CN119717546B - Automatic coating method of titanium anode and related equipment - Google Patents
Automatic coating method of titanium anode and related equipment Download PDFInfo
- Publication number
- CN119717546B CN119717546B CN202510222156.2A CN202510222156A CN119717546B CN 119717546 B CN119717546 B CN 119717546B CN 202510222156 A CN202510222156 A CN 202510222156A CN 119717546 B CN119717546 B CN 119717546B
- Authority
- CN
- China
- Prior art keywords
- coating
- data
- automatic
- analysis
- feature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Application Of Or Painting With Fluid Materials (AREA)
Abstract
The invention relates to the technical field of titanium anodes, and provides an automatic coating method and related equipment of a titanium anode, wherein the method comprises the steps of obtaining a target coating area of the titanium anode, coating, acquiring data of the target coating area in the coating process, obtaining coating real-time data, and calculating coating uniformity and thickness variation conditions; the method comprises the steps of evaluating coating uniformity to generate a coating quality evaluation result and a coating defect distribution condition, analyzing thickness variation conditions based on the coating quality evaluation result and the coating defect distribution condition to obtain thickness optimization parameters, generating a feedback map according to the thickness optimization parameters, and adjusting coating parameters of automatic coating equipment based on the feedback map. Through automatic coating equipment, sensor arrays, real-time data acquisition and application of a coating analysis model, the problems of unstable coating quality, uneven coating thickness and difficulty in real-time monitoring and adjusting of coating parameters exist when uniformity and thickness in a coating process are improved and controlled.
Description
Technical Field
The application relates to the technical field of titanium anodes, in particular to an automatic coating method of a titanium anode and related equipment.
Background
With the rapid development of the modern industry, the titanium anode is widely applied to a plurality of fields such as electrochemistry, electroplating, water treatment and the like due to the characteristics of excellent corrosion resistance, conductivity, long service life and the like. In order to improve the performance of titanium anodes, it is often necessary to coat the surface thereof. The high-efficiency and uniform coating can not only enhance the service life of the titanium anode, but also improve the working efficiency and effect of the titanium anode.
In the related technical means, the titanium anode coating method is mainly finished by manual or semi-automatic equipment. In the prior art, titanium anodes are generally coated by spraying, brushing, dipping or other methods, which can achieve basic coating effects, but have certain limitations in terms of uniformity and thickness control of the coating. For example, manual operations often rely on the experience and skill of workers, resulting in coating uniformity and thickness control difficulties, while semi-automated equipment can improve production efficiency, but still present difficult coverage problems for complex shapes or areas requiring fine coating.
According to the technical scheme, although basic coating treatment can be carried out on the titanium anode through manual or semi-automatic equipment, the problems of unstable coating quality, uneven coating thickness and difficulty in monitoring and adjusting coating parameters in real time exist when the uniformity and thickness in the coating process are controlled.
Disclosure of Invention
In order to solve the problems of unstable coating quality, uneven coating thickness and difficulty in monitoring and adjusting coating parameters in real time when the uniformity and thickness in the coating process are controlled, the application provides an automatic coating method of a titanium anode and related equipment.
The invention provides an automatic coating method of a titanium anode, which is applied to automatic coating equipment, and comprises the steps of obtaining a target coating area of the titanium anode, and coating the target coating area by using the automatic coating equipment; the method comprises the steps of acquiring data of a target coating area in a coating process according to a preset sensor array on automatic coating equipment to obtain coating real-time data, inputting the coating real-time data into a preset coating analysis model, analyzing coating uniformity and thickness change conditions of the target coating area in the coating process of the automatic coating equipment according to the coating real-time data through the coating analysis model, evaluating the coating uniformity by utilizing a preset neural network algorithm to generate a coating quality evaluation result and coating defect distribution conditions, analyzing the thickness change conditions based on the coating quality evaluation result and the coating defect distribution conditions to obtain thickness optimization parameters, generating a feedback map according to the thickness optimization parameters, adjusting the coating parameters of the automatic coating equipment, and coating the target coating area again by the automatic coating equipment after adjusting the coating parameters.
The method comprises the steps of obtaining a target coating area of a titanium anode, utilizing automatic coating equipment to carry out coating on the target coating area, utilizing laser scanning and a computer vision algorithm to scan the surface of the titanium anode, identifying and marking the target area needing coating to obtain the target coating area, utilizing a finite element analysis technology to carry out first coating simulation on the target coating area to obtain a first coating path and a first coating strategy, utilizing an optimization algorithm to adjust the first coating path to obtain an adjusted coating path, optimizing the first coating strategy according to the adjusted coating path to obtain an optimized first coating strategy, utilizing the finite element analysis technology to carry out second coating simulation on the target coating area based on the optimized first coating strategy to obtain a second coating path and a second coating strategy, comparing the first coating path with the second coating path to obtain a path difference value, comparing the first coating strategy with the second coating strategy to obtain a difference value, utilizing the path difference value to adjust the first coating path to obtain the first coating strategy, utilizing the optimized coating strategy to optimize the first coating strategy, utilizing the optimized coating strategy to carry out final coating control system to carry out final coating strategy, and finally utilizing the optimized coating equipment to carry out final coating system to obtain the final coating strategy.
The method comprises the steps of acquiring data of a target coating area in a coating process according to a preset sensor array on automatic coating equipment to obtain coating real-time data, acquiring data of the target coating area in the coating process by using a preset laser thickness meter, a preset infrared temperature sensor and a preset coating speed sensor on the automatic coating equipment as the sensor array to obtain coating thickness, acquiring data of the target coating area in the coating process by using the laser thickness meter to obtain coating temperature by using the infrared temperature sensor to obtain coating temperature, acquiring data of the target coating area in the coating process by using the coating speed sensor to obtain coating speed, and taking the coating thickness, the coating temperature and the coating speed as the coating real-time data.
The method comprises the steps of inputting the coating real-time data into a preset coating analysis model, analyzing the coating uniformity and thickness change conditions of the target coating area according to the coating real-time data through the coating analysis model, preprocessing the coating real-time data, inputting the preprocessed coating real-time data into the analysis model, extracting features of the coating real-time data in the analysis model by utilizing a convolutional neural network algorithm to obtain feature data, generating a feature map based on the feature data, performing cluster analysis on the feature data according to a statistical analysis method to obtain feature cluster results and a plurality of feature center points, performing multidimensional scale analysis on all the feature center points to obtain feature space coordinates, dividing the feature map by utilizing the feature space coordinates, taking all the feature center points as initial dividing points, marking on the feature map, calculating the distance between the feature center points, establishing a proximity relation in the feature space, dividing the feature map into areas based on the proximity relation and a preset distance threshold, performing cluster analysis on the feature map, calculating the feature uniformity and the average value in the feature area, and performing the coating uniformity analysis in the coating area according to the average value and the average value, and the coating uniformity in the area, and the coating area is evaluated according to the average value and the average value.
The method comprises the steps of inputting data of coating uniformity into a neural network algorithm, evaluating the coating uniformity by using a local sensitivity analysis method in the neural network algorithm to generate evaluation scores and defect characteristic distribution data, classifying the evaluation scores to obtain coating quality grades, comparing the coating quality grades with preset quality standards to obtain coating quality evaluation results, carrying out trend analysis on the coating quality evaluation results to obtain trend analysis results, and carrying out defect positioning on the defect characteristic distribution data by using the trend analysis results to obtain coating defect distribution conditions.
The method comprises the steps of analyzing the thickness variation situation based on the coating quality evaluation result and the coating defect distribution situation to obtain a thickness optimization parameter, wherein the step of analyzing the thickness variation situation based on the coating quality evaluation result and the coating defect distribution situation to obtain a coating quality comprehensive analysis result comprises the steps of comprehensively analyzing the coating quality evaluation result by utilizing a data analysis technology, carrying out feature extraction on the coating defect distribution situation by utilizing a data mining technology to obtain a defect feature extraction result, carrying out fusion analysis on the coating quality comprehensive analysis result and the defect feature extraction result to obtain a fusion analysis result, and optimizing the thickness variation situation based on the fusion analysis result to obtain the thickness optimization parameter.
The method comprises the steps of generating a feedback map according to the thickness optimization parameters, adjusting the coating parameters of automatic coating equipment based on the feedback map, and re-coating the target coating area through the automatic coating equipment after adjusting the coating parameters, wherein the map generation rule comprises comprehensive evaluation standards for coating uniformity, thickness consistency and coating efficiency, inputting the feedback map into a control system of the automatic coating equipment, analyzing and processing the feedback map by using a control algorithm and model predictive control in the control system to obtain a coating parameter adjustment instruction, adjusting the coating parameters of the automatic coating equipment based on the coating parameter adjustment instruction, and re-coating the target coating area through the automatic coating equipment after adjusting the coating parameters, wherein the coating parameters comprise coating speed, coating pressure, coating temperature, spraying angle, spraying distance and flow control of coating materials.
The application further provides an automatic coating device of the titanium anode, which comprises an acquisition module, a second analysis module and an adjustment module, wherein the acquisition module is used for acquiring a target coating area of the titanium anode, the acquisition module is used for carrying out data acquisition on the target coating area in the coating process according to a preset sensor array on the automatic coating equipment to obtain coating real-time data, the first analysis module is used for inputting the coating real-time data into a preset coating analysis model, analyzing the coating uniformity and the thickness change condition of the automatic coating equipment in the coating process of the target coating area according to the coating real-time data through the coating analysis model, the evaluation module is used for evaluating the coating uniformity by utilizing a preset neural network algorithm to generate a coating quality evaluation result and a coating defect distribution condition, the second analysis module is used for analyzing the thickness change condition according to the coating quality evaluation result and the coating defect distribution condition to obtain a thickness optimization parameter, and the adjustment module is used for generating feedback according to the thickness optimization parameter, carrying out automatic adjustment on the coating parameter of the automatic coating equipment, and carrying out adjustment on the coating map of the coating equipment after the automatic adjustment parameter is carried out on the coating map.
The application also provides an electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, the processor implementing the method for automatically coating a titanium anode according to any one of the above when executing the computer program.
The application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform the method of automatic coating of a titanium anode as described in any of the above.
Compared with the prior art, the application has the advantages of stable quality and uniform thickness. Through precision measurement and image recognition technology, the automatic coating equipment can accurately acquire the target coating area of the titanium anode, and real-time data acquisition is carried out by utilizing the sensor array in the coating process, so that the controllability of the coating process is ensured. After the coating real-time data is input into a coating analysis model, coating uniformity and thickness change are analyzed through big data and a machine learning algorithm, and a coating quality evaluation result and a coating defect distribution situation are generated through a neural network algorithm, so that coating parameters are optimized. After the thickness optimization parameters generate a feedback map, the control system adjusts parameters of automatic coating equipment, recoats a target coating area, ensures the uniformity and thickness consistency of final coating, greatly improves the coating efficiency and quality of the titanium anode, and solves the problems of unstable coating quality, uneven coating thickness and difficulty in real-time monitoring and adjustment of coating parameters when the uniformity and thickness in the coating process are controlled.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
The structures, proportions, sizes, etc. shown in the drawings are shown only in connection with the present disclosure, and are not intended to limit the scope of the invention, since any modification, variation in proportions, or adjustment of the size, etc. of the structures, proportions, etc. should be considered as falling within the spirit and scope of the invention, without affecting the effect or achievement of the objective.
FIG. 1 is a schematic flow chart of an automatic coating method for a titanium anode according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of an automatic coating device for a titanium anode according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the present invention.
Reference numerals illustrate:
10. The automatic coating device for the titanium anode comprises 11, an acquisition module, 12, an acquisition module, 13, a first analysis module, 14, an evaluation module, 15, a second analysis module, 16, an adjustment module, 20, electronic equipment, 21, a memory, 22 and a processor.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The technical scheme of the invention is further described below by the specific embodiments with reference to the accompanying drawings.
Example 1:
As shown in fig. 1, the automatic coating method of the titanium anode provided by the embodiment of the application is applied to automatic coating equipment, and the automatic coating method of the titanium anode comprises steps S100 to S600, and specifically comprises the following steps:
and step S100, acquiring a target coating area of the titanium anode, and coating the target coating area by using automatic coating equipment.
In this step, the target coating area of the titanium anode is first determined by precision measurement and image recognition techniques. Specifically, a camera or a laser range finder is used for scanning the surface of the titanium anode to generate a detailed three-dimensional model, and the area to be coated is identified through a software algorithm. These data are input into the control system of the automatic coating apparatus.
For example, the laser range finder is used for scanning the surface of the titanium anode, a generated three-dimensional surface model is used, a software algorithm determines specific areas to be coated on the titanium anode by analyzing model data, and the data of the areas are transmitted to a control system of the automatic coating equipment for coating operation.
Step 200, data acquisition is performed on a target coating area in the coating process according to a sensor array preset on automatic coating equipment, so that coating real-time data are obtained.
In this step, a plurality of sensor arrays on the automated coating apparatus monitor the coating process in real time. In particular, these sensors include temperature sensors, humidity sensors, pressure sensors, and optical sensors for monitoring the coating environment and the coating state, respectively. The sensor array transmits the acquired data to the central processing unit in real time for summarizing and analyzing.
For example, during coating, a temperature sensor monitors the temperature of the coating environment, a humidity sensor records changes in ambient humidity, and an optical sensor measures the thickness and uniformity of the coating by light reflectance, which data is uploaded to a central processing unit in real time to ensure coating quality.
And step S300, inputting the coating real-time data into a preset coating analysis model, and analyzing the coating uniformity and thickness variation conditions of the automatic coating equipment in the coating process of the target coating area according to the coating real-time data through the coating analysis model.
In this step, the coating real-time data is input into a preset coating analysis model, which is built based on big data and machine learning algorithms. Specifically, the analysis model evaluates the coating uniformity and thickness variation by processing and comparing real-time data to generate detailed analysis reports.
For example, the coating analysis model evaluates the uniformity and thickness of the current coating by comparing the historical coating data to a standard model using the input real-time data and generates a real-time report of coating quality, including uniformity index and thickness profile.
And step 400, evaluating the coating uniformity by using a preset neural network algorithm, and generating a coating quality evaluation result and a coating defect distribution condition.
In this step, the coating uniformity is evaluated in detail using a preset neural network algorithm. Specifically, the neural network algorithm generates a coating quality evaluation result and a coating defect distribution map by learning and recognizing various data patterns occurring in the coating process.
For example, the neural network algorithm analyzes the input coating real-time data, identifies minor non-uniformities and defects occurring during the coating process, generates a coating quality assessment report and a defect distribution map, and details the location and type of coating defects.
And S500, analyzing the thickness change condition based on the coating quality evaluation result and the coating defect distribution condition to obtain a thickness optimization parameter.
In this step, the coating thickness is optimally analyzed in combination with the coating quality evaluation result and the coating defect distribution. Specifically, by analyzing the coating defect position and the coating thickness variation, the optimized thickness parameter is calculated, and the uniformity and the thickness consistency of the coating are ensured.
For example, analysis reports that the coating thickness of certain areas is thinner, coating parameters of the areas are adjusted through an optimization algorithm, and new thickness optimization parameters are calculated to ensure the overall uniformity and quality of the coating.
And S600, generating a feedback map according to the thickness optimization parameters, adjusting the coating parameters of the automatic coating equipment based on the feedback map, and coating the target coating area again through the automatic coating equipment after adjusting the coating parameters.
In this step, a feedback map is generated according to the thickness optimization parameters, the feedback map showing in detail the coating parameters to be adjusted and the corresponding target coating areas. Specifically, the control system adjusts parameters such as the coating speed, the coating injection quantity, the moving track and the like of the automatic coating equipment according to the feedback map.
For example, feedback patterns indicate that certain areas require increased coating spray and adjustment of spray rates, and automatic coating equipment re-coats the target coated areas of the titanium anode with accurate coating according to these adjusted parameters, ensuring that the coating uniformity and thickness meet the desired criteria.
In this embodiment, by acquiring a target coating region of the titanium anode, the target coating region is coated with an automatic coating apparatus. In the coating process, a preset sensor array performs data acquisition on a target coating area to obtain coating real-time data. And inputting the coating real-time data into a preset coating analysis model, and analyzing the coating uniformity and thickness variation condition of the automatic coating equipment in the coating process through the coating analysis model. And then, evaluating the coating uniformity by using a preset neural network algorithm to generate a coating quality evaluation result and a coating defect distribution condition. And analyzing the thickness change condition based on the coating quality evaluation result and the coating defect distribution condition to obtain the thickness optimization parameter. And finally, generating a feedback map according to the thickness optimization parameters, adjusting the coating parameters of the automatic coating equipment based on the feedback map, and coating the target coating area again through the automatic coating equipment after adjusting the coating parameters.
The automatic coating equipment and the sensor array are utilized to realize accurate coating of a titanium anode target coating area, uniformity and thickness consistency in a coating process are ensured through real-time data acquisition and a coating analysis model, a preset neural network algorithm further optimizes coating quality and generates detailed coating defect distribution conditions and thickness optimization parameters, so that subsequent coating parameter adjustment is guided, coating efficiency and quality are improved, and the problems of unstable coating quality, uneven coating thickness and difficulty in real-time monitoring and adjustment of coating parameters exist when uniformity and thickness in a coating process are controlled.
Example 2:
In step S100, the surface of the titanium anode is scanned by using a laser scanning and computer vision algorithm, and a target area to be coated is identified and marked, thereby obtaining a target coating area.
The laser scanning system emits laser beams, three-dimensional data of the surface of the titanium anode is calculated through the time and angle of reflected light, and the computer vision algorithm processes the scanned data to identify and mark a target area to be coated. The data of these marked areas is stored in the control system for reference in the subsequent coating process.
For example, the laser scanning equipment is used for carrying out full coverage scanning on the surface of the titanium anode, the generated three-dimensional model is analyzed through an image processing algorithm, the area to be coated is determined, and the three-dimensional model is marked, so that the coating equipment can accurately identify the target area.
And performing first coating simulation on the target coating area by using a finite element analysis technology to obtain a first coating path and a first coating strategy.
The method can predict the behavior and effect of the coating under different conditions by accurately simulating the coating process by utilizing a finite element analysis technology, and particularly, the method can simulate the flowing and solidifying process of the coating on the surface by establishing a finite element model of a target coating area to obtain an optimal coating path and coating strategy and ensure the uniformity and adhesive force of the coating.
For example, finite element analysis software is used for coating simulation on a target area, and the distribution situation of the coating under different temperature, pressure and speed conditions is simulated to obtain a first coating path and strategy, wherein the parameters comprise a spraying angle, a spraying speed, a coating amount and the like.
And adjusting the first coating path by using an optimization algorithm to obtain an adjusted coating path, and optimizing the first coating strategy according to the adjusted coating path to obtain an optimized first coating strategy.
The initial coating path and strategy are adjusted by applying an optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) to further improve the coating efficiency and quality, and specifically, the optimization algorithm performs iterative optimization on the first simulation result and adjusts the nodes and strategy parameters of the coating path to minimize coating defects and improve coating uniformity.
For example, the first coating path is optimized using a genetic algorithm, and by continually iterating and selecting the optimal path, an adjusted optimal coating path and an optimized coating strategy are ultimately obtained, including an adjusted spray angle and a coating flow rate.
Performing second coating simulation on the target coating area by utilizing a finite element analysis technology based on the optimized first coating strategy to obtain a second coating path and a second coating strategy, comparing the first coating path with the second coating path to obtain a path difference value, and comparing the first coating strategy with the second coating strategy to obtain a strategy difference value.
And specifically, the difference of the paths and the strategies is calculated by comparing the paths and the strategies simulated twice, the optimization effect is analyzed, and the reliability and the effectiveness of the coating paths and the strategies are ensured.
For example, in the second simulation, it was found that some areas were unevenly coated, and by comparing the first and second coating paths, the path difference was calculated, further optimizing the spray angle and the amount of coating, ensuring uniformity and consistency of the final coating effect.
And optimizing the optimized first coating strategy by utilizing the strategy difference value to obtain a final coating strategy.
The method comprises the steps of determining a path difference value, a strategy difference value, a coating path and a strategy, further optimizing the coating path and the strategy by combining the path difference value and the strategy difference value to ensure the optimal final coating effect, specifically, adjusting the spraying parameters and the path nodes according to the difference value, eliminating the non-uniformity and the defects in the coating process, and finally determining the optimal coating path and strategy.
For example, the coating speed and the coating trajectory are adjusted by using the path difference, the coating flow and the coating angle are adjusted by using the strategy difference, and the optimal coating path and strategy are finally determined to ensure the coating uniformity and quality.
The final coating path and the final coating strategy are input into a control system of the automatic coating device, and the automatic coating device is controlled by the control system to coat the target coating area.
The automatic coating equipment can precisely coat the target area according to the preset path and strategy by inputting the optimized coating path and strategy into the control system of the automatic coating equipment, and particularly, the control system adjusts the spraying parameters such as the coating speed, the coating pressure and the spraying angle according to the input path and strategy to ensure the uniformity and the adhesive force of the coating.
For example, after the control system receives the final coating path and strategy, the position and the spraying speed of the nozzle of the spraying equipment are automatically adjusted, and accurate coating is started to be carried out on the target coating area, so that the thickness and the uniformity of the coating meet preset requirements.
In step S200, a laser thickness gauge preset on the automatic coating apparatus, an infrared temperature sensor preset, and a coating speed sensor preset are used as the sensor array.
The laser thickness gauge is used for measuring the thickness of the coating, the infrared temperature sensor is used for monitoring the temperature change of a coating area, the coating speed sensor is used for recording the spraying speed, and the data of all the sensors are synchronously transmitted to the control system.
For example, a laser thickness gauge is installed beside the spray head, measuring the coating layer thickness in real time, an infrared temperature sensor is installed above the spray area, monitoring the temperature change during the coating process, and a coating speed sensor is installed on the moving part of the coating apparatus, recording the spray speed.
And acquiring data of a target coating area in the coating process by using a laser thickness gauge to obtain the thickness of the coating layer.
The laser thickness meter emits laser beams to the surface of the coating layer, the thickness of the coating layer is calculated through the time and intensity change of reflected light, and the data are transmitted to the control system for analysis and adjustment in real time.
For example, a laser thickness gauge collects thousands of thickness data points per second, generating a real-time map of coating layer thickness, ensuring that the spray parameters are adjusted at any time during the coating process to achieve the desired thickness.
And acquiring data of a target coating area in the coating process by using an infrared temperature sensor to obtain the coating temperature.
The infrared temperature sensor is used for measuring the temperature change of the coating area, so that the coating process is ensured to be carried out within a proper temperature range, specifically, the infrared temperature sensor is used for carrying out non-contact temperature measurement on the coating area, and data are transmitted to the control system in real time for adjusting coating parameters so as to prevent uneven coating curing.
For example, an infrared temperature sensor collects temperature data every second and compares the temperature data with a preset temperature range, and if the temperature exceeds the range, a control system automatically adjusts the spraying speed or the spraying angle to ensure that the coating process is performed in the optimal temperature range.
And acquiring data of a target coating area in the coating process by using a coating speed sensor to obtain the coating speed.
The method comprises the steps of monitoring the spraying speed through a coating speed sensor, and ensuring the stability and uniformity of a coating process, wherein the coating speed sensor records the moving speed of a spraying head and the spraying speed of the coating, and data are transmitted to a control system in real time for dynamically adjusting the spraying parameters so as to ensure the quality of the coating.
For example, the coating speed sensor monitors the coating speed in real time, and if the speed fluctuation exceeds a set range, the control system can immediately adjust the motion parameters of the coating equipment to ensure the stability and uniformity of the coating process.
Coating layer thickness, coating temperature and coating speed were taken as coating real-time data.
The quality of the coating process is comprehensively evaluated by taking the three key parameters as coating real-time data, and particularly, the control system gathers and analyzes the real-time data, so that each stage of the coating process is ensured to be in an optimal parameter range, and the coating strategy is adjusted in real time.
For example, the control system receives thousands of data points per second, comprehensively analyzes the coating layer thickness, temperature and speed data, generates a real-time coating quality report, and ensures the continuous stability of the coating effect.
In step S300, the coating real-time data is preprocessed, the preprocessed coating real-time data is input into an analysis model, the characteristic extraction is performed on the coating real-time data in the analysis model by using a convolutional neural network algorithm, the characteristic data is obtained, and a characteristic map is generated based on the characteristic data.
The method comprises the steps of preprocessing coating real-time data, removing noise and abnormal values, and ensuring the accuracy and consistency of the data, specifically, processing the original data by utilizing a filtering algorithm and a data cleaning technology to obtain clean coating real-time data, and then inputting the clean coating real-time data into a convolutional neural network for feature extraction to generate a feature map for analysis.
For example, in the preprocessing process, the filtering algorithm removes high-frequency noise in the sensor data, the data cleaning technology removes abnormal values, and the convolutional neural network performs multi-level feature extraction on the processed data to generate a feature map reflecting the quality of the coating layer.
And carrying out cluster analysis on the feature data according to a statistical analysis method to obtain feature cluster results and a plurality of feature center points.
The characteristic data is clustered by utilizing a statistical analysis method to identify modes and trends in the data, specifically, the characteristic data is grouped by adopting a K-means clustering algorithm or a DBSCAN algorithm to obtain a characteristic clustering result, and a characteristic center point is determined based on the characteristic clustering result to reflect key parameter changes in the coating process.
For example, the characteristic data is divided into groups using a K-means clustering algorithm, each group representing a particular coating condition, and the characteristic center point identifies the center position of each group of data, reflecting the average coating state of the group.
And carrying out multidimensional scale analysis on all the feature center points to obtain feature space coordinates, dividing a feature map by using the feature space coordinates, marking all the feature center points serving as initial dividing points on the feature map, calculating the distance between the feature center points, and establishing an adjacent relation in the feature space.
The method comprises the steps of mapping high-dimensional data into a low-dimensional space through multidimensional scale analysis, and facilitating segmentation and analysis of feature patterns, specifically, converting the high-dimensional data of feature center points into low-dimensional coordinates through an MDS algorithm, taking the low-dimensional coordinates as feature space coordinates, marking the feature space coordinates on the feature patterns to segment the feature patterns, and finally calculating the distance between the center points to determine the adjacent relation between the data points.
For example, the high-dimensional data of the feature center points are reduced to a two-dimensional space through an MDS algorithm, a two-dimensional representation of the feature map is formed, each center point is marked, the distance between adjacent center points is calculated, and a proximity relation matrix is established.
Based on the proximity relation and a preset distance threshold, the feature map is divided into a plurality of areas, and each area comprises feature data.
The characteristic spectrum is divided into a plurality of areas by utilizing the proximity relation and the distance threshold value, and each area represents a coating state, and specifically, the characteristic spectrum is divided according to the proximity relation matrix and the preset distance threshold value, so that the data in each area has similar characteristics.
For example, a distance threshold is set, points with distances between feature center points smaller than the threshold are divided into one region, and finally a plurality of regions representing different coating states are formed.
And carrying out statistical analysis on the characteristic data in each region, calculating standard deviation and mean value of the characteristic values in the region, and evaluating the coating uniformity in the region according to the standard deviation and the mean value to obtain the overall coating uniformity of the target coating region.
And specifically, calculating the standard deviation and the average value of the characteristic data in each region to evaluate the coating uniformity in the region, determining the overall coating quality and obtaining the overall coating uniformity of the target coating region.
For example, the standard deviation and the mean value of each region were calculated, the smaller the standard deviation was indicative of the more uniform coating, the mean value reflected the average thickness of the coating layer, and the overall coating uniformity was evaluated by integrating these data.
Modeling the characteristic clustering result and the coating uniformity according to a multiple regression analysis method to obtain the thickness variation condition.
And specifically, constructing a mathematical model between the characteristic data and the coating uniformity by using a multiple regression analysis method, and predicting the thickness variation condition under different coating conditions.
For example, a model is established by utilizing multiple regression analysis, the characteristic clustering result is taken as an independent variable, the coating uniformity is taken as a dependent variable, and the model outputs the thickness variation condition of the coating layer in different areas.
In step S400, the data of the coating uniformity is input into a neural network algorithm, and the coating uniformity is evaluated by using a local sensitivity analysis method in the neural network algorithm, so as to generate evaluation scores and defect characteristic distribution data.
The coating quality is evaluated by inputting the coating uniformity data into a neural network algorithm, and specifically, the change of the coating uniformity data under different conditions is analyzed by using a local sensitivity analysis method to generate evaluation scores and defect distribution data so as to identify problems in the coating process.
For example, the coating uniformity data is evaluated using a convolutional neural network, an evaluation score is generated for each region, a local sensitivity analysis identifies possible defect locations during the coating process, and a defect signature profile is generated.
And classifying the evaluation scores to obtain coating quality grades.
The coating quality grade is determined by classifying the evaluation scores, specifically, the evaluation scores are classified into different grades according to the preset quality standard, the coating quality is evaluated, and the coating process is ensured to meet the quality requirement.
For example, the evaluation scores are classified into class a (good), class B (acceptable) and class C (improvement) according to preset criteria, and a coating quality class report is generated reflecting the coating quality of different areas.
And comparing the coating quality grade with a preset quality standard to obtain a coating quality evaluation result.
And specifically, according to the comparison result, determining whether the coating quality meets the expected requirement, generating a detailed evaluation report, and guiding the subsequent process adjustment.
For example, comparing the coating quality grade to a standard quality requirement, generating an assessment report indicating the area to be improved and an improvement recommendation, ensuring that the coating quality meets the intended objective.
And carrying out trend analysis on the coating quality evaluation result to obtain a trend analysis result, and carrying out defect positioning on the defect characteristic distribution data by utilizing the trend analysis result to obtain a coating defect distribution condition.
And specifically, the trend analysis technology is utilized to analyze the time sequence of the evaluation result, identify the abnormal trend occurring in the coating process and position the defect.
Quality fluctuations occurring during certain time periods of the coating process are identified, for example, by trend analysis, and these fluctuations are combined with defect distribution data to determine the specific location and type of defects, creating a coating defect profile.
In step S500, the coating quality evaluation result is comprehensively analyzed by using a data analysis technique, so as to obtain a coating quality comprehensive analysis result.
And specifically, the evaluation result is comprehensively processed by utilizing a statistical analysis and data mining technology to generate an analysis report of the overall coating quality.
For example, statistical software is used to perform multidimensional analysis on the evaluation results, identify major problems in the coating process, generate comprehensive analysis reports, and provide data support for process improvement.
And carrying out feature extraction on the coating defect distribution condition by utilizing a data mining technology to obtain a defect feature extraction result.
The method comprises the steps of carrying out deep analysis on coating defect distribution data through a data mining technology, extracting key features, specifically, identifying patterns and rules in defect distribution by utilizing technologies such as cluster analysis, association rule mining and the like, and generating feature extraction results.
For example, the defect data are grouped by using cluster analysis, the distribution rule of different types of defects is identified, a defect characteristic extraction report is generated, and the types, positions and frequencies of the defects are described in detail.
And carrying out fusion analysis on the coating quality comprehensive analysis result and the defect feature extraction result to obtain a fusion analysis result.
And specifically, combining the quality analysis result with defect characteristic data, identifying key problems in the coating process, and proposing improvement suggestions.
For example, fusion analysis shows that certain defects are associated with specific process parameters, and by adjusting these parameters, coating quality can be significantly improved, generating detailed improvement advice reports.
And optimizing the thickness change condition based on the fusion analysis result to obtain a thickness optimization parameter.
And optimizing the thickness variation by fusing the analysis result to determine the optimal thickness control parameter, specifically, adjusting the process parameter by utilizing an optimization algorithm to ensure the consistency and uniformity of the thickness of the coating layer.
For example, genetic algorithms are used to optimize the thickness control parameters, ensure uniformity of thickness under different coating conditions, generate optimized parameter reports, and guide process adjustments.
In step S600, a feedback spectrum is constructed according to the thickness optimization parameters and preset spectrum generation rules, wherein the spectrum generation rules comprise comprehensive evaluation standards for coating uniformity, thickness consistency and coating efficiency.
And constructing a feedback map for control by optimizing parameters and generating rules, wherein the thickness optimization parameters comprise the thickness range of a target coating layer, the allowable error range in the coating process, the optimized coating path and the like, and the parameters are basic data for constructing the feedback map. The profile generation rule is composed of a plurality of coating evaluation criteria including a coating uniformity criterion, which is to evaluate the uniformity of the coating layer in the target coating region, including calculating standard deviation and mean of the coating layer thickness. And (5) evaluating thickness consistency of the coating layers at different positions to ensure that the thicknesses of all parts accord with preset optimization parameters. Coating efficiency criteria the speed and efficiency of the coating process, including the area of coating completed per unit time and the quality of coating, were assessed.
And designing a data structure of the feedback map according to the thickness optimization parameters and the map generation rules. The feedback profile is typically represented in a two-dimensional or three-dimensional grid format, wherein each cell represents a portion of the target coating area, and each cell contains data of current position coordinates, actual coating thickness, theoretical coating thickness, coating uniformity evaluation, coating efficiency evaluation, and data population, with coating real-time data, the actual measured coating thickness and other relevant data being populated into the individual cells of the feedback profile. Meanwhile, calculating theoretical coating thickness, coating uniformity evaluation value and coating efficiency evaluation value of each cell by using the thickness optimization parameters and preset standards, and filling corresponding data. Based on the filled data, the feedback map is converted into a visual image, typically in the form of a heat map, a contour map, or a 3D surface map. In the visual image, different colors and heights represent different coating thickness and uniformity evaluation results, so that operators can intuitively see the coating quality and the area to be adjusted, and a feedback map is generated to guide the real-time adjustment of the coating process by using the thickness optimization parameters and the coating evaluation standard.
For example, based on the optimized parameters, a feedback map is generated, including information on coating uniformity, thickness uniformity, and efficiency, to provide a real-time adjustment reference for the coating apparatus.
And inputting the feedback pattern into a control system of the automatic coating equipment, and analyzing and processing the feedback pattern by using a control algorithm and model predictive control in the control system to obtain a coating parameter adjustment instruction.
The coating parameters are adjusted in real time by inputting the feedback pattern into a control system, specifically, the feedback pattern is analyzed by using a model predictive control and feedback control algorithm to generate an adjustment instruction, and the coating process is optimized.
For example, the control system automatically adjusts parameters such as spraying speed, pressure, temperature and the like according to the feedback map, so as to ensure the stability and high efficiency of the coating process.
And adjusting the coating parameters of the automatic coating equipment based on the coating parameter adjustment instruction, and re-coating the target coating area by the automatic coating equipment after adjusting the coating parameters, wherein the coating parameters comprise coating speed, coating pressure, coating temperature, spraying angle, spraying distance and flow control of coating materials.
The coating quality is optimized by adjusting the coating parameters in real time, specifically, each parameter of the coating equipment is reset according to the adjusting instruction, the precise control of the coating process is ensured, and the coating quality is improved.
For example, the automatic coating equipment adjusts the spraying angle and distance according to the instruction of the control system, controls the material flow, ensures the thickness uniformity of the coating layer, and improves the coating effect.
In this embodiment, the target coating area is identified and marked by scanning the titanium anode surface using laser scanning and computer vision algorithms, ensuring coating accuracy. And performing first coating simulation by using a finite element analysis technology to obtain a first coating path and strategy. And the path and the strategy are adjusted through an optimization algorithm, so that the coating precision and quality are improved. And carrying out finite element analysis again to obtain a second coating path and strategy, and comparing and optimizing to ensure the accuracy and consistency of the coating process. The final coating path and strategy are entered into the control system of the automated coating apparatus to ensure high quality coating.
Example 3:
As shown in fig. 2, the present application further provides an automatic coating apparatus 10 for a titanium anode, where the automatic coating apparatus 10 for a titanium anode includes an acquisition module 11, an acquisition module 12, a first analysis module 13, an evaluation module 14, a second analysis module 15, and an adjustment module 16.
The acquisition module 11 is mainly used for acquiring a target coating area of the titanium anode, and coating the target coating area by using automatic coating equipment.
The acquisition module 12 is mainly used for acquiring data of a target coating area in a coating process according to a sensor array preset on automatic coating equipment, so as to obtain coating real-time data.
The first analysis module 13 is mainly used for inputting the coating real-time data into a preset coating analysis model, and analyzing the coating uniformity and thickness variation conditions of the coating process of the automatic coating equipment on the target coating area according to the coating real-time data through the coating analysis model.
The evaluation module 14 is mainly used for evaluating the coating uniformity by using a preset neural network algorithm, and generating a coating quality evaluation result and a coating defect distribution situation.
The second analysis module 15 is mainly used for analyzing the thickness variation condition based on the coating quality evaluation result and the coating defect distribution condition to obtain a thickness optimization parameter.
The adjustment module 16 is mainly configured to generate a feedback map according to the thickness optimization parameter, adjust a coating parameter of the automatic coating device based on the feedback map, and re-coat the target coating area through the automatic coating device after adjusting the coating parameter.
In the embodiment, the accurate control of the titanium anode coating process is realized through the cooperative work of the modules. The acquisition module 11 first accurately identifies and coats the target coating area using an automatic coating apparatus, ensuring the initial accuracy of coating. The acquisition module 12 monitors the coating process in real time using the sensor array, and collects key data such as coating layer thickness, temperature and speed to form coating real time data. The first analysis module 13 performs a depth analysis on the real-time data through the coating analysis model, evaluates the coating uniformity and thickness variation, and provides real-time feedback of the coating process. The evaluation module 14 further uses a neural network algorithm to evaluate the coating uniformity in detail, and generates a coating quality evaluation result and a defect distribution situation, so as to ensure a high standard of the coating quality. The second analysis module 15 analyzes the thickness variation condition based on the evaluation result and the defect distribution, proposes a thickness optimization parameter, and optimizes the coating effect. Finally, the adjustment module 16 generates a feedback map according to the optimization parameters, adjusts the coating parameters of the automatic coating equipment, realizes the self-adaptive optimization of the coating process, and improves the overall coating quality and efficiency.
It should be noted that, for convenience and brevity of description, the specific working process of the apparatus and each module described above may refer to a corresponding process in the foregoing embodiment of the automatic coating method for a titanium anode, which is not described herein again.
Example 4:
as shown in fig. 3, the present application further provides an electronic device 20, including a memory 21 and a processor 22, where the memory 21 stores a computer program that can be run on the processor 22, and the processor 22 implements the automatic coating method of the titanium anode of embodiment 1 when executing the computer program.
In the present embodiment, efficient execution of the titanium anode automatic coating method is ensured by the coordinated operation of the memory 21 and the processor 22 of the electronic device 20. The computer program stored in the memory 21 comprises the complete steps of acquiring the target coating area, acquiring coating real-time data, analyzing coating uniformity and thickness variations, evaluating coating quality, analyzing thickness optimization parameters, and adjusting coating equipment parameters. The processor 22, when executing the program, first identifies and marks the target coating area, ensuring coating accuracy. The coating process is then monitored in real time by the sensor array, and the coating real time data is collected and processed. The program further analyzes the data, evaluates the coating quality, generates optimization parameters, and adjusts the operating parameters of the coating apparatus in real time to ensure dynamic optimization of the coating process. In this way, the electronic device 20 achieves full automation and high precision control of the titanium anode coating process, significantly improving coating efficiency and quality.
Example 5:
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform the automatic coating method of a titanium anode as in embodiment 1.
In the present embodiment, the processor can efficiently execute the automatic coating method of the titanium anode by a computer program on a computer-readable storage medium. The program stored in the storage medium comprises the steps of acquiring a target coating area, data acquisition, analysis and evaluation, optimizing thickness parameters, and adjusting the coating apparatus. When the processor runs the program, the target coating area is precisely acquired first, and preliminary coating is performed. Then, coating data are acquired in real time through the sensor array, and are input into a coating analysis model to analyze coating uniformity and thickness variation conditions. The program evaluates the coating quality using a neural network algorithm, generating defect distribution conditions and optimization parameters. And generating a feedback map and adjusting parameters of the coating equipment according to the optimized parameters by the program, so as to ensure that the optimal effect can be achieved for each coating. In this way, the computer readable storage medium provides powerful technical support for achieving the intellectualization and the high efficiency of the titanium anode coating process.
While the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit and scope of the embodiments of the invention.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510222156.2A CN119717546B (en) | 2025-02-27 | 2025-02-27 | Automatic coating method of titanium anode and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510222156.2A CN119717546B (en) | 2025-02-27 | 2025-02-27 | Automatic coating method of titanium anode and related equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119717546A CN119717546A (en) | 2025-03-28 |
| CN119717546B true CN119717546B (en) | 2025-05-09 |
Family
ID=95088183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510222156.2A Active CN119717546B (en) | 2025-02-27 | 2025-02-27 | Automatic coating method of titanium anode and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119717546B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120366748A (en) * | 2025-04-11 | 2025-07-25 | 东莞市龙驰模具配件有限公司 | Preparation method of rear mold insert composite coating |
| CN120243400B (en) * | 2025-06-04 | 2025-08-05 | 上海艾录包装股份有限公司 | Coating device adjusting method and coating device for coating processing |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109499796A (en) * | 2018-12-20 | 2019-03-22 | 广州帆智能自动化设备有限公司 | A kind of coat thickness detection device and its paint finishing and spray painting control method |
| CN113592845A (en) * | 2021-08-10 | 2021-11-02 | 深圳市华汉伟业科技有限公司 | Defect detection method and device for battery coating and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8042361B2 (en) * | 2004-07-20 | 2011-10-25 | Corning Incorporated | Overflow downdraw glass forming method and apparatus |
| CN118466417B (en) * | 2024-05-14 | 2024-11-01 | 徐州德高电动车科技有限公司 | Self-adaptive adjustment method and system for spraying process based on temperature measurement |
-
2025
- 2025-02-27 CN CN202510222156.2A patent/CN119717546B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109499796A (en) * | 2018-12-20 | 2019-03-22 | 广州帆智能自动化设备有限公司 | A kind of coat thickness detection device and its paint finishing and spray painting control method |
| CN113592845A (en) * | 2021-08-10 | 2021-11-02 | 深圳市华汉伟业科技有限公司 | Defect detection method and device for battery coating and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119717546A (en) | 2025-03-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119717546B (en) | Automatic coating method of titanium anode and related equipment | |
| CN118134062B (en) | Numerical control machine tool casting quality tracking system | |
| CN119272984B (en) | Machine room energy consumption optimization method and system based on temperature monitoring | |
| CN117930750B (en) | Coating machine control method and coating machine system | |
| CN118466417B (en) | Self-adaptive adjustment method and system for spraying process based on temperature measurement | |
| CN118143937B (en) | Control method and system of building spraying robot | |
| CN118456126B (en) | Polishing path optimization method of cladding layer on inner surface of cylindrical barrel based on machine learning | |
| CN116224930B (en) | Processing control method and system for numerically controlled grinder product | |
| CN119159442B (en) | Cutting control method, device and equipment of liquid crystal screen and storage medium | |
| CN117620448B (en) | Processing control method, device and equipment of laser engraving machine and storage medium | |
| CN118607888B (en) | Large-tonnage spherical hinge installation precision control construction method | |
| CN120171051B (en) | Intelligent process optimization and real-time quality monitoring method based on multi-algorithm cooperation and fused deposition modeling | |
| CN117047569A (en) | Tool clamp polishing method and device based on sensor data interaction | |
| CN118657957A (en) | A treatment system for optimizing the wear resistance of hose outer membranes | |
| CN118070671A (en) | A method for predicting the milling accuracy of aeroengine blades based on KDE feature representation | |
| CN118663460A (en) | Technological parameter optimization method and system for flexible three-dimensional paint spraying line | |
| CN118719886B (en) | Intelligent control method and system for computer hydraulic pipe bending machine | |
| CN118839580B (en) | Production method of mold for cold stamping 6G antenna parts | |
| CN118999443A (en) | Full-coverage automatic measuring method for thickness of multi-size wafer | |
| CN117171922B (en) | Method and system for parallel correction in steel structure manufacturing | |
| CN119203421A (en) | A method and system for intelligently generating a process model for sheet metal parts | |
| CN117392182B (en) | Film pasting precision detection method, device, equipment and storage medium | |
| CN119861658B (en) | A dynamic compensation control system and method for high-precision trimming die | |
| CN119444736B (en) | Auxiliary processing quality assessment method, medium and equipment based on three-dimensional point cloud data | |
| CN119973299B (en) | Intelligent tracking method and system for free curve weld joint by mechanical arm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |