Automatic feeding and process adjustment control method for plasma rotating electrode atomization equipment

Abstract

The present invention relates to the field of automation control technology, and more specifically, to an automatic feeding and process adjustment control method for a plasma rotating electrode atomization device, wherein the image data between the plasma torch nozzle and the front end of a workpiece is obtained by an industrial camera; based on the image data, the distance L between the plasma torch nozzle and the front end of the workpiece is calculated by an image processing algorithm; according to the distance L, the workpiece feeding speed adjustment strategy is determined; and the workpiece feeding speed control instruction is output to the equipment controller. The method of the present invention realizes the accurate measurement of the distance between the plasma torch nozzle and the front end of the workpiece by introducing high-precision image acquisition and processing technology. Combined with an intelligent control algorithm, the system can automatically adjust the workpiece feeding speed to maintain the optimal atomization state. This not only greatly improves the stability and consistency of the production process, but also significantly reduces the workload of operators.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and more specifically to an automatic feeding and process adjustment control method for a plasma rotating electrode atomization device. Background Art

[0002] Plasma rotating electrode atomization technology is an important process in the field of metal powder preparation and is widely used in high-end manufacturing fields such as 3D printing and powder metallurgy. Traditional plasma rotating electrode atomization equipment mainly relies on manual observation and control. The operator needs to monitor the distance between the plasma torch nozzle and the front end of the workpiece in real time through the observation window and manually adjust the feed speed of the workpiece. Although this method is intuitive, it has many limitations and problems.

[0003] First, the accuracy and consistency of manual observation are difficult to guarantee. Due to the high brightness and rapidly changing characteristics of the plasma arc, it is difficult for operators to accurately judge the actual distance between the plasma torch nozzle and the front end of the workpiece. There may also be differences in the judgment criteria between different operators, resulting in parameter fluctuations during the production process.

[0004] Secondly, the response speed of manual control is limited. Under high-speed rotation conditions, the melting and atomization process of the workpiece changes rapidly, and manual adjustments are often difficult to keep up with the pace of process changes. This may lead to unstable atomization effects and affect product quality.

[0005] Furthermore, long-term manual monitoring can easily cause operator fatigue and increase the risk of human error. Especially in the continuous production process, maintaining a high level of concentration is a very challenging task.

[0006] In addition, it is difficult to accurately record and trace process parameters using traditional methods. Minor adjustments during manual control are usually not accurately recorded, which makes subsequent process optimization and quality analysis difficult.

[0007] Finally, the traditional methods have a low degree of automation and intelligence, which makes it difficult to meet the needs of modern manufacturing for high-efficiency and high-consistency production. With the increasing application of metal powders in high-end fields such as aerospace and medical devices, the requirements for powder quality and production efficiency are also increasing. Summary of the invention

[0008] In view of the above problems, there is an urgent need for a control method that can realize automatic feeding and intelligent process adjustment to improve the accuracy, consistency and efficiency of the plasma rotating electrode atomization process. The present invention proposes an innovative control method to meet this demand.

[0009] The present invention provides an automatic feeding and process adjustment control method for a plasma rotating electrode atomization device, comprising:

[0010] The acquisition steps include:

[0011] The image data between the plasma torch nozzle and the front end of the workpiece is obtained by an industrial camera;

[0012] Processing steps include:

[0013] Based on the image data, a distance L between the plasma torch nozzle and the front end of the workpiece is calculated by an image processing algorithm;

[0014] According to the distance L, a workpiece feed speed adjustment strategy is determined;

[0015] Output steps include:

[0016] Output workpiece feed speed control instructions to the equipment controller.

[0017] Preferably, the processing step further comprises:

[0018] Analyzing the shape of the front end of the workpiece based on the image data;

[0019] Whether the current atomization state is normal is determined according to the shape of the front end of the workpiece.

[0020] Preferably, the step of determining whether the current atomization state is normal specifically includes:

[0021] Acquire preset standard images of a normal workpiece, an abnormal workpiece 1, and an abnormal workpiece 2;

[0022] Calculating the similarity between the front end shape of the current workpiece and the standard image;

[0023] According to the similarity, the current atomization state is determined.

[0024] As a preference, it also includes:

[0025] When it is determined that the current atomization state is abnormal, an alarm signal or an emergency stop command is generated.

[0026] Preferably, the processing step further comprises:

[0027] Determine whether the distance L is less than a preset safety threshold;

[0028] When the distance L is smaller than the safety threshold, an emergency feed stop instruction is generated.

[0029] Preferably, the workpiece feed speed adjustment strategy includes:

[0030] When the distance L is greater than a preset value, increasing the workpiece feed speed;

[0031] When the distance L is smaller than a preset value, the workpiece feeding speed is reduced.

[0032] Preferably, the method further comprises a plasma torch position adjustment step:

[0033] Based on the distance L and the shape of the front end of the workpiece, a plasma torch position adjustment strategy is determined;

[0034] Outputs plasma torch position adjustment instructions.

[0035] Preferably, the plasma torch position adjustment strategy includes:

[0036] When the shape of the front end of the workpiece deviates from the normal state and the distance L is too large, reduce the distance between the plasma torch and the axis of the workpiece;

[0037] When the shape of the front end of the workpiece deviates from the normal state and the distance L is too small, increase the distance between the plasma torch and the axis of the workpiece.

[0038] Preferably, the method further comprises the step of data storage:

[0039] Storing parameters such as the distance L, workpiece feed speed, plasma torch position, and workpiece front end shape in real time;

[0040] Based on the stored parameters, a batch report is generated.

[0041] Preferably, a self-optimization step is also included:

[0042] Analyze the best process parameter combination based on historical batch data;

[0043] In the subsequent production process, the process parameters are automatically adjusted to approach the optimal process parameter combination.

[0044] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0045] The method of the present invention achieves accurate measurement of the distance between the plasma torch nozzle and the front end of the workpiece by introducing high-precision image acquisition and processing technology. Combined with the intelligent control algorithm, the system can automatically adjust the workpiece feed speed to maintain the optimal atomization state. This not only greatly improves the stability and consistency of the production process, but also significantly reduces the workload of operators.

[0046] The method of the present invention has powerful data collection and analysis capabilities. The system can record and store various process parameters in real time, providing valuable data support for subsequent quality analysis and process optimization. Through in-depth analysis of historical data, the system can also continuously optimize the control strategy and achieve continuous improvement of the process.

[0047] In addition, the method of the present invention also introduces an abnormality detection and safety control mechanism. The system can detect and respond to abnormal situations in a timely manner, greatly improving the safety and reliability of the equipment. This is of great significance for protecting high-value plasma rotating electrode atomization equipment and ensuring production safety.

[0048] In general, the automatic feeding and process adjustment control method provided by the present invention realizes the comprehensive intelligence of the plasma rotating electrode atomization process by integrating advanced sensing, control and data analysis technologies. This not only significantly improves production efficiency and product quality, but also provides strong technical support for process optimization and quality control. The application of this method is expected to promote the development of metal powder preparation technology towards higher precision, higher efficiency and higher intelligence, and make important contributions to the progress of high-end manufacturing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a simplified schematic diagram of the plasma rotating electrode atomization device of the present invention.

[0050] Figure 2 It is a structural schematic diagram of installing an industrial camera at the observation window of the present invention.

[0051] Figure 3 These are images of normal workpieces and abnormal workpieces of the present invention.

[0052] Figure 4 It is the overall flow chart of the present invention.

[0053] Figure 5 This is a flow chart of image processing and distance calculation of the present invention.

[0054] Figure 6 This is a workpiece feed speed adjustment strategy diagram of the present invention.

[0055] Figure 7 This is a flowchart of the abnormality detection and processing of the present invention.

[0056] Figure 8 This is a flow chart of plasma torch position adjustment according to the present invention. DETAILED DESCRIPTION

[0057] Please refer to Figure 1-8 The present invention relates to an automatic feeding and process adjustment control method for a plasma rotating electrode atomization device. The method aims to solve the limitations of manual observation and control in the traditional plasma rotating electrode atomization process and improve production efficiency and product quality stability.

[0058] like Figure 1The figure shows a simplified schematic diagram of a plasma rotating electrode atomization device, which mainly includes a plasma torch, a radial adjustment mechanism of the plasma torch, an atomization tank, a workpiece, a rotating electrode, a feed motor, a machine tool base and an observation window. The plasma torch generates a red plasma flame to melt the workpiece. The workpiece rotates at a high speed driven by the rotating electrode, and the melted liquid at the front end of the workpiece is thrown into the tank to form small droplets, which are then cooled to form spherical powder. The workpiece moves toward the plasma torch driven by the feed motor, and is fed according to the melting speed of the workpiece until it reaches the front end that the workpiece can be fed to complete the atomization processing of the current workpiece, and then the next workpiece is replaced for processing.

[0059] An industrial camera is installed at the observation window of the existing plasma rotating electrode atomization equipment. During the atomization process, the camera takes pictures from the plasma torch nozzle to the front end of the workpiece, such as Figure 2 As shown, the camera photo is transmitted back to the computer, and the computer calculates the distance L from the plasma torch nozzle to the front end of the workpiece through image processing. The computer transmits the distance L to the equipment controller. If L is greater than the set value, the controller will automatically increase the speed of the workpiece feed axis. If L is less than the set value, the controller will automatically reduce the workpiece feed speed, and adjust in real time to keep L within a small range around the set value. Since L of each batch is very similar, the powder batches produced in this way are very consistent. Compared with the current control method, it eliminates the need for manual real-time observation of the distance L to adjust the workpiece feed speed, which greatly improves the personnel cost and performance of the equipment.

[0060] If an unexpected situation occurs and the distance L reaches the minimum safe value, the equipment controller will automatically stop the feed axis, preventing collisions, improving the safety of equipment use, and reducing the concentration requirements on operators.

[0061] like Figure 3 As shown, during the atomization process of the equipment, the front end of the workpiece is similar to the normal workpiece picture under normal circumstances. If an abnormality occurs during the operation of the equipment, the front end of the workpiece may appear close to the abnormal workpiece 1 or abnormal workpiece 2. At this time, it is necessary to prompt the operator to adjust the equipment in time and stop working if necessary. The main reason why the figure is between abnormal workpiece 1 and normal workpiece is that the distance between the plasma torch and the axis of the workpiece is too large, and the plasma flame only melts the material at the edge of the workpiece, and the material in the middle part is atomized and melted. In this case, it is necessary to reduce the distance between the plasma torch and the axis of the workpiece. The main reason why the image is between abnormal work 2 and normal workpiece is that the distance between the plasma torch and the axis of the workpiece is too small or the diameter of the plasma flame is too small. The plasma flame only melts the material in the middle of the workpiece, and the material at the edge is not melted. In this case, it is necessary to increase the distance between the plasma torch and the axis of the workpiece or increase the diameter of the plasma torch flame.

[0062] During the operation of the equipment, the industrial camera captures the front end of the workpiece in real time to obtain a real-time image, and performs image processing and similarity calculation through a computer. If the similarity between the current image and the normal workpiece image is within the set range, the equipment is operating normally. If it exceeds the set range, the equipment will alarm and simultaneously calculate the similarity with the abnormal workpiece 1 image and the abnormal workpiece 2 image, determine which type of abnormality the equipment has, and inform the operator how to adjust the equipment. If the equipment is equipped with an electric plasma torch radial adjustment mechanism, the equipment can automatically adjust the distance between the plasma torch and the workpiece axis to automatically handle abnormal situations online.

[0063] During the operation of the equipment, the operator does not need to observe the plasma torch nozzle and the front end position L of the workpiece in real time to adjust the workpiece feeding speed and equipment abnormalities, which reduces the difficulty of operating the equipment for the operator. At the same time, one person can operate several devices, greatly reducing the cost of personnel use.

[0064] The radial adjustment mechanism of the plasma torch can be used to adjust the process during the operation of the equipment, so that the equipment can automatically adjust to the best parameters.

[0065] Specifically, the method provided by the present invention comprises the following steps:

[0066] First, in the acquisition step, image data between the plasma torch nozzle and the front end of the workpiece is acquired by an industrial camera. Preferably, the industrial camera can be a high-speed camera with an acquisition rate of at least 60 frames per second to ensure that the instantaneous state of the atomization process is captured. In one embodiment of the present invention, the resolution of the camera is not less than 1920x1080 pixels to ensure image clarity.

[0067] Next, in the processing step, based on the acquired image data, the distance L between the plasma torch nozzle and the front end of the workpiece is calculated by an image processing algorithm. This method uses an improved edge detection algorithm to achieve accurate measurement. Specifically, the image is first preprocessed, including grayscale and noise reduction, and then the Canny edge detection algorithm is applied to extract the contours of the plasma torch nozzle and the front end of the workpiece. Finally, the L value is obtained by calculating the shortest distance between the two contours. The distance calculation formula is as follows:

[0068]

[0069] Where, L is the distance between the plasma torch nozzle and the front end of the workpiece, x i and i is the coordinate of the point on the plasma torch nozzle contour, x j and j is the coordinate of the point on the front contour of the workpiece.

[0070] According to the calculated distance L, the method further determines the workpiece feed speed adjustment strategy. Preferably, a fuzzy PID control algorithm is used to achieve adaptive speed regulation. The control rules of the algorithm are as follows:

[0071]

[0072] Among them, Δv is the speed adjustment, e is the deviation between the distance L and the target distance, K p , K i and K d They are proportional, integral and differential coefficients respectively. These coefficients are dynamically adjusted through fuzzy rules to adapt to different working conditions.

[0073] Finally, in the output step, the method outputs the workpiece feed speed control instruction to the equipment controller. The instruction is transmitted to the servo drive via industrial Ethernet or field bus to achieve precise control of the feed motor.

[0074] Furthermore, the method of the present invention also includes analyzing the shape of the front end of the workpiece. This step uses deep learning technology, specifically a pre-trained convolutional neural network (CNN) model to extract the features of the front end of the workpiece. The structure of the CNN model includes 5 convolutional layers and 3 fully connected layers, the input is a 224x224 pixel workpiece front end image, and the output is a 256-dimensional feature vector.

[0075] Based on the extracted features, this method determines whether the current atomization state is normal. The judgment process uses the support vector machine (SVM) classifier to divide the front shape of the workpiece into three states: normal, abnormal 1, and abnormal 2. The decision function of the SVM classifier is as follows:

[0076]

[0077] Among them, x is the feature vector to be classified, x i is the support vector, y i is the category label, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, and this method uses the radial basis function (RBF) kernel. b is the bias term.

[0078] In order to further improve the accuracy of classification, an ensemble learning method is introduced in a preferred embodiment of the present invention. Specifically, a random forest algorithm is used to vote on the results of multiple decision trees to obtain the final classification result. This method can effectively reduce the risk of overfitting and improve the generalization ability of the model.

[0079] A significant advantage of the method of the present invention is that it realizes fully automated control of the atomization process, greatly reducing the errors caused by human operation. Through real-time image analysis and intelligent algorithms, the system can quickly respond to changes in process parameters and maintain the optimal atomization state. This not only improves production efficiency, but also significantly improves the consistency of product quality.

[0080] On the other hand, the abnormality detection function of this method greatly improves the safety and reliability of the equipment. By timely discovering and handling abnormal situations, equipment damage and safety accidents can be effectively prevented. This has important economic significance for high-value plasma rotating electrode atomization equipment.

[0081] In addition, the adaptive characteristics of the method of the present invention enable it to be applied to different types of materials and process parameters. The system can dynamically adjust the control strategy according to actual conditions without frequent manual intervention and parameter adjustment. This greatly improves the versatility and flexibility of the equipment and provides technical support for multi-variety and small-batch production.

[0082] In general, the automatic feeding and process adjustment control method of the plasma rotating electrode atomization equipment provided by the present invention realizes the precise control and abnormal monitoring of the atomization process through advanced image processing and intelligent control algorithms. It not only improves production efficiency and product quality, but also enhances the safety and flexibility of the equipment, representing an important advancement in the technology in this field.

[0083] An important feature of the present invention is the abnormal situation handling mechanism. When the system determines that the current atomization state is abnormal, it will automatically generate an alarm signal or an emergency stop command. The realization of this function depends on the aforementioned workpiece front end shape analysis results. Preferably, the method adopts a multi-level early warning mechanism and takes corresponding measures according to the degree of abnormality.

[0084] Specifically, when a minor anomaly is detected, the system will first sound an audible and visual alarm to alert the operator. If the anomaly persists or intensifies, the system will automatically adjust process parameters, such as reducing the feed speed or adjusting the plasma torch position, to try to correct the abnormal state. In extreme cases, if a serious anomaly is detected that may cause equipment damage or safety hazards, the system will immediately trigger an emergency shutdown procedure.

[0085] The method of the present invention also includes a key safety control mechanism. The system will continuously determine whether the distance L between the plasma torch nozzle and the front end of the workpiece is less than a preset safety threshold. The setting of this safety threshold is determined based on a large amount of experimental data and expert experience. In a preferred embodiment of the present invention, the safety threshold is set to 5mm. This value takes into account the characteristics of the plasma arc and the melting speed of the workpiece material, which can not only ensure a good atomization effect, but also effectively prevent the collision between the plasma torch and the workpiece.

[0086] When it is detected that the distance L is less than the safety threshold, the method will immediately generate an emergency stop feed command. This command is directly sent to the servo drive of the feed system through a high priority channel to ensure that the movement of the workpiece is stopped in the shortest time. At the same time, the system will also trigger a rapid shutdown procedure of the plasma power supply to prevent the workpiece from overheating or melting and deformation.

[0087] Another innovation of the present invention is its adaptive workpiece feed speed adjustment strategy. The strategy dynamically adjusts the feed speed based on the real-time measured distance L to maintain the optimal atomization state. Specifically, when the distance L is greater than a preset value, the system increases the workpiece feed speed; when the distance L is less than the preset value, the feed speed is reduced.

[0088] In one embodiment of the present invention, the preset target distance is set to 10 mm. This value is obtained through a large number of experimental optimizations and can achieve the best atomization effect under most working conditions. The feed speed is adjusted using a proportional-integral-differential (PID) control algorithm, and its mathematical model is as follows:

[0089]

[0090] Where, v(t) is the adjusted feed speed, v 0 is the initial feed speed, e(t) is the distance deviation (i.e. the difference between the actual distance and the target distance), K p , K i and K d They are proportional, integral and differential coefficients respectively. These parameters can be automatically adjusted according to different workpiece materials and atomization process requirements through adaptive algorithms to obtain the best control effect.

[0091] The method of the present invention also includes an automatic adjustment function of the plasma torch position. The realization of this function is based on a comprehensive analysis of the distance L and the shape of the front end of the workpiece. The system will determine the plasma torch position adjustment strategy based on this information and output the corresponding adjustment instructions.

[0092] In a preferred embodiment of the present invention, the plasma torch position adjustment adopts a fuzzy control algorithm. The algorithm uses the distance L and the deviation of the front shape of the workpiece as input variables and outputs the moving direction and amplitude of the plasma torch. The design of fuzzy rules is based on expert experience and a large amount of experimental data, and can effectively handle various complex atomization conditions.

[0093] For example, when it is detected that the shape of the front end of the workpiece deviates from the normal state and the distance L is too large, the system will instruct to reduce the distance between the plasma torch and the axis of the workpiece. Conversely, if the distance L is too small, the distance between the plasma torch and the axis of the workpiece will be increased. This dynamic adjustment ensures that the atomization process is always carried out in the best state, greatly improving the consistency of product quality.

[0094] In general, the automatic feeding and process adjustment control method of the plasma rotating electrode atomization equipment provided by the present invention realizes all-round intelligent control of the atomization process by comprehensively applying advanced technologies such as image processing, machine learning, and adaptive control. This method not only significantly improves production efficiency and product quality, but also greatly enhances the safety and reliability of the equipment, representing an important development direction of plasma rotating electrode atomization technology.

[0095] An important feature of the present invention is the fine adjustment strategy of the plasma torch position. In practical applications, a small change in the position of the plasma torch may have a significant impact on the atomization effect. Therefore, the present method adopts a set of complex adjustment strategies to ensure that the optimal atomization state is always maintained.

[0096] Specifically, when the shape of the front end of the workpiece deviates from the normal state and the distance L is too large, the system will instruct to reduce the distance between the plasma torch and the axis of the workpiece. This adjustment is based on the understanding that too large a distance usually means that the energy density of the plasma arc is insufficient and cannot effectively melt the workpiece material. By reducing the distance, the energy input can be increased to promote sufficient melting and atomization of the material.

[0097] In a preferred embodiment of the present invention, the plasma torch position adjustment adopts an adaptive stepping algorithm. The core idea of ​​the algorithm is to dynamically adjust the step size according to the size of the deviation. For example, when a large deviation is detected, the system will use a larger adjustment step size to quickly approach the target position; and when approaching the target position, a smaller step size is used to avoid over-adjustment. This method ensures both the speed of adjustment and the accuracy of the final position.

[0098] On the contrary, when the shape of the front end of the workpiece deviates from the normal state and the distance L is too small, the system will increase the distance between the plasma torch and the axis of the workpiece. In this case, too small a distance may cause the workpiece to overheat or even cause unnecessary melting. By increasing the distance, the energy input can be appropriately reduced, making the atomization process more stable and controllable.

[0099] It is worth noting that the adjustment of the plasma torch position is not a simple linear process. The method of the present invention takes into account multiple factors, including the thermophysical properties of the workpiece material, the characteristics of the plasma arc, and environmental conditions. The system comprehensively analyzes these factors through a complex mathematical model to derive the optimal adjustment strategy.

[0100] For example, for materials with higher thermal conductivity, the system may choose a slightly larger plasma torch distance to prevent overheating of the workpiece, while for materials with higher melting points, a smaller distance may be required to ensure sufficient melting. This intelligent adjustment strategy greatly improves the adaptability and flexibility of the system, enabling it to cope with a variety of different process requirements.

[0101] Another innovative feature of the present invention is its comprehensive data management and analysis capabilities. The system stores various parameters including distance L, workpiece feed speed, plasma torch position, workpiece front end shape, etc. in real time. These data are not only used for real-time control, but also provide valuable information for subsequent process optimization and quality analysis.

[0102] In one embodiment of the present invention, data storage uses a distributed database system. This architecture can support high-speed data writing and complex query operations, meeting the dual needs of real-time control and offline analysis. Each record in the database contains complete timestamp information, which is convenient for tracing and analyzing process parameters in a specific time period.

[0103] Based on the stored parameters, the system can automatically generate detailed batch reports. These reports contain not only conventional statistical information, such as average feed speed, average distance, etc., but also more in-depth analysis results, such as the volatility of process parameters, the frequency of abnormal events, etc. This information is of great value for production management and quality control.

[0104] The method of the present invention also has a self-optimization function, which is an important manifestation of its intelligence. The system will analyze the best process parameter combination based on historical batch data. This process involves complex data mining and machine learning techniques.

[0105] Specifically, this method uses an optimization algorithm based on deep reinforcement learning. The algorithm models the atomization process as a Markov decision process, where the state space includes key parameters such as distance L and workpiece front shape, and the action space includes control instructions such as feed speed adjustment and plasma torch position adjustment. The system continuously optimizes its policy network through continuous interaction with the environment and eventually learns the optimal control strategy.

[0106] The optimization objective function takes into account multiple factors, including product quality (such as powder particle size distribution, sphericity, etc.), production efficiency, energy consumption, etc. These factors are considered in a weighted manner, and the weights can be adjusted according to specific production needs. For example, for the production of special alloy powders that require high quality, the weight of quality factors can be increased; while for mass production, more attention may be paid to efficiency and cost.

[0107] In the subsequent production process, the system will automatically adjust the process parameters to approach the optimal parameter combination obtained through analysis. This adjustment is gradual, and the system will continuously monitor the adjustment effect and fine-tune the optimization strategy according to the actual situation. This self-optimization mechanism enables the system to continuously improve its performance and adapt to changing production needs and environmental conditions.

[0108] In general, the automatic feeding and process adjustment control method of the plasma rotating electrode atomization equipment provided by the present invention realizes a highly intelligent and automated atomization process through fine plasma torch position adjustment, comprehensive data management and analysis, and intelligent self-optimization functions. This method not only greatly improves production efficiency and product quality, but also provides strong technical support for process optimization and quality control, and represents the future development direction of metal powder preparation technology.

[0109] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Automatic feeding and process adjustment control method of plasma rotating electrode atomization equipment, characterized in that: include: The acquisition steps include: The image data between the plasma torch nozzle and the front end of the workpiece is obtained by an industrial camera; Processing steps include: Based on the image data, a distance L between the plasma torch nozzle and the front end of the workpiece is calculated by an image processing algorithm; According to the distance L, a workpiece feed speed adjustment strategy is determined; Output steps include: Output workpiece feed speed control instructions to the equipment controller.

2. The method according to claim 1, characterized in that The processing steps also include: Analyzing the shape of the front end of the workpiece based on the image data; Whether the current atomization state is normal is determined according to the shape of the front end of the workpiece.

3. The method according to claim 2, characterized in that The step of determining whether the current atomization state is normal specifically includes: Acquire preset standard images of a normal workpiece, an abnormal workpiece 1, and an abnormal workpiece 2; Calculating the similarity between the front end shape of the current workpiece and the standard image; According to the similarity, the current atomization state is determined.

4. The method according to claim 3, characterized in that Also includes: When it is determined that the current atomization state is abnormal, an alarm signal or an emergency stop command is generated.

5. The method according to claim 1, characterized in that The processing steps also include: Determine whether the distance L is less than a preset safety threshold; When the distance L is smaller than the safety threshold, an emergency feed stop instruction is generated.

6. The method according to claim 1, characterized in that The workpiece feed speed adjustment strategy includes: When the distance L is greater than a preset value, increasing the workpiece feed speed; When the distance L is smaller than a preset value, the workpiece feeding speed is reduced.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes the plasma torch position adjustment steps: Based on the distance L and the shape of the front end of the workpiece, a plasma torch position adjustment strategy is determined; Outputs plasma torch position adjustment instructions.

8. The method according to claim 7, characterized in that The plasma torch position adjustment strategy includes: When the shape of the front end of the workpiece deviates from the normal state and the distance L is too large, reduce the distance between the plasma torch and the axis of the workpiece; When the shape of the front end of the workpiece deviates from the normal state and the distance L is too small, increase the distance between the plasma torch and the axis of the workpiece.

9. The method according to claim 8, characterized in that It also includes the data storage step: Storing parameters such as the distance L, workpiece feed speed, plasma torch position, and workpiece front end shape in real time; Based on the stored parameters, a batch report is generated.

10. The method according to claim 9, characterized in that Also includes a self-optimization step: Analyze the best process parameter combination based on historical batch data; In the subsequent production process, the process parameters are automatically adjusted to approach the optimal process parameter combination.

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