount Rbz is obtained by the following formula: Rbz=ΔRghs*Rtj; wherein ΔRghs represents the difference between the predicted dye concentration value and the middle value within the target range, and Rtj represents the total volume of the electrolyte solution in the anodizing tank; and the system will automatically stop after the replenishment pump reaches the required amount of dye to avoid excessive replenishment.

[0104] In this embodiment, the system can accurately determine whether the current dye concentration needs to be adjusted by comparing the dye prediction concentration value obtained by the prediction analysis module with the pre-set target concentration range. If the predicted concentration value is within the target range, the system does not issue a dye replenishment instruction, further avoiding unnecessary dye replenishment, reducing dye waste and operation frequency, and effectively improving the efficiency of the system. When the dye prediction concentration value exceeds the target concentration range, the system will automatically issue a dye replenishment instruction and realize automatic replenishment by starting the dye replenishment pump. The replenishment process is completely controlled by the system, and accurate dye addition can be performed according to real-time needs, further reducing the need for manual intervention, while ensuring the continuity and stability of the dyeing process. The dye prediction concentration value Rghs (t+Δt) can accurately calculate the required dye replenishment amount according to the predicted demand, further avoiding excessive addition. By calculating the concentration difference, the system can quickly respond to concentration fluctuations, ensure the accuracy and timeliness of dye replenishment, and ensure the consistency of dyeing effects. During the dye replenishment process, the system controls the replenishment pump in real time. When the replenishment amount reaches the required amount of dye, the system will automatically stop the replenishment pump to avoid excessive dye injection. This function not only reduces the waste of dye, but also avoids the impact of excessive dye on the anodizing tank and process quality, thereby improving the safety and economic benefits of the entire system. By integrating dye prediction, replenishment instructions, replenishment amount calculation and pump control into the same automated process, the system realizes fully automated dye management. At the same time, the system's intelligent judgment mechanism can reduce unnecessary replenishment operations, further reducing energy consumption and production costs.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An anode automatic dye addition management and monitoring system, characterized in that: It includes data acquisition module, data processing module, polarization analysis module, prediction analysis module and dye control module; The data acquisition module is used to install several groups of monitoring equipment in the anodizing tank in advance, and monitor the dynamic change data of the electrolyte solution in the anodizing tank, the surface state data of the relevant workpiece and the relevant oxidation reaction data of the anode surface in real time according to the several groups of monitoring equipment; The data processing module is used to pre-process the dynamic change data of the electrolyte solution in the anodizing tank, the relevant workpiece surface state data and the relevant oxidation reaction data of the anode surface, and perform linear normalization processing on the pre-processed data so that the pre-processed data range falls within [0,1]; The polarization analysis module is used to analyze the influence of bubble generation on current during the anodic oxidation process based on the relevant oxidation reaction data on the anode surface, so as to obtain the current density Dmd under different bubble states, and preliminarily judge whether the electrode is polarized based on the current density Dmd value, and if so, issue a current fluctuation analysis instruction to the outside; The prediction and analysis module is used to calculate the coating uniformity factor Gbyz in combination with the relevant workpiece surface state data after receiving the current fluctuation analysis instruction, and to construct the current fluctuation coefficient Dbxs in combination with the dynamic change data of the electrolyte solution in the anodizing tank, and to obtain the dye consumption rate Rhs(t) according to the current fluctuation, and to estimate the dye predicted concentration value Rghs(t+Δt) in combination with the trained prediction model; The dye control module is used to determine whether to issue a dye replenishment instruction based on the dye predicted concentration value Rghs(t+Δt) obtained in the prediction analysis module.

2. The anode automatic dye addition management and monitoring system according to claim 1 is characterized by: The data acquisition module includes a deployment unit, a first monitoring unit, a second monitoring unit and a third monitoring unit; The deployment unit is used to deploy several groups of monitoring equipment in the anodizing tank, wherein the several groups of monitoring equipment include a laser profiler, a three-dimensional scanner, a Hall effect current sensor, an X-ray fluorescence coating thickness meter, a laser displacement sensor, a temperature sensor and a spectrophotometer; The first monitoring unit is used to monitor the dynamic change data of the electrolyte solution in the anodizing tank in real time using the monitoring equipment, and the dynamic change data of the electrolyte solution in the anodizing tank includes the electrode offset value Djpz, the dye concentration Rghs, the total surface area Zbmj of the electrode, the temperature Dwd of the electrolyte solution and the total volume Rtj of the electrolyte solution in the anodizing tank in each time period; The second monitoring unit is used to monitor the relevant workpiece surface status data in real time using the monitoring equipment. The relevant workpiece surface status data includes the coating thickness Hdz of each part of the workpiece surface, and calculates the standard deviation Hdz of the coating thickness respectively through a statistical algorithm. σ And the average thickness of the coating Hdz avg ; The third monitoring unit is used to monitor the relevant oxidation reaction data of the anode surface in real time using the monitoring device, and the relevant oxidation reaction data includes the current density Dlz when there is no bubble coverage, the maximum current density Dlz when there is no bubble coverage max And the electrode surface area covered by bubbles Dbmj.

3. The anode automatic dye addition management and monitoring system according to claim 2 is characterized by: The data processing module includes a preprocessing unit and a standardization unit; The pre-processing unit is used to detect and remove abnormal values ​​and duplicate values ​​in the dynamic change data of the electrolyte solution in the anodizing tank, the relevant workpiece surface state data and the relevant oxidation reaction data of the anode surface, and fill the missing data points by interpolation to generate the in-tank data set; The standardization unit is used to normalize information of different dimensions in the in-slot data set to make them within the same range, and to extract features from the information in the in-slot data set.

4. The anode automatic dye addition management and monitoring system according to claim 3 is characterized by: The polarization analysis module includes a preliminary analysis unit and a judgment unit; The preliminary analysis unit is used to analyze the influence of the generation of bubbles on the current during the anodic oxidation process based on the relevant oxidation reaction data on the anode surface, so as to obtain the current density Dmd under different bubble states, which is specifically obtained in the following manner: Where Dlz max It is represented as the maximum current density when there is no bubble coverage, Dbmj is represented as the electrode surface area covered by bubbles, and Zbmj is represented as the total surface area of ​​the electrode. Expressed as bubble coverage.

5. The anode automatic dye addition management and monitoring system according to claim 4 is characterized by: The judgment unit is used to pre-set a density threshold, and compare and analyze the current density Dmd under the corresponding bubble state with the density threshold to preliminarily judge whether the electrode is polarized. The specific judgment content is as follows: If the current density Dmd in the corresponding bubble state exceeds the density threshold, it will be preliminarily judged that the electrode has a polarization risk and issue a current fluctuation analysis instruction; If the current density Dmd under the corresponding bubble state does not exceed the density threshold, it can be preliminarily judged that there is no polarization risk of the electrode.

6. The anode automatic dye addition management and monitoring system according to claim 3 is characterized by: The prediction and analysis module includes a coating analysis unit, a fluctuation analysis unit and a prediction unit; The coating analysis unit is used to calculate the coating uniformity factor Gbyz in combination with relevant workpiece surface state data after receiving the current fluctuation analysis instruction, which is specifically obtained in the following manner: In the formula, Hdz σ Hdz is the standard deviation of the coating thickness, indicating the degree of fluctuation of the coating thickness; avg Expressed as the average thickness of the coating.

7. The anode automatic dye addition management and monitoring system according to claim 6 is characterized by: The fluctuation analysis unit is used to construct a current fluctuation coefficient Dbxs according to the dynamic change data of the electrolyte solution in the anodizing tank and in combination with the coating uniformity factor Gbyz after linear normalization. The current fluctuation coefficient Dbxs is specifically obtained by the following formula: Wherein, Djpz represents the electrode offset value, Dwd represents the temperature of the electrolyte solution, α and β are weight values, and the specific values ​​of α and β are set by the user according to the situation, and C represents the correction constant.

8. The anode automatic dye addition management and monitoring system according to claim 7 is characterized by: The prediction unit is used to calculate and obtain the dye consumption rate Rhs(t) according to the fluctuation of the current, which is specifically obtained in the following manner: Wherein, Dlz(t) represents the current value at the current moment, a1 and a2 are weight values, DWd(t) represents the current temperature value of the liquid, DWd(t0) represents the standard temperature value, and e represents the Euler number, which is approximately 2.71828.

9. The anode automatic dye addition management and monitoring system according to claim 8, characterized in that: The initial model is constructed using convolutional neural network technology, and the initial model is trained and tested with the information in the tank data set. The trained initial model is used as the state recognition model, and the characteristic information in the state recognition model is obtained respectively. The state recognition model is trained and tested with the obtained characteristic information. Combined with the current fluctuation analysis instruction sent out by the system, the trained state recognition model is used as the prediction model. After training, the prediction model estimates the dye prediction concentration value Rghs(t+Δt), which is obtained in the following way: Where Rghs(t) represents the dye concentration at the current moment, Rhs(t) represents the dye consumption rate, Dbxs represents the current fluctuation coefficient, and Δt represents the time interval.

10. The anode automatic dye addition management and monitoring system according to claim 9, characterized in that: The dye control module compares the dye predicted concentration value Rghs(t+Δt) obtained in the prediction and analysis module with the target concentration range Mnd to determine whether to issue a dye replenishment instruction. The specific content is: If the predicted dye concentration value Rghs(t+Δt) falls within the target concentration range Mnd, no dye replenishment instruction will be issued temporarily; If the predicted dye concentration value Rghs(t+Δt) does not fall within the target concentration range Mnd, a dye replenishment instruction will be issued. According to the dye replenishment instruction, the dye replenishment pump will be automatically started to inject the dye into the anodizing tank according to the dye replenishment amount Rbz, wherein the dye replenishment amount Rbz is obtained by the following formula: Rbz=ΔRghs*Rtj; wherein ΔRghs represents the difference between the predicted dye concentration value and the middle value within the target range, and Rtj represents the total volume of the electrolyte solution in the anodizing tank.

Images