Multi-dimensional cutting precision control system applied to laser pipe cutting machine

Abstract

The invention discloses a multi-dimensional cutting precision control system applied to a laser pipe cutting machine, and relates to the technical field of laser cutting control, and the multi-dimensional cutting precision control system comprises a data acquisition module, a prediction module, an actual verification module, a judgment module and a response module; the data acquisition module is used for acquiring cutting track data of the initial laser pipe cutting machine, including but not limited to cutting speed, cutting depth, cutting angle and cutting path; a sensor is installed at the corresponding position of the laser pipe cutting machine; according to the laser pipe cutting machine, through the integrated data acquisition module, key parameters such as the cutting speed, depth, angle and path of the laser pipe cutting machine in the cutting process can be monitored in real time, the control precision of the cutting process is remarkably improved, and therefore the consistency and reliability of the cutting quality are ensured; the system can more accurately predict and compensate deviation possibly occurring in the cutting process, and the rejection rate caused by parameter fluctuation is reduced.

Description

Technical Field

[0001] The present invention relates to the field of laser cutting control technology, and in particular to a multi-dimensional cutting precision control system applied to a laser tube cutting machine. Background Art

[0002] In modern industrial production, laser cutting technology is widely used in the processing of various materials due to its high precision, high efficiency and flexibility, especially in the field of metal pipe processing.

[0003] Laser tube cutting machines use high-energy laser beams to cut tubes, which can achieve precise processing of complex shapes. However, the accuracy of the cutting process is affected by many factors, including cutting speed, depth, angle and path.

[0004] Traditional laser tube cutting machines may have unstable precision during the cutting process, resulting in fluctuations in cutting quality, affecting product consistency and reliability. In addition, traditional control systems often lack the ability to monitor and adjust in real time, making it difficult to adapt to changing cutting conditions and material properties, thus limiting the improvement of production efficiency and product quality.

[0005] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0006] The purpose of the present invention is to solve the problems of unstable precision and limited production efficiency in the cutting process of traditional laser tube cutting machines, and to propose a multi-dimensional cutting precision control system applied to laser tube cutting machines.

[0007] The purpose of the present invention can be achieved through the following technical solutions: Multi-dimensional cutting precision control system applied to laser tube cutting machine, including: Data acquisition module, used to collect cutting trajectory data of the initial laser tube cutting machine; The prediction module is used to analyze the collected initial cutting trajectory data through machine learning algorithms to predict the deviations that may occur in the subsequent cutting process; An actual verification module is used to calibrate the actual cut portion of the workpiece with the preset cutting standard of the workpiece by using an image processing method; A judgment module is used to judge whether the current cutting trajectory meets the expected accuracy requirements based on the prediction results; The response module is used to make corresponding controls according to the judgment results and different signals, including parameter correction or emergency stop.

[0008] Furthermore, the specific process of the prediction module predicting the deviation occurring in the subsequent cutting process is as follows: Collect historical cutting data, including speed, depth, angle and path, and clean, standardize and normalize them; Extract key features from the data, including velocity change rate and depth fluctuations, and the identification of important features is determined by algorithms; Select the corresponding machine learning model based on the data characteristics, train it with historical data, use cross-validation to optimize the model parameters, use the validation set to evaluate the model's prediction accuracy, and adjust the model based on performance; Integrate the trained model into the prediction module, process new cutting data in real time, use the model to make real-time predictions of the cutting process, and identify deviations from the preset standard after prediction; Analyze the prediction results and set thresholds to determine whether it is necessary to adjust the cutting parameters or take emergency measures. When the deviation exceeds the preset threshold, a correction or emergency stop signal is issued.

[0009] Furthermore, the specific operation steps of the actual verification module to calibrate the actual cutting part of the workpiece with the predicted cutting standard of the workpiece by using an image processing method are as follows: The camera installed on the laser tube cutting machine captures the image of the cutting process, and the position of the camera is selected so that it covers the entire cutting area; Capture the image of the workpiece regularly or in real time during the cutting process to ensure that the frequency of image acquisition matches the cutting speed to accurately reflect the cutting status; Preprocess the acquired images, including denoising, contrast enhancement, and grayscale conversion, to improve the accuracy of subsequent image analysis, and perform image correction, including perspective transformation, to ensure that the image is consistent with the size of the actual workpiece; Extract features related to cutting quality from the preprocessed image, including the straightness of the cutting edge, the smoothness of the cutting surface, and the cutting depth. Use image processing techniques such as edge detection and grayscale analysis to extract features related to cutting quality. Using image analysis algorithms, the extracted features are analyzed and the analysis results are compared with the preset cutting standards to determine whether there is a deviation; The results of the image analysis are compared with the prediction results of the prediction module to verify whether the accuracy of the prediction meets the requirements.

[0010] Furthermore, the specific operation steps of the actual verification module to verify whether the prediction accuracy meets the standard are as follows: Comprehensive analysis is performed by obtaining deviation parameters, which include: Cutting straightness: Measures the deviation between the cutting edge and the preset standard straight line, which is quantified by the edge detection algorithm in image analysis and recorded as the straight deviation value; Cutting verticality: Evaluate the verticality between the cutting surface and the pipe axis. Use image processing technology to measure the angular deviation between the cutting surface and the vertical line and record it as the angular deviation value. Cutting depth: The depth estimation algorithm in image analysis is used to measure the depth of laser penetration into the pipe, and the difference between the depth and the preset depth standard is calculated and recorded as the depth difference; Cutting width consistency: The cutting width consistency on the entire cutting path is evaluated by measuring the distance between the cutting edges in the image, and the deviation between the cutting width consistency and the predicted cutting width consistency is calculated and quantified by percentage, which is recorded as a deviation value; The obtained straight deviation value, angular deviation value, depth difference value and one deviation value are normalized and then input into the following formula: To get the difference judgment value CJJ, where PZ, PJ, CS and YZ are the straight deviation value, angle deviation value, depth difference value and one deviation value respectively. They are respectively the preset weight coefficients of straight deviation value, angular deviation value, depth difference value and one deviation value; The calculated difference judgment value CJJ is then compared with the preset difference judgment threshold. When the difference judgment value CJJ is greater than the preset difference judgment threshold, it is judged that the prediction accuracy does not meet the standard, and optimization is performed.

[0011] Furthermore, the specific operation steps of the actual verification module for prediction optimization are as follows: First, conduct a detailed analysis of the errors to determine the types and sources of errors, including systematic errors, random errors, or errors caused by external factors; Get more data or different features to retrain the prediction model to improve prediction accuracy and calibrate the laser tube cutting machine regularly ， Ensure alignment and functionality of cutting heads, sensors and actuators, inspect and maintain equipment; According to the error analysis results, the cutting parameters are adjusted, including laser power, cutting speed, focus position and auxiliary gas flow rate, and an adaptive control strategy is implemented to automatically adjust the cutting parameters according to real-time feedback; Increase monitoring points and increase the frequency of measuring and monitoring parameters during the cutting process to a preset degree.

[0012] Furthermore, the specific operation steps of the judgment module for judging whether the current cutting trajectory meets the expected accuracy requirements are as follows: Determine the cutting quality criteria, including cutting width, verticality, roughness, burr height, and the cutting quality criteria are fixed or dynamically adjusted according to the workpiece material and type; Obtain prediction results from the prediction module, including the expected cutting deviation, cutting quality score or accuracy index, set a threshold for each accuracy index, and if the threshold is exceeded, it is determined that the predicted cutting trajectory does not meet the requirements; Compare the prediction results with the preset accuracy standards, and for each indicator, determine whether the predicted cutting quality is within the preset error range; When all indicators are within the threshold range, the current cutting trajectory is judged to meet the accuracy requirements. When any indicator exceeds the threshold, it is judged to not meet the accuracy requirements, and the indicator that does not meet the requirements is recorded; Output the judgment result, specifically qualified or unqualified. When the judgment result is unqualified, analyze the unqualified indicators; When there is only one unqualified indicator, the ratio of the part of the unqualified indicator that exceeds the preset standard is analyzed, which is recorded as the threshold-exceeding ratio. The threshold-exceeding ratio is compared with the preset corresponding threshold-exceeding threshold. When it exceeds the preset threshold-exceeding threshold, an emergency stop signal is generated. When the threshold-exceeding ratio is less than the preset threshold-exceeding threshold, a correction signal is generated. When there is more than one unqualified indicator, the threshold ratio of each unqualified indicator is calculated, and the comprehensive excess value is obtained by weighted summation. The obtained comprehensive excess value is also compared with the preset corresponding comprehensive excess threshold. When it exceeds the preset comprehensive excess threshold, an emergency stop signal is generated. When the comprehensive excess value is less than the preset comprehensive excess value, a correction signal is generated.

[0013] Furthermore, the specific operation steps of the response module to make corresponding controls according to the judgment results and different signaling are as follows: After receiving the correction signal, the deviation data is analyzed to determine the cutting parameters that need to be adjusted; Calculate correction values ​​and apply control algorithms to make adjustments; Test the correction effect in a simulated environment to ensure that the correction value is within the equipment performance range; Send control commands to the cutting head and laser source control systems to implement corrections; Monitor the corrected cutting results in real time, record all correction operation data, and inform the operator of the correction measures and effects through the user interface; When receiving the emergency stop signal, the laser source is immediately cut off and the cutting head stops moving; Automatically performs equipment checks and diagnostics, and locks the control system to prevent restart; Notify the operator of an emergency stop through an audible and visual alarm and user interface, record the details of the emergency stop event, and notify the operator or maintenance personnel to conduct an on-site safety inspection; After troubleshooting, perform a system reset and test verification to ensure that the equipment safely returns to normal operation, conduct subsequent analysis, automatically write an incident report, and take preventive measures to avoid recurrence of the incident.

[0014] Furthermore, the specific process of the data acquisition module collecting the cutting trajectory data of the initial laser tube cutting machine is as follows: Install sensors on the pipe cutter to measure cutting speed, depth, angle and path; set up data interfaces and transmission protocols to ensure effective communication between the sensors and the system; Configure the data acquisition system, set the sampling frequency, data format and storage parameters, and collect data from all sensors simultaneously; Filter, denoise and normalize the raw data, and store the processed data in hard disk, solid-state drive or cloud storage; synchronize data regularly to ensure real-time and consistency; Test module performance during actual cutting to ensure that data accuracy and real-time performance meet system requirements.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention, through the integrated data acquisition module, can monitor the key parameters of the laser tube cutting machine in the cutting process, such as cutting speed, depth, angle and path, in real time, significantly improving the control accuracy of the cutting process, thereby ensuring the consistency and reliability of the cutting quality. Compared with the traditional laser tube cutting machine, the system can more accurately predict and compensate for the deviations that may occur in the cutting process, reducing the scrap rate caused by parameter fluctuations; In the present invention, the prediction module uses a machine learning algorithm to conduct an in-depth analysis of the collected data, and can predict the deviations that may occur in the subsequent cutting process and adjust the cutting parameters in time. This prediction and correction mechanism enables the system to not only cope with the current cutting task, but also adapt to different material properties and cutting conditions, thereby improving the adaptability and flexibility of the system. The actual verification module uses image processing technology to calibrate the cutting results, further ensuring the cutting accuracy, so that the finished cutting product can meet strict industrial standards. In the present invention, the response module can intelligently execute parameter correction or emergency stop command according to the output result of the judgment module, effectively avoiding production accidents caused by operation errors or equipment failures. This automatic response mechanism significantly improves production safety, reduces downtime, and improves production efficiency. At the same time, the user interface of the system provides intuitive operation and monitoring means, so that operators can easily manage the cutting process, reduce the difficulty of operation, and improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0019] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0020] like Figure 1 As shown, the multi-dimensional cutting precision control system applied to the laser tube cutting machine includes a data acquisition module, a prediction module, an actual verification module, a judgment module and a response module; The data acquisition module is used to collect the cutting trajectory data of the initial laser tube cutting machine, including but not limited to cutting speed, cutting depth, cutting angle and cutting path; Install sensors at corresponding parts of the laser tube cutting machine, including: Cutting speed sensor: used to measure the speed of the cutting head; cutting depth sensor: used to measure the depth of the laser penetrating the pipe; cutting angle sensor: used to measure the angle of the cutting head relative to the pipe; cutting path sensor: used to track the movement path of the cutting head on the pipe; Configure the data interface to enable the installed sensors to communicate with the system, and determine the corresponding data transmission protocol, such as serial communication interface (RS-232, RS-485), industrial Ethernet or wireless communication technology; configure the data acquisition system, including sampling frequency, data format and storage parameters, and determine the accuracy and resolution of data acquisition to meet the needs of subsequent analysis; start data acquisition, collect data in real time through different sensors, and synchronously collect data from all sensors for subsequent analysis; Preprocess the collected raw data, including filtering, denoising and normalization, to ensure the accuracy and reliability of the data and provide accurate input for subsequent predictions and judgments; store the preprocessed data in appropriate storage media, such as hard disks, solid-state drives or cloud storage services, to ensure the security and accessibility of the data for subsequent analysis and backtracking; synchronize the data regularly to make the data real-time and consistent, and update the data in real time during the cutting process to capture any changes in the cutting process; test the performance of the data acquisition module during the actual cutting process to ensure its stability and accuracy, and verify whether the data acquisition module can meet the system's requirements for data accuracy and real-time performance.

[0021] The prediction module is used to analyze the collected initial cutting trajectory data through machine learning algorithms to predict possible deviations in the subsequent cutting process; Collect historical cutting data, including cutting speed, cutting depth, cutting angle and cutting path, and preprocess the collected historical cutting data, including cleaning, standardization and normalization, to improve the accuracy and efficiency of the algorithm; select features useful for the prediction model from the collected data, such as the rate of change of cutting speed, fluctuation of cutting depth, etc., and use feature selection algorithms such as recursive feature elimination (RFE) or model-based feature selection to determine the most important features; Select a suitable machine learning model, such as random forest, support vector machine (SVM), neural network or deep learning model, based on the nature of the problem. Consider the complexity, training time and prediction performance of the model to select the model that best suits the current task. Use historical cutting data to train the selected machine learning model and adjust the model parameters through methods such as cross-validation to prevent overfitting and underfitting. Use an independent validation set to evaluate the trained model to verify the model's predictive performance. According to the model's performance on the validation set, further adjust the model parameters or select a different model. Deploy the trained model to the prediction module so that it can receive new cutting trajectory data in real time and make predictions, ensuring that the model can respond quickly to facilitate timely adjustment of the cutting process. Receive data provided by the data acquisition module in real time and use the trained model to make predictions. The prediction results include possible deviation size, location and type. Analyze the prediction results to determine whether the cutting process needs to be adjusted, and preset a threshold. When the predicted deviation exceeds the threshold, send a correction signal or an emergency stop signal to the response module.

[0022] The actual verification module is used to calibrate the actual cutting part of the workpiece with the predicted cutting standard of the workpiece by using an image processing method; The camera installed on the laser tube cutting machine is used to capture images of the cutting process. The position selection needs to ensure that the camera covers the entire cutting area. During the cutting process, the image of the workpiece is captured regularly or in real time to ensure that the frequency of image acquisition matches the cutting speed so that the cutting status can be accurately reflected. The captured images are pre-processed, including denoising, contrast enhancement, grayscale, etc., to improve the accuracy of subsequent image analysis. Image correction, such as perspective transformation, is also required to ensure that the image is consistent with the size of the actual workpiece. Extract features related to cutting quality from the preprocessed image, such as the straightness of the cutting edge, the smoothness of the cutting surface, the cutting depth, etc., and use image processing technologies such as edge detection and grayscale analysis to extract these features; use image analysis algorithms, such as machine learning models and deep network learning models, to analyze the extracted features, compare the analysis results with the preset cutting standards, and determine whether there is a deviation; compare the image analysis results with the prediction results of the prediction module to verify whether the prediction accuracy meets the standard, and conduct a comprehensive analysis by obtaining deviation parameters. The deviation parameters include: Cutting straightness: measure the deviation between the cutting edge and the preset standard straight line, which is quantified by the edge detection algorithm in image analysis and recorded as the straight deviation value, which is used as the standard for measuring the deviation of cutting straightness; Cutting verticality: evaluate the perpendicularity of the cutting surface to the axis of the pipe, measure the angular deviation between the cutting surface and the vertical line by image processing technology, and record it as the angular deviation value, which is used as the standard for measuring the deviation of cutting verticality; Cutting depth: measure the depth of laser penetration into the pipe by the depth estimation algorithm in image analysis, and calculate the difference with the preset depth standard, which is recorded as the depth difference value, which is used as the standard for measuring the deviation of cutting depth; Cutting width consistency: evaluate the consistency of cutting width on the entire cutting path by measuring the distance between the cutting edges in the image, and calculate the deviation between the cutting width consistency and the predicted cutting width consistency, and quantify it by percentage, which is recorded as a deviation value, which is used as the standard for measuring the deviation of cutting width consistency; The obtained straight deviation value, angular deviation value, depth difference value and one deviation value are normalized and then input into the following formula: To get the difference judgment value CJJ, where PZ, PJ, CS and YZ are the straight deviation value, angle deviation value, depth difference value and one deviation value respectively. are preset weight coefficients for straight deviation, angular deviation, depth difference and first deviation respectively; the calculated difference judgment value CJJ is then compared with the preset difference judgment threshold. When the difference judgment value CJJ is greater than the preset difference judgment threshold, it is judged that the prediction accuracy does not meet the standard, and prediction optimization is performed. The specific process is as follows: First, conduct a detailed analysis of the errors to determine the types and sources of errors, including systematic errors, random errors, or errors caused by specific factors (such as equipment vibration, material inhomogeneity); obtain more data or different features to retrain the prediction model to improve the prediction accuracy, regularly calibrate the laser tube cutting machine to ensure the precise alignment and function of the cutting head, sensors, and actuators, and inspect and maintain the equipment, such as replacing worn parts to ensure that the equipment operates in the best condition; according to the error analysis results, adjust the cutting parameters, such as laser power, cutting speed, focus position, and auxiliary gas flow, implement an adaptive control strategy, and automatically adjust the cutting parameters based on real-time feedback; increase monitoring points to measure and monitor key parameters in the cutting process more frequently.

[0023] The judgment module is used to judge whether the current cutting trajectory meets the expected accuracy requirements based on the prediction results; Determine the criteria for cutting quality, which may include cutting width, verticality, roughness, burr height, etc. These criteria can be fixed or dynamically adjusted according to the workpiece material and type; obtain the prediction results from the prediction module, which may include the expected cutting deviation, cutting quality score or other relevant indicators, and set thresholds for each accuracy indicator. Exceeding these thresholds means that the predicted cutting trajectory may not meet the requirements; Compare the prediction results with the preset accuracy standards, and for each indicator, determine whether the predicted cutting quality is within the acceptable error range; if all indicators are within the threshold range, the current cutting trajectory is judged to meet the accuracy requirements; if any indicator exceeds the threshold, it is judged to not meet the accuracy requirements, and the indicator that does not meet the requirements is recorded; Output the judgment result, which is usually a binary decision (such as qualified / unqualified). When the judgment result is unqualified, the unqualified indicator is specifically analyzed. When there is only one unqualified indicator, the proportion of the part of the unqualified indicator that exceeds the preset standard is determined and recorded as the threshold-exceeding ratio. The threshold-exceeding ratio is compared with the preset corresponding threshold-exceeding threshold. When it exceeds the preset threshold-exceeding threshold, an emergency stop signal is generated. When the threshold-exceeding ratio is less than the preset threshold-exceeding threshold, a correction signal is generated. When there is more than one unqualified indicator, the threshold ratio of each unqualified indicator is calculated, and the comprehensive excess value is obtained by weighted summation. The obtained comprehensive excess value is also compared with the preset corresponding comprehensive excess threshold. When it exceeds the preset comprehensive excess threshold, an emergency stop signal is generated. When the comprehensive excess value is less than the preset comprehensive excess value, a correction signal is generated.

[0024] The response module is used to make corresponding controls according to the judgment results and different signals, such as correction or emergency stop; When receiving the correction signal, analyze the deviation data provided by the prediction module to determine which parameters (such as speed, depth, angle) need to be adjusted, and determine the adjustment direction (increase or decrease) and expected adjustment range of each parameter based on the deviation data and the preset accuracy standard; use the preset correction algorithm or model to calculate the specific correction value, including proportional control, integral control or differential control; Before implementing the correction, test the correction effect in a simulated environment to predict the cutting quality after the correction, ensure that the correction value is within the normal working range of the equipment and does not exceed the performance limit of the laser tube cutting machine; convert the calculated correction value into a control command and send it to the control system of the cutting head and the laser source. After the correction is implemented, monitor the cutting result in real time to ensure that the correction effect is in line with expectations; record the data of all correction operations, including the deviation before correction, the calculated correction value, the implemented correction measures and the effect after correction, and inform the operator through the user interface that the correction measures have been executed and the effect of the correction; When receiving the emergency stop signal, send a shutdown command to the laser source control system, immediately cut off the laser source, stop laser emission, and send a stop command to the cutting head motion control system to stop the cutting head from moving immediately; automatically execute the post-shutdown inspection procedure, including automatic diagnosis of the equipment to determine the cause of the emergency stop, lock the control system, and prevent the equipment from being restarted before the problem is resolved; notify the operator of the emergency stop through the sound and light alarm and user interface, and provide deviation information, record detailed information of the emergency stop event, including time, cause, system status, etc., for subsequent analysis, and notify the operator or maintenance personnel to conduct on-site safety inspections to confirm that there are no safety risks; After troubleshooting, execute the system reset procedure and prepare to restart the equipment. Before restarting, perform necessary test verification to ensure that the equipment returns to normal working condition; conduct follow-up analysis of the emergency stop event, write an accident report, and take preventive measures to avoid similar incidents from happening again.

[0025] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The multi-dimensional cutting precision control system applied to the laser tube cutting machine is characterized by: include: Data acquisition module, used to collect cutting trajectory data of the initial laser tube cutting machine; The prediction module is used to analyze the collected initial cutting trajectory data through machine learning algorithms to predict the deviations that may occur in the subsequent cutting process; An actual verification module is used to calibrate the actual cut portion of the workpiece with the preset cutting standard of the workpiece by using an image processing method; A judgment module is used to judge whether the current cutting trajectory meets the expected accuracy requirements based on the prediction results; The response module is used to make corresponding controls according to the judgment results and different signals, including parameter correction or emergency stop.

2. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 1, characterized in that: The specific process of the prediction module predicting the deviations that occur in the subsequent cutting process is as follows: Collect historical cutting data, including speed, depth, angle and path, and clean, standardize and normalize them; Extract key features from the data, including velocity change rate and depth fluctuations, and the identification of important features is determined by algorithms; Select the corresponding machine learning model based on the data characteristics, train it with historical data, use cross-validation to optimize the model parameters, use the validation set to evaluate the model's prediction accuracy, and adjust the model based on performance; Integrate the trained model into the prediction module, process new cutting data in real time, use the model to make real-time predictions of the cutting process, and identify deviations from the preset standard after prediction; Analyze the prediction results and set thresholds to determine whether it is necessary to adjust the cutting parameters or take emergency measures. When the deviation exceeds the preset threshold, a correction or emergency stop signal is issued.

3. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 1 is characterized in that: The specific operation steps of the actual verification module to calibrate the actual cutting part of the workpiece with the predicted cutting standard of the workpiece by using the image processing method are as follows: The camera installed on the laser tube cutting machine captures the image of the cutting process, and the position of the camera is selected so that it covers the entire cutting area; Capture the image of the workpiece regularly or in real time during the cutting process to ensure that the frequency of image acquisition matches the cutting speed to accurately reflect the cutting status; Preprocess the acquired images, including denoising, contrast enhancement, and grayscale conversion, to improve the accuracy of subsequent image analysis, and perform image correction, including perspective transformation, to ensure that the image is consistent with the size of the actual workpiece; Extract features related to cutting quality from the preprocessed image, including the straightness of the cutting edge, the smoothness of the cutting surface, and the cutting depth. Use image processing techniques such as edge detection and grayscale analysis to extract features related to cutting quality. Using image analysis algorithms, the extracted features are analyzed and the analysis results are compared with the preset cutting standards to determine whether there is a deviation; The results of the image analysis are compared with the prediction results of the prediction module to verify whether the accuracy of the prediction meets the requirements.

4. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 3 is characterized in that: The specific operation steps of the actual verification module to verify whether the prediction accuracy meets the standard are as follows: Comprehensive analysis is performed by obtaining deviation parameters, which include: Cutting straightness: Measures the deviation between the cutting edge and the preset standard straight line, which is quantified by the edge detection algorithm in image analysis and recorded as the straight deviation value; Cutting verticality: Evaluate the verticality between the cutting surface and the pipe axis. Use image processing technology to measure the angular deviation between the cutting surface and the vertical line and record it as the angular deviation value. Cutting depth: The depth estimation algorithm in image analysis is used to measure the depth of laser penetration into the pipe, and the difference between the depth and the preset depth standard is calculated and recorded as the depth difference; Cutting width consistency: The cutting width consistency on the entire cutting path is evaluated by measuring the distance between the cutting edges in the image, and the deviation between the cutting width consistency and the predicted cutting width consistency is calculated and quantified by percentage, which is recorded as a deviation value; The obtained straight deviation value, angular deviation value, depth difference value and one deviation value are normalized and then input into the following formula: To get the difference judgment value CJJ, where PZ, PJ, CS and YZ are the straight deviation value, angle deviation value, depth difference value and one deviation value respectively. They are respectively the preset weight coefficients of straight deviation value, angular deviation value, depth difference value and one deviation value; The calculated difference judgment value CJJ is then compared with the preset difference judgment threshold. When the difference judgment value CJJ is greater than the preset difference judgment threshold, it is judged that the prediction accuracy does not meet the standard, and prediction optimization is performed.

5. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 4, characterized in that: The specific operation steps of the actual verification module for predictive optimization are as follows: First, conduct a detailed analysis of the errors to determine the types and sources of errors, including systematic errors, random errors, or errors caused by external factors; Get more data or different features to retrain the prediction model to improve prediction accuracy and calibrate the laser tube cutting machine regularly ， Ensure alignment and functionality of cutting heads, sensors and actuators, inspect and maintain equipment; According to the error analysis results, the cutting parameters are adjusted, including laser power, cutting speed, focus position and auxiliary gas flow rate, and an adaptive control strategy is implemented to automatically adjust the cutting parameters according to real-time feedback; Increase monitoring points and increase the frequency of measuring and monitoring parameters during the cutting process to a preset degree.

6. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 1, characterized in that: The specific operation steps of the judgment module to judge whether the current cutting trajectory meets the expected accuracy requirements are as follows: Determine the cutting quality criteria, including cutting width, verticality, roughness, burr height, and the cutting quality criteria are fixed or dynamically adjusted according to the workpiece material and type; Obtain prediction results from the prediction module, including the expected cutting deviation, cutting quality score or accuracy index, set a threshold for each accuracy index, and if the threshold is exceeded, it is determined that the predicted cutting trajectory does not meet the requirements; Compare the prediction results with the preset accuracy standards, and for each indicator, determine whether the predicted cutting quality is within the preset error range; When all indicators are within the threshold range, the current cutting trajectory is judged to meet the accuracy requirements. When any indicator exceeds the threshold, it is judged to not meet the accuracy requirements, and the indicator that does not meet the requirements is recorded; Output the judgment result, specifically qualified or unqualified. When the judgment result is unqualified, analyze the unqualified indicators; When there is only one unqualified indicator, the ratio of the part of the unqualified indicator that exceeds the preset standard is analyzed, which is recorded as the threshold-exceeding ratio. The threshold-exceeding ratio is compared with the preset corresponding threshold-exceeding threshold. When it exceeds the preset threshold-exceeding threshold, an emergency stop signal is generated. When the threshold-exceeding ratio is less than the preset threshold-exceeding threshold, a correction signal is generated. When there is more than one unqualified indicator, the threshold ratio of each unqualified indicator is calculated, and the comprehensive excess value is obtained by weighted summation. The obtained comprehensive excess value is also compared with the preset corresponding comprehensive excess threshold. When it exceeds the preset comprehensive excess threshold, an emergency stop signal is generated. When the comprehensive excess value is less than the preset comprehensive excess value, a correction signal is generated.

7. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 1, characterized in that: The specific operation steps of the response module to make corresponding control according to the judgment results and different signaling are as follows: After receiving the correction signal, the deviation data is analyzed to determine the cutting parameters that need to be adjusted; Calculate correction values ​​and apply control algorithms to make adjustments; Test the correction effect in a simulated environment to ensure that the correction value is within the equipment performance range; Send control commands to the cutting head and laser source control systems to implement corrections; Monitor the corrected cutting results in real time, record all correction operation data, and inform the operator of the correction measures and effects through the user interface; When receiving the emergency stop signal, the laser source is immediately cut off and the cutting head stops moving; Automatically performs equipment checks and diagnostics, and locks the control system to prevent restart; Notify the operator of an emergency stop through an audible and visual alarm and user interface, record the details of the emergency stop event, and notify the operator or maintenance personnel to conduct an on-site safety inspection; After troubleshooting, perform a system reset and test verification to ensure that the equipment safely returns to normal operation, conduct subsequent analysis, automatically write an incident report, and take preventive measures to avoid recurrence of the incident.

8. The multi-dimensional cutting precision control system for a laser tube cutting machine according to claim 1, characterized in that: The specific process of the data acquisition module collecting the cutting trajectory data of the initial laser tube cutting machine is as follows: Install sensors on the pipe cutter to measure cutting speed, depth, angle and path; set up data interfaces and transmission protocols to ensure effective communication between the sensors and the system; Configure the data acquisition system, set the sampling frequency, data format and storage parameters, and collect data from all sensors simultaneously; Filter, denoise and normalize the raw data, and store the processed data in hard disk, solid-state drive or cloud storage; Synchronize data regularly to ensure real-time and consistency; Test module performance during actual cutting to ensure data accuracy and real-time performance meet system requirements.

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