Metal pipe cutting servo correction system assisted by three-dimensional simulation

Abstract

The invention provides a three-dimensional simulation assisted metal pipe cutting servo correction system, and relates to the technical field of metal cutting, and the system comprises the steps that metal pipe data are collected through a multi-dimensional image and a multi-dimensional laser array, and a multi-dimensional image set and a point cloud set are obtained; fusing the image set to generate a metal tube contour, and fitting a point cloud to construct a three-dimensional model; in the simulation space, comparing the standard three-dimensional model with the actually-measured three-dimensional model, and determining transverse and longitudinal displacement deviations; a precision threshold value is set, and if the deviation exceeds the threshold value, the servo control parameters are corrected; adjusting the position of the metal pipe according to the correction strategy, and executing cutting operation. According to the method, the cutting precision and quality of the metal pipe are improved. By means of the metal pipe cutting device, the technical problems that due to the fact that tiny displacement deviation during metal pipe cutting cannot be perceived in time, the metal pipe cutting precision and cutting quality are poor, and the expected cutting machining requirement cannot be met can be solved, and the technical effects that the metal pipe cutting precision and cutting quality are improved, and it is ensured that cutting machining meets the expected requirement are achieved.

Description

Technical Field

[0001] This application relates to the field of metal cutting technology, and in particular to a servo correction system for metal tube cutting assisted by three-dimensional simulation. Background Technology

[0002] With the continuous advancement of metal processing technology, the precision and quality requirements for metal tube cutting are becoming increasingly stringent. However, during the metal tube cutting process, the cutting position often shifts due to the tube's own vibration, minute deviations in motor feed control, and other factors. This includes both lateral and longitudinal displacement deviations. Existing metal tube cutting methods cannot accurately monitor and identify these subtle changes in real time, making it difficult to detect and correct them promptly. Consequently, the cutting accuracy and quality of metal tubes often fail to meet expectations, a problem particularly prominent in high-precision, high-quality cutting applications. Summary of the Invention

[0003] The purpose of this application is to provide a servo correction system for metal tube cutting with three-dimensional simulation assistance, in order to solve the technical problem that the accuracy and quality of metal tube cutting are poor due to the inability to detect minute displacement deviations during metal tube cutting in time, which fails to meet the expected cutting and processing requirements.

[0004] In view of the above problems, this application provides a servo correction system for metal tube cutting with three-dimensional simulation assistance.

[0005] This application provides a 3D simulation-assisted servo correction system for metal tube cutting. The system includes: a data acquisition module, used to acquire data from the metal tube to be cut via a multi-dimensional image array and a multi-dimensional laser array at a preset node, obtaining a multi-dimensional image set and a multi-dimensional point cloud set, wherein the preset node is located before the cutting node; a 3D model construction module, used to perform image fusion on the multi-dimensional image set based on an image registration algorithm to generate a metal tube image contour, and to perform point cloud fitting on the multi-dimensional point cloud set using the metal tube image contour as a constraint to construct a 3D model of the metal tube; and a deviation comparison module, used to execute a standard metal tube 3D model and... The deviation comparison of the three-dimensional model of the metal tube determines the displacement deviation result, which includes lateral deviation and longitudinal deviation. The deviation judgment module is used to set lateral constraint thresholds and longitudinal constraint thresholds based on the cutting accuracy index. When the lateral deviation does not meet the lateral constraint threshold and / or the longitudinal deviation does not meet the longitudinal constraint threshold, the servo control parameters are corrected and optimized according to the displacement deviation result to generate a servo control correction strategy. The metal tube cutting module is used to perform displacement correction on the metal tube to be cut according to the servo control correction strategy, and after the displacement correction is completed, the metal tube cutting of the cutting node is performed based on the preset cutting scheme.

[0006] The technical solution provided in this application has at least the following technical effects or advantages: The aforementioned 3D simulation-assisted metal tube cutting servo correction system acquires data from the metal tube to be cut at predetermined time points using a multi-dimensional image array and a multi-dimensional laser array, obtaining multi-angle images and point cloud data. Subsequently, through an image registration algorithm, these multi-dimensional images are fused into a complete metal tube image contour. Simultaneously, using this image contour as a reference, the multi-dimensional point cloud data is fitted to construct a 3D model of the metal tube. After obtaining the 3D model of the metal tube, it is compared with a standard 3D model. Through comparison in the 3D simulation space, the displacement deviation of the metal tube in the lateral and longitudinal directions can be accurately calculated. Based on preset cutting accuracy requirements, lateral and longitudinal displacement thresholds are set. If the detected displacement deviation exceeds these thresholds, the servo control parameters are automatically corrected according to the deviation results, and a correction strategy is generated. Then, according to this correction strategy, the metal tube is displacement corrected to ensure it is in the correct position. Once correction is complete, the metal tube is precisely cut according to the preset cutting scheme. This system can significantly improve the accuracy and quality of metal tube cutting through real-time monitoring, comparison, and correction, meeting the stringent requirements of modern industrial production.

[0007] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of the servo correction system for metal tube cutting with three-dimensional simulation assistance as described in this application.

[0010] Figure 2 This is a flowchart illustrating the deviation judgment module of the 3D simulation-assisted servo correction system for metal tube cutting in this application.

[0011] Figure labeling: Data acquisition module 1, 3D model construction module 2, deviation comparison module 3, deviation judgment module 4, metal pipe cutting module 5. Detailed Implementation

[0012] This application solves the technical problem that the inability to detect subtle displacement deviations during metal tube cutting leads to poor cutting accuracy and quality, thus failing to meet expected cutting and processing requirements. It provides a servo correction system for metal tube cutting with three-dimensional simulation assistance, thereby improving the cutting accuracy and quality of metal tubes and ensuring that the cutting and processing meet the expected requirements.

[0013] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0014] For examples, please refer to the appendix. Figure 1This application provides a 3D simulation-assisted servo correction system for cutting metal tubes, the system specifically including the following modules: The data acquisition module 1 is used to acquire data from the metal tube to be cut through a multi-dimensional image array and a multi-dimensional laser array at a preset node, so as to obtain a multi-dimensional image set and a multi-dimensional point cloud set, wherein the preset node is located before the cutting node.

[0015] In this embodiment, before cutting the metal tube, the system terminal pre-sets a node to ensure that necessary data is obtained before the cutting operation, i.e., before the cutting node, and performs corresponding processing and analysis. Subsequently, under this pre-set node, a multi-dimensional image array and a multi-dimensional laser array are used to comprehensively acquire data about the metal tube to be cut. Through this method, the system terminal can collect image sets and point cloud sets from different angles and dimensions. These image and point cloud data provide detailed three-dimensional information about the metal tube, providing important reference for the subsequent cutting process.

[0016] The 3D model construction module 2 is used to perform image fusion on the multi-dimensional image set based on the image registration algorithm to generate the metal tube image outline, and to perform point cloud fitting on the multi-dimensional point cloud set using the metal tube image outline as a constraint to construct a 3D model of the metal tube.

[0017] In one embodiment, during the preparation stage before cutting the metal tube, to construct an accurate 3D model of the metal tube, the system terminal first extracts key image features from a multi-dimensional image set. These features include edges, corners, textures, etc., representing significant information and structure in the images. Subsequently, the extracted feature points are matched between different images using Euclidean distance to find the best-matching feature point pair. Then, based on the matched feature point pair, geometric transformation parameters between the images are estimated. These parameters describe how to map one image onto another to achieve precise image alignment. Geometric transformations include translation, rotation, scaling, and affine transformations. Subsequently, the multi-dimensional image set is fused using the estimated geometric transformation parameters. This process involves integrating information from different images into a unified coordinate system and eliminating inconsistencies between overlapping parts. The fused image presents a complete view of the metal tube from multiple perspectives. Afterward, the image contour of the metal tube is extracted from the fused image through edge detection, generating the metal tube image contour. This contour describes the shape and boundaries of the metal tube in the 2D image. After obtaining the outline of the metal tube image, the system terminal uses the outline as a constraint to perform multiple random fittings on the point cloud dataset, and selects the optimal point cloud fitting result based on the fitting evaluation coefficients generated in each fitting process. Then, the system terminal organizes and fits the selected point cloud fitting results to construct a 3D model of the metal tube. This 3D model not only has high accuracy and realism, but also comprehensively displays the spatial structure and dimensional information of the metal tube, providing important reference for subsequent metal tube cutting operations.

[0018] Furthermore, the 3D model building module 2 also includes: The multidimensional point cloud set is fused based on the point cloud registration strategy to obtain a point cloud dataset; the outline of the metal tube image is used as a constraint to perform random fitting in the point cloud dataset to obtain the first point cloud fitting result.

[0019] Preferably, the system terminal uses a point cloud registration strategy to integrate multiple point cloud datasets acquired from different angles or at different times. The main purpose of the point cloud registration strategy is to align these point cloud datasets to the same coordinate system, thereby eliminating deviations caused by different measurement positions or angles. Through this process, the system terminal can generate a more complete and accurate point cloud dataset, which contains detailed 3D information of all areas on the surface of the metal tube. Subsequently, using the generated metal tube image contour as a constraint, the system terminal randomly selects an initial fitting position and orientation from the fused point cloud dataset based on the approximate shape and position of the metal tube image contour. Then, based on the initial fitting position, corresponding points are selected from the point cloud dataset to construct the first 3D model of the metal tube, and the constructed first 3D model is used as the first point cloud fitting result. This fitting result initially describes the shape and size of the metal tube, providing important reference information for the subsequent construction of the metal tube 3D model.

[0020] The fitting quality of the first point cloud fitting result is evaluated to obtain the first fitting evaluation coefficient, wherein the fitting evaluation coefficient is the ratio of the number of point clouds falling within the outline of the metal tube image to the total number of point clouds in the point cloud dataset.

[0021] Preferably, for the first point cloud fitting result, the system terminal evaluates its quality to ensure its accuracy and reliability. In this process, the system terminal counts the number of point clouds falling within the outline of the metal tube image and calculates the ratio of the counted number of point clouds to the total number of point clouds in the point cloud dataset to obtain the first fitting evaluation coefficient. This evaluation coefficient reflects the degree of fit between the fitting result and the actual shape of the metal tube. If the fit is good, then most of the point cloud data should fall within the area defined by the metal tube image outline, so the evaluation coefficient will be close to or equal to 1. Conversely, if the fitting result deviates significantly from the actual shape, then only a portion of the point cloud data will fall within the outline, and the evaluation coefficient will be lower.

[0022] Again, using the outline of the metal tube image as a constraint, random fitting is performed on the point cloud dataset to obtain a second point cloud fitting result and a second fitting evaluation coefficient. If the first fitting evaluation coefficient is greater than the second fitting evaluation coefficient, the second point cloud fitting result is discarded; otherwise, the first point cloud fitting result is discarded. Random iterative fitting is performed, and when the preset number of fitting times is met, the current point cloud fitting result is output and set as the optimal point cloud fitting result. Based on the optimal point cloud fitting result, the three-dimensional model of the metal tube is built.

[0023] Preferably, the system terminal again uses the outline of the metal tube image as a constraint, performs random fitting on the point cloud dataset, and repeats the same steps as obtaining the first point cloud fitting result to obtain a second point cloud fitting result, and calculates the corresponding fitting evaluation coefficient, i.e., the second fitting evaluation coefficient. Then, the first fitting evaluation coefficient and the second fitting evaluation coefficient are compared. If the first fitting evaluation coefficient is greater than the second, the system terminal judges the first fitting result to be better and discards the second fitting result. Conversely, if the second fitting evaluation coefficient is higher, the first fitting result is discarded. The system terminal then continues to repeat the above process, performing multiple random iterative fittings. Each iteration attempts to find a point cloud fitting result that is closer to the actual shape of the metal tube and calculates the corresponding fitting evaluation coefficient. This process continues until a preset number of fittings is met. Then, when the preset number of fittings is reached, the system terminal selects the point cloud fitting result with the highest current evaluation coefficient as the optimal point cloud fitting result and builds a 3D model of the metal tube based on this result. This model will serve as a reference for subsequent metal tube cutting. This process involves iterative fitting and comparison of evaluation coefficients to continuously optimize the point cloud fitting results, ultimately yielding a 3D model that most closely approximates the shape of the actual metal tube. This method improves the accuracy and reliability of model construction, providing strong support for subsequent operations.

[0024] Deviation comparison module 3 is used to perform a deviation comparison between the standard metal tube 3D model and the metal tube 3D model in the 3D simulation space to determine the displacement deviation result, wherein the displacement deviation result includes lateral deviation and longitudinal deviation.

[0025] In one embodiment, within a 3D simulation space, to ensure the metal tube to be cut conforms to a preset standard, the system terminal compares the 3D model of the metal tube with a 3D model of a standard metal tube. This process primarily compares the angular deviations of the two models in 3D space. Specifically, the system terminal places the two 3D models within the same 3D simulation space and calculates their differences. These differences manifest as angular deviations in the lateral and longitudinal directions of the two models. Through this process, the displacement deviation results, including the lateral and longitudinal deviation angles of the metal tube's 3D model, can be calculated.

[0026] Furthermore, the deviation comparison module 3 also includes: In three-dimensional space, the lateral and longitudinal deviation analyses of the standard metal tube 3D model and the metal tube 3D model are performed to determine the lateral deviation angle and the longitudinal deviation angle. The lateral deviation angle is the angular deviation between the horizontal plane of the metal tube center and the horizontal plane of the standard metal tube center, and the longitudinal deviation angle is the angular deviation between the metal tube cutting surface and the standard metal tube cutting surface. The displacement deviation result is generated based on the lateral and longitudinal deviation angles.

[0027] Preferably, in three-dimensional space, to ensure that the position and angle of the metal tube match the standard model, the system terminal performs deviation analysis on the standard metal tube 3D model and the actual metal tube 3D model. This analysis focuses on two aspects: lateral deviation and longitudinal deviation. The lateral deviation angle refers to the angular difference between the horizontal plane of the actual metal tube's center and the horizontal plane of the standard metal tube's center, i.e., the rotational deviation of the actual metal tube in the horizontal direction. For example, if the actual metal tube is slightly rotated relative to the standard model, there will be an angular deviation between their horizontal planes. The longitudinal deviation angle refers to the angular difference between the cut surface of the actual metal tube and the cut surface of the standard metal tube. This deviation reflects whether the cut surface of the actual metal tube is consistent with the standard model in the vertical direction. If the cut surface of the actual metal tube is slightly tilted or deviates from the standard position, a longitudinal deviation angle will occur. Specifically, the system terminal aligns the standard metal tube 3D model and the actual metal tube 3D model to ensure that the two 3D models are in similar positions and orientations in three-dimensional space to facilitate deviation analysis. Subsequently, the center points of the top and bottom of the metal tube are calculated, and the horizontal plane of the metal tube 3D model's center is determined based on the calculation results. Next, the horizontal plane of the center of the metal tube is compared with the horizontal plane of the center of the standard metal tube, and the angle between the two horizontal planes, i.e., the lateral deviation angle, is calculated. Then, the system terminal determines the position and orientation of the metal tube's cut surface and compares it with the standard metal tube's cut surface, calculating the angle between the two cut surfaces, i.e., the longitudinal deviation angle. Finally, the system terminal summarizes the calculated lateral and longitudinal deviation angles to form a complete displacement deviation result, providing accurate data support for subsequent adjustments and corrections.

[0028] The deviation judgment module 4 is used to set the lateral constraint threshold and the longitudinal constraint threshold based on the cutting accuracy index. When the lateral deviation does not meet the lateral constraint threshold and / or the longitudinal deviation does not meet the longitudinal constraint threshold, the servo control parameters are corrected and optimized according to the displacement deviation result to generate a servo control correction strategy.

[0029] In one embodiment, based on cutting accuracy indicators, the system terminal sets two key deviation thresholds for the cutting process of the 3D model of the metal tube: a lateral constraint threshold and a longitudinal constraint threshold. These two thresholds are set according to the maximum allowable deviation in the actual cutting operation, aiming to ensure cutting accuracy and consistency. When the lateral deviation between the actual cutting position and the standard position of the metal tube exceeds the lateral constraint threshold, or the longitudinal deviation exceeds the longitudinal constraint threshold, it indicates that the current cutting operation may not meet the accuracy requirements and needs adjustment. At this time, based on the displacement deviation results obtained from previous analysis, the servo control parameters are calibrated and optimized using a control correction evaluation function. By adjusting the servo control parameters, the system terminal can improve the accuracy of the cutting position, making it closer to the standard position. The calibration and optimization process involves multiple iterations and adjustments, continuously trying different parameter combinations to find the optimal parameter configuration that minimizes the cutting deviation. Once the optimal servo control parameter configuration is found, the system terminal generates a servo control correction strategy. Through this servo control correction strategy, the system terminal can perform displacement correction on the metal tube to be cut, reducing cutting deviation, improving cutting accuracy, and ensuring product quality.

[0030] Furthermore, the deviation judgment module 4 also includes: Obtain servo control parameters and servo parameter rated thresholds, and randomly generate multiple initial servo control parameters based on the servo parameter rated thresholds; construct a control correction evaluation function based on the displacement deviation results, and use the control correction evaluation function to optimize the multiple initial servo control parameters to obtain the servo control correction strategy.

[0031] Preferably, in optimizing servo control to improve cutting accuracy, the system terminal first acquires the current servo control parameters and their rated thresholds. These rated thresholds are safe and effective operating ranges determined based on historical verification. Subsequently, based on these rated thresholds, the system terminal randomly generates multiple initial servo control parameter combinations. These initial parameter combinations serve as the initial population for subsequent parameter optimization. Then, based on the acquired displacement deviation results, a control correction evaluation function is constructed. The main function of this function is to evaluate the cutting accuracy under different servo control parameter combinations and calculate the evaluation coefficients of the servo control parameters. Then, the system terminal substitutes each individual in the initial population, i.e., the servo control parameter combination, into the control correction evaluation function to calculate its evaluation coefficient. The higher the evaluation coefficient, the better the set of servo control parameters performs in reducing displacement deviation. Further, based on the fitness evaluation results, the system terminal selects individuals with higher evaluation coefficients from the population as parents using a roulette wheel selection process. Subsequently, the system terminal randomly selects two parent individuals from the multiple parents selected by the roulette wheel selection and generates new offspring individuals through a crossover operation. The crossover operation simulates the gene recombination process in biological evolution, which helps to discover new excellent solutions. Next, certain genes in the offspring individuals, i.e., the servo control parameters, are randomly mutated. Mutation increases population diversity and helps avoid getting trapped in local optima. Then, the offspring individuals generated after crossover and mutation are merged with a portion of the parent individuals to form a new population. This new population replaces the old population and enters the next iteration. The process of calculating evaluation coefficients, selecting, crossovering, mutating, and generating new populations is repeated until the preset number of iterations is met. After the iteration ends, the system terminal selects the individual with the highest evaluation coefficient in the population as the optimal solution, i.e., the optimal combination of servo control parameters. This optimal solution is the servo control correction strategy, which can be used for displacement correction to improve cutting accuracy.

[0032] Furthermore, the control correction evaluation function includes: The expression for the control correction evaluation function is: ; in, Let x be the evaluation coefficient for the x-th servo control parameter. To correct the precision weights, To correct for efficiency weights, As energy consumption weight, and Greater than and the sum of For horizontal weighting, For vertical weighting, This is the lateral correction data for the x-th servo control parameter. For lateral deviation, This is the longitudinal correction data for the x-th servo control parameter. For longitudinal deviation, Let x be the calibration time for the x-th servo control parameter. Let x be the energy consumption of the x-th servo control parameter.

[0033] Optionally, the control calibration evaluation function aims to comprehensively evaluate multiple aspects of servo control parameters, including calibration accuracy, calibration efficiency, and energy consumption. It combines horizontal and vertical calibration data, calibration time, and energy consumption to provide an evaluation coefficient used to quantify the performance of the x-th servo control parameter. The specific control calibration evaluation function is as follows: ; in, is the evaluation coefficient for the x-th servo control parameter, used to quantify the overall performance of this parameter during the calibration process. The calibration accuracy weight represents the importance of calibration accuracy in the evaluation. Since v1 is greater than the sum of v2 and v3, it means that calibration accuracy is the most important factor in the evaluation process. The corrected efficiency weight indicates the importance of the corrected efficiency in the evaluation. Energy consumption weight indicates the importance of energy consumption in the evaluation. The horizontal correction weights represent the importance of the horizontal correction data in the evaluation. The longitudinal calibration weight represents the importance of the longitudinal calibration data in the evaluation. This is the lateral correction data for the x-th servo control parameter, representing the performance of this parameter during the lateral correction process. The lateral deviation represents the difference between the standard value and the actual lateral position. This is the longitudinal correction data for the x-th servo control parameter, representing the performance of this parameter during the longitudinal correction process. The longitudinal deviation represents the difference between the standard value and the actual longitudinal position. The calibration time for the x-th servo control parameter represents the time required to complete the calibration. Let be the energy consumption of the x-th servo control parameter, representing the energy consumed during the calibration process. This evaluation function comprehensively assesses servo control parameters by combining multiple aspects such as calibration accuracy, calibration efficiency, and energy consumption. Among these, calibration accuracy is the most important factor, quantified using horizontal and vertical calibration data; calibration efficiency and energy consumption, as secondary factors, are also included in the evaluation scope. This evaluation method helps to select servo control parameters that ensure calibration accuracy while achieving optimal performance in terms of time and energy consumption.

[0034] Furthermore, such as Figure 2 As shown, the deviation judgment module further includes: When the lateral deviation meets the lateral constraint threshold and the longitudinal deviation meets the longitudinal constraint threshold, the cutting twin model is activated. The cutting twin model is constructed based on the simulation of the cutting equipment and the metal tube to be cut.

[0035] Preferably, when both lateral and longitudinal deviations meet the set constraint thresholds, it indicates that the servo control device has achieved sufficient precision in positioning the metal tube, meeting the preset accuracy requirements. In this case, the system terminal activates the cutting twin model, which is constructed based on simulations of the actual cutting equipment and the metal tube to be cut. Specifically, the system terminal collects detailed parameters of the cutting equipment, such as the size of the cutting blade, cutting speed, and power. Then, based on modeling tools, a three-dimensional model of the cutting equipment is built according to the collected data. These models can accurately reflect the geometry and size of the actual object cutting equipment. Subsequently, the simulation environment is configured in the simulation software, including physical factors that may affect the cutting process, such as gravity, temperature, and humidity, and the motion trajectory, speed, and cutting parameters of the cutting equipment are set to simulate the real cutting process. Afterward, the configured simulation environment, the three-dimensional model of the cutting equipment, and the three-dimensional model of the metal tube are combined to form the cutting twin model. This cutting twin model simulates various conditions and parameters in the real cutting process, providing simulation results that are very close to the real cutting environment. When the servo control device determines that the positional accuracy of the metal tube meets the requirements, activating this model can help predict and verify the cutting process, thereby ensuring the accuracy and efficiency of the cutting.

[0036] Based on the cutting twin model, simulated cutting is performed within a preset time window according to the displacement deviation results to determine the deviation prediction node and the deviation prediction result, wherein the preset time window is the remaining cutting time.

[0037] Preferably, the system terminal, based on the established cutting twin model, performs simulated cutting within the remaining cutting time, i.e., a preset time window, according to the calculated displacement deviation results. This simulated cutting process aims to predict and evaluate the cutting deviations that may occur within the remaining time. During the simulated cutting process, for each prediction node, the system terminal evaluates whether the lateral and longitudinal deviations of the cutting meet preset deviation thresholds. When the simulation results show that the predicted cutting deviation does not meet the deviation threshold at the preset node of the remaining cutting time, it means that, based on the current displacement deviation and cutting conditions, the system terminal cannot maintain the cutting accuracy within an acceptable range. In this case, the system terminal uses this preset node as the deviation prediction node and the simulated position deviation result as the deviation prediction result.

[0038] Based on the deviation prediction results, servo control parameters are corrected and optimized to generate a servo control correction strategy.

[0039] Preferably, after completing the simulated cutting based on the cutting twin model and obtaining the deviation prediction results, the system terminal performs calibration and optimization of the servo control parameters to generate a servo control calibration strategy. This process is achieved by the system terminal comparing the actual 3D position information with the 3D simulated position information and re-executing the servo control parameter calibration and optimization. This process iterates continuously until a set of servo control parameters that can significantly reduce cutting deviation is found. Then, based on the optimized servo control parameters, a servo control calibration strategy is generated. This strategy includes the adjusted servo control parameter settings and can be directly used for displacement correction, thereby reducing deviation and improving cutting accuracy during actual cutting.

[0040] At the deviation prediction node, the displacement of the metal tube to be cut is corrected according to the servo control correction strategy, and the metal tube is cut after the displacement correction is completed.

[0041] Preferably, during the cutting of a metal tube, if it is predicted that the displacement deviation of the metal tube may exceed a preset threshold at a specific deviation prediction node, the system terminal needs to take measures to ensure the accuracy and quality of the cutting. Before the deviation prediction node arrives, the system terminal will adjust the servo control device according to the parameter settings in the servo control correction strategy, thereby correcting the displacement of the metal tube. This process includes adjusting the moving speed, acceleration, and position of the cutting device to ensure that the metal tube can move along the predetermined trajectory and with the desired accuracy. Once the displacement correction is completed and it is confirmed that the position and orientation of the metal tube meet the cutting requirements, the system terminal will perform the cutting operation on the metal tube to improve the accuracy and efficiency of the cutting.

[0042] Furthermore, this application provides methods for determining deviation prediction nodes and deviation prediction results, including: Based on the displacement deviation results and the preset time window, simulated cutting is performed using the cutting twin model, and three-dimensional simulated position information under the preset node is obtained; deviation analysis is performed on the standard metal tube three-dimensional model and the three-dimensional simulated position information to determine the simulated position deviation results, wherein the simulated position deviation results include simulated lateral deviation and simulated longitudinal deviation, and the simulated position deviation results correspond one-to-one with the preset node.

[0043] Optionally, to ensure the accuracy and efficiency of cutting, the system terminal simulates the cutting process using a cutting twin model based on the current displacement deviation of the metal tube to be cut and a preset time window. This means that at each preset time node, the system terminal performs a simulated cutting operation to obtain the three-dimensional simulated position information at that node. This information represents the expected position of the metal tube at each critical moment during the simulated cutting process. Subsequently, the system terminal performs a deviation analysis between the simulated three-dimensional position information and the standard metal tube three-dimensional model. This deviation analysis method is similar to the aforementioned method for determining the displacement deviation result. By comparing the angular deviations of the metal tube's center horizontal plane and the cutting surface from the standard position during the simulated cutting process, the simulated position deviation result is determined. These simulated position deviation results include simulated lateral deviation and simulated longitudinal deviation. This process is repeated, and each preset node generates a corresponding simulation result, meaning that each simulation yields a simulated position deviation result that corresponds one-to-one with the preset node. This node-to-node correspondence method ensures that the system terminal can monitor and correct the position deviation of the metal tube in real time throughout the entire cutting process, thereby ensuring the accuracy and efficiency of the cutting.

[0044] If the simulated lateral deviation does not meet the lateral constraint threshold and / or the simulated longitudinal deviation does not meet the longitudinal constraint threshold, then the preset node is set as the deviation prediction node, and the simulated position deviation result is set as the deviation prediction result.

[0045] Optionally, during the simulated cutting process using the cutting twin model, the system terminal continuously monitors the positional deviations. When the system terminal detects that the simulated lateral deviation does not meet the preset lateral constraint threshold and / or the simulated longitudinal deviation does not meet the preset longitudinal constraint threshold, it indicates that the position of the metal tube has deviated from the expected range in the simulated scenario, which may lead to a decrease in actual cutting accuracy. To identify and record such deviations, the system terminal sets these preset nodes that do not meet the constraint thresholds as deviation prediction nodes. Simultaneously, the simulated positional deviation results corresponding to these nodes are also set as deviation prediction results. The purpose of this is to clarify which nodes have deviated in the simulation and the specific values ​​of these deviations, thereby providing data support for subsequent analysis and adjustments.

[0046] Furthermore, this application provides a servo control correction strategy, including: Using a multi-dimensional image array and a multi-dimensional laser array, the actual three-dimensional position information of the metal tube to be cut is acquired at each preset node within the time period of the current preset node and the deviation prediction node. The actual three-dimensional position information is compared with the three-dimensional simulated position information at the same preset node. If the deviation comparison result does not meet the predetermined tolerance deviation range, the servo control parameter correction optimization is re-executed at this preset node to obtain the updated servo control correction strategy.

[0047] Optionally, the system terminal uses a multi-dimensional image array and a multi-dimensional laser array to capture the actual three-dimensional position information of the metal tube to be cut at each preset node within the time period of the current preset node and the deviation prediction node. This position information is acquired in real time to ensure accurate tracking of the metal tube's position. Subsequently, the system terminal compares this actually acquired three-dimensional position information with the three-dimensional simulated position information at the same preset node. During the comparison process, the system terminal checks whether the deviation between the actual position and the simulated position exceeds a predetermined tolerance deviation range. This tolerance deviation range is a pre-set error range based on the accuracy of the simulation and the accuracy of the equipment acquisition. If the deviation comparison result shows that the difference between the actual position and the simulated position exceeds this predetermined tolerance range, the system terminal will determine that the current servo control parameters are not accurate enough and need to be adjusted. After determining that the deviation exceeds the tolerance range, the system terminal uses the same method as described above for parameter optimization of multiple initial servo control parameters to re-execute the servo control parameter correction and optimization process at the current preset node to find a more suitable servo control strategy, thereby ensuring more accurate position and accuracy control in the subsequent cutting process. After recalibration and optimization, the system terminal obtains an updated servo control calibration strategy. This strategy will be used for servo control in subsequent cutting processes to improve cutting accuracy and efficiency.

[0048] The metal tube cutting module 5 is used to perform displacement correction on the metal tube to be cut according to the servo control correction strategy, and after the displacement correction is completed, to perform metal tube cutting at the cutting node based on the preset cutting scheme.

[0049] In one embodiment, to ensure cutting accuracy and efficiency, the system terminal performs displacement correction on the metal tube to be cut according to a servo control correction strategy. The purpose of displacement correction is to bring the position and orientation of the metal tube to the preset cutting requirements, ensuring cutting accuracy and precision. This process involves fine-tuning the metal tube to perfectly match the preset cutting path and position. After displacement correction is completed, the system terminal begins cutting the metal tube at the corresponding cutting node based on a preset cutting scheme. This cutting scheme is pre-set and includes parameters such as the cutting path, speed, and depth to ensure cutting quality and efficiency.

[0050] In summary, the 3D simulation-assisted servo correction system for metal tube cutting provided in this application has the following technical effects: This application acquires data of the metal tube to be cut at preset nodes using a multi-dimensional image array and a multi-dimensional laser array, and constructs an accurate 3D model using an image registration algorithm. Subsequently, in the 3D simulation space, the standard 3D model of the metal tube is compared with the actual 3D model of the metal tube, and the servo control parameters are corrected and optimized based on the lateral and longitudinal displacement deviations. If the position of the metal tube meets a preset threshold, the cutting twin model is activated to simulate cutting, predicting possible deviations and further correcting the servo control parameters accordingly. Furthermore, an iterative random fitting method is used to optimize the 3D model construction, and multi-angle deviation analysis ensures a comprehensive improvement in cutting accuracy. These technical effects collectively solve the technical problem that the inability to detect subtle displacement deviations during metal tube cutting leads to poor cutting accuracy and quality, failing to meet expected cutting requirements, thus achieving the technical effect of improving the cutting accuracy and quality of metal tubes and ensuring that cutting processes meet expected requirements.

[0051] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0052] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A 3D simulation-assisted servo correction system for metal tube cutting, characterized in that, The system includes: The data acquisition module is used to acquire data from the metal tube to be cut through a multi-dimensional image array and a multi-dimensional laser array at a preset node, so as to obtain a multi-dimensional image set and a multi-dimensional point cloud set, wherein the preset node is located before the cutting node; The three-dimensional model construction module is used to perform image fusion on the multi-dimensional image set based on the image registration algorithm to generate the metal tube image outline, and to perform point cloud fitting on the multi-dimensional point cloud set using the metal tube image outline as a constraint to construct a three-dimensional model of the metal tube. The deviation comparison module is used to perform a deviation comparison between the standard metal tube 3D model and the metal tube 3D model in a 3D simulation space to determine the displacement deviation result, wherein the displacement deviation result includes lateral deviation and longitudinal deviation. The deviation judgment module is used to set lateral constraint thresholds and longitudinal constraint thresholds based on the cutting accuracy index. When the lateral deviation does not meet the lateral constraint threshold and / or the longitudinal deviation does not meet the longitudinal constraint threshold, the servo control parameters are corrected and optimized according to the displacement deviation result to generate a servo control correction strategy. The metal tube cutting module is used to perform displacement correction on the metal tube to be cut according to the servo control correction strategy, and after the displacement correction is completed, to perform metal tube cutting at the cutting node based on the preset cutting scheme.

2. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 1, characterized in that, The deviation judgment module also includes: When the lateral deviation meets the lateral constraint threshold and the longitudinal deviation meets the longitudinal constraint threshold, the cutting twin model is activated. The cutting twin model is constructed based on the simulation of the cutting equipment and the metal tube to be cut. Based on the cutting twin model, simulated cutting is performed within a preset time window according to the displacement deviation results to determine the deviation prediction node and the deviation prediction result, wherein the preset time window is the remaining cutting time; Based on the deviation prediction results, servo control parameters are corrected and optimized to generate a servo control correction strategy. At the deviation prediction node, the displacement of the metal tube to be cut is corrected according to the servo control correction strategy, and the metal tube is cut after the displacement correction is completed.

3. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 2, characterized in that, Determine the deviation prediction nodes and deviation prediction results, including: Based on the displacement deviation results and the preset time window, the cutting twin model is used to simulate cutting and obtain the three-dimensional simulated position information under the preset node. Perform a deviation analysis between the standard metal tube 3D model and the 3D simulated position information to determine the simulated position deviation results. The simulated position deviation results include simulated lateral deviation and simulated longitudinal deviation, and the simulated position deviation results correspond one-to-one with the preset nodes. If the simulated lateral deviation does not meet the lateral constraint threshold and / or the simulated longitudinal deviation does not meet the longitudinal constraint threshold, then the preset node is set as the deviation prediction node, and the simulated position deviation result is set as the deviation prediction result.

4. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 3, characterized in that, Generate a servo control correction strategy, including: By using a multi-dimensional image array and a multi-dimensional laser array, the actual three-dimensional position information of the metal tube to be cut under each preset node is acquired within the time period between the current preset node and the deviation prediction node. The actual three-dimensional position information is compared with the three-dimensional simulated position information under the same preset node. If the deviation comparison result does not meet the predetermined tolerance deviation range, the servo control parameter correction optimization is re-executed under this preset node to obtain the updated servo control correction strategy.

5. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 1, characterized in that, The 3D model construction module also includes: The multidimensional point cloud set is fused based on the point cloud registration strategy to obtain a point cloud dataset. Using the outline of the metal tube image as a constraint, random fitting is performed on the point cloud dataset to obtain the first point cloud fitting result; The fitting quality of the first point cloud fitting result is evaluated to obtain the first fitting evaluation coefficient, wherein the fitting evaluation coefficient is the ratio of the number of point clouds falling within the outline of the metal tube image to the total number of point clouds in the point cloud dataset. Again, using the outline of the metal tube image as a constraint, random fitting is performed on the point cloud dataset to obtain a second point cloud fitting result and a second fitting evaluation coefficient. If the first fitting evaluation coefficient is greater than the second fitting evaluation coefficient, then the second point cloud fitting result is discarded; otherwise, the first point cloud fitting result is discarded. Perform random iterative fitting, and when the preset number of fitting times is met, output the current point cloud fitting result as the optimal point cloud fitting result, and build the three-dimensional model of the metal tube based on the optimal point cloud fitting result.

6. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 1, characterized in that, The deviation comparison module also includes: In three-dimensional space, the lateral and longitudinal deviation analyses of the standard metal tube three-dimensional model and the metal tube three-dimensional model are performed to determine the lateral deviation angle and the longitudinal deviation angle. The lateral deviation angle is the angular deviation between the horizontal plane of the metal tube center and the horizontal plane of the standard metal tube center, and the longitudinal deviation angle is the angular deviation between the metal tube cutting surface and the standard metal tube cutting surface. The displacement deviation result is generated based on the lateral deviation angle and the longitudinal deviation angle.

7. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 1, characterized in that, The deviation judgment module also includes: Obtain servo control parameters and servo parameter rated thresholds, and randomly generate multiple initial servo control parameters based on the servo parameter rated thresholds; Based on the displacement deviation results, a control correction evaluation function is constructed. The control correction evaluation function is then used to optimize the multiple initial servo control parameters to obtain the servo control correction strategy.

8. The three-dimensional simulation-assisted servo correction system for metal tube cutting according to claim 7, characterized in that, The expression for the control correction evaluation function is: ； in, Let x be the evaluation coefficient for the x-th servo control parameter. To correct the precision weights, To correct for efficiency weights, As energy consumption weight, and Greater than and The sum of For horizontal weighting, For vertical weighting, This is the lateral correction data for the x-th servo control parameter. For lateral deviation, This is the longitudinal correction data for the x-th servo control parameter. For longitudinal deviation, Let x be the calibration time for the x-th servo control parameter. Let x be the energy consumption of the x-th servo control parameter.

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