A method for planning machining paths of CNC machine tools

Abstract

The present invention relates to the technical field of CNC machine tool processing, and in particular to a method for planning a processing path of a CNC machine tool. The method comprises the following steps: scanning the surface of a workpiece to be processed to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing workpiece surface perception based on the workpiece surface scanning data and the workpiece surface curvature data to obtain workpiece surface perception data; constructing a cutting force spatiotemporal dynamic model based on the workpiece surface scanning data and the workpiece surface perception data to obtain a cutting force spatiotemporal dynamic model; performing a cutting force spatiotemporal influence analysis based on the cutting force spatiotemporal dynamic model to obtain cutting heat energy and vibration data. The present invention improves processing quality and efficiency as well as processing accuracy and surface quality by analyzing the dynamic changes between the tool and the workpiece and the shape of the workpiece during the cutting process, and then correcting the local difference in tool radius.

Description

Technical Field

[0001] The invention relates to the technical field of numerical control machine tool processing, and in particular to a method for planning a processing path of a numerical control machine tool. Background Art

[0002] The machining path planning of CNC machine tools is to analyze and process the workpiece through computer software to determine the best machining path to achieve an efficient and accurate machining process. Most of the existing machining path planning methods for CNC machine tools are based on static cutting conditions for path planning, such as preset cutting speed, feed speed, etc., without considering the dynamic changes between the tool and the workpiece during the cutting process, and the shape of the workpiece will change, or the need for subsequent processing on the machined surface, which may lead to inappropriate cutting parameters in the actual machining process, affecting the machining quality and efficiency. Tool radius compensation is usually global, that is, the entire machining path is corrected at one time, without considering the correction needs of the local area; under complex workpieces or special machining requirements, insufficient local correction capabilities will affect machining accuracy and surface quality. Summary of the invention

[0003] Based on this, it is necessary to provide a method for planning a machining path of a CNC machine tool to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for planning a machining path of a CNC machine tool comprises the following steps:

[0005] Step S1: Scanning the workpiece surface by a laser scanner to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing workpiece surface sensing according to the workpiece surface scanning data and the workpiece surface curvature data to obtain workpiece surface sensing data;

[0006] Step S2: construct a spatiotemporal dynamic model of cutting force based on the workpiece surface scanning data and the workpiece surface sensing data to obtain the spatiotemporal dynamic model of cutting force; perform spatiotemporal influence analysis of cutting force based on the spatiotemporal dynamic model of cutting force to obtain cutting heat energy and vibration data; explore a stable cutting domain model based on the spatiotemporal dynamic model of cutting force and the cutting heat energy and vibration data; and obtain tool cutting parameters;

[0007] Step S3: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters; performing dynamic machining path planning according to the workpiece surface curvature data and the cutting force spatiotemporal dynamic model to obtain dynamic machining path data; performing adaptive machining path planning on the dynamic machining path data using tool cutting parameters to obtain adaptive machining path data;

[0008] Step S4: performing dynamic path correction according to the adaptive machining path data; obtaining a dynamic path correction strategy; extracting a tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data; performing tool radius correction on the adaptive machining path data using the dynamic path correction strategy and the tool correction amount data to obtain adaptive machining correction path data;

[0009] Step S5: Perform multi-axis motion analysis on the CNC machine tool to obtain multi-axis motion data; perform synchronous motion path compensation based on the multi-axis motion data to obtain CNC machine tool processing path data; generate CNC code based on the CNC machine tool processing path data to obtain CNC code data.

[0010] The present invention can fully understand the shape, characteristics and curvature changes of the workpiece through surface perception data, and provide an important basis for subsequent processing path planning, cutting parameter optimization and surface quality improvement. The construction of the spatiotemporal dynamic model of cutting force and the cutting impact analysis can help optimize the use parameters of the tool, improve cutting efficiency and tool life; the exploration of the stable cutting domain model helps to determine safe and stable cutting conditions, reduce vibration and thermal deformation during processing, and improve processing accuracy and surface quality. The extraction of surface quality optimization parameters helps to optimize the surface quality during processing; dynamic processing path planning and adaptive processing path planning can dynamically adjust the processing path according to the real-time shape and cutting conditions of the workpiece, and improve processing efficiency and accuracy. Dynamic path correction and tool radius correction can adjust the processing path and tool parameters according to real-time conditions during processing, improve processing accuracy and stability, and reduce processing errors and damage. The analysis of multi-axis motion data and synchronous motion path compensation can realize the precise control and optimization of the CNC machine tool processing process, improve processing efficiency and finished product quality; the generation of CNC code is based on the optimized processing path data, which can accurately guide the CNC machine tool to perform processing operations, ensure that the processing process is carried out as expected, reduce human errors and processing time, and improve production efficiency. Therefore, the present invention provides a method for planning a machining path for a CNC machine tool. By analyzing the dynamic changes between the tool and the workpiece and the shape of the workpiece during the cutting process, the workpiece surface perception data and the tool cutting parameters are obtained to plan an adaptive machining path. The adaptive path planning can dynamically adjust the machining path according to actual conditions to adapt to the changing workpiece shape or surface conditions; the local difference in tool radius is corrected on the adaptively planned path to improve the machining quality and efficiency as well as the machining accuracy and surface quality.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: scanning the workpiece surface by a laser scanner to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing linear feature extraction on the workpiece surface curvature data to obtain workpiece surface linear feature data;

[0013] Step S12: performing local area segmentation according to the workpiece surface scanning data to obtain local area segmentation data; performing local area feature extraction on the local area segmentation data according to the workpiece surface curvature data to obtain local area feature data;

[0014] Step S13: performing surface shape modeling according to the local area feature data and the workpiece surface linear feature data to obtain the workpiece surface shape model data;

[0015] Step S14: performing surface defect anomaly analysis based on the workpiece surface linear feature data and the workpiece surface curvature data to obtain abnormal area marking data;

[0016] Step S15: topologically connecting the surface feature points according to the workpiece surface shape model data and the workpiece surface linear feature data to obtain a feature point topological map; topologically linking the local area according to the workpiece surface shape model data and the local area feature data to obtain a local area topological map; performing surface accessibility analysis according to the feature point connection data and the local area connection data to obtain a surface accessibility map;

[0017] Step S16: Integrate the workpiece surface perception data according to the workpiece surface shape model data, abnormal area marking data, feature point topology map, local area topology map and surface accessibility map to obtain the workpiece surface perception data.

[0018] The present invention obtains the scanning data of the workpiece surface through a laser scanner, and can realize high-precision acquisition of the workpiece surface shape, which is helpful for subsequent processing path planning and surface feature analysis; at the same time, surface topology analysis and curvature data extraction are performed to obtain the geometric feature information of the workpiece surface, and provide basic data for subsequent workpiece surface shape modeling and abnormal analysis. Through local area segmentation and feature extraction, the workpiece surface can be segmented into different local areas, and its feature information is extracted for each area; it is helpful to conduct in-depth analysis of the local features of the workpiece surface, such as surface unevenness, angular features, etc., to provide more detailed data support for subsequent surface shape modeling and defect abnormal analysis. Through surface shape modeling, the geometric shape of the workpiece surface can be described in the form of a mathematical model, thereby realizing an abstract representation of the workpiece surface; it is helpful for processing path planning and workpiece design evaluation, and improving processing accuracy and efficiency. Through abnormal analysis, defects and abnormal areas on the workpiece surface, such as cracks, bumps, etc., can be detected; it is helpful to discover quality problems on the workpiece surface in advance, repair or adjust processing parameters, and ensure processing quality. Through the topological connection of feature points and the topological link of local areas, the feature points or local areas on the workpiece surface can be connected to form a topological map structure, which helps to have a deeper understanding of the structure and correlation of the workpiece surface; at the same time, surface accessibility analysis can determine the accessibility relationship between different areas on the workpiece surface, providing a reference for subsequent processing path planning and process optimization. By integrating various data, comprehensive perception data of the workpiece surface can be obtained, including shape characteristics, defect anomalies, structural topology and other information; it helps to formulate more accurate processing path planning strategies, improve processing quality and efficiency, and provide important reference for workpiece design and quality control.

[0019] Preferably, step S14 comprises the following steps:

[0020] Step S141: performing surface defect image detection on the linear feature data of the workpiece surface to obtain surface defect influence degree data; performing defect region image segmentation on the linear feature data of the workpiece surface to obtain defect region contour data;

[0021] Step S142: performing defect penetration depth analysis according to the defect area contour data to obtain defect penetration depth data; generating a defect thermal map according to the defect area contour data and the defect penetration depth data to obtain a defect thermal map;

[0022] Step S143: performing surface defect impact analysis based on the defect penetration depth data and the defect thermal map to obtain surface defect impact degree data; performing surface quality assessment based on the surface defect impact degree data and the workpiece surface curvature data to obtain surface quality index data;

[0023] Step S144: marking abnormal regions of the workpiece surface shape model data according to the surface quality index data to obtain abnormal region marking data.

[0024] The present invention can detect defects on the surface of the workpiece, such as scratches, bumps, etc., through surface defect image detection, obtain defect impact degree data, and understand the impact degree of defects on the quality of the workpiece; perform defect area image segmentation, separate the defect area from the normal area, obtain defect area contour data, and provide basic data for subsequent defect analysis. Through defect area contour data, defect penetration depth analysis can be performed to understand the depth information of the defect on the workpiece surface, obtain defect penetration depth data, and provide more accurate data support for evaluating the impact of defects; through the generation of defect heat maps, the distribution and degree of defects on the workpiece surface can be intuitively displayed, which is helpful for quantitative and qualitative analysis of defects. Through surface defect impact analysis, the penetration depth and distribution of defects can be comprehensively considered to obtain surface defect impact degree data to help evaluate the actual impact degree of defects on the quality of the workpiece; surface quality assessment based on surface defect impact degree data and workpiece surface curvature data can quantitatively evaluate the quality status of the workpiece surface and provide a basis for formulating subsequent quality improvement measures. By marking abnormal areas of the workpiece surface shape model data, the abnormal areas reflected by the surface quality index data can be marked on the model to facilitate subsequent quality improvement and repair work; the abnormal area marking data can guide operators or automation systems to perform corresponding processing to improve the quality and reliability of the workpiece surface.

[0025] Preferably, step S2 comprises the following steps:

[0026] Step S21: analyzing the material characteristics of the workpiece to be processed according to the workpiece surface scanning data to obtain the workpiece material performance parameters; predicting the cutting performance using a preset cutting performance prediction model and the workpiece material performance parameters to obtain cutting performance prediction data;

[0027] Step S22: performing a cutting force time series change analysis based on the workpiece material performance parameters and the cutting performance prediction data to obtain cutting force time series change data; performing a cutting force spatial law analysis based on the workpiece surface perception data and the cutting performance prediction data to obtain cutting force spatial law data;

[0028] Step S23: performing spatiotemporal dynamic modeling of cutting force based on the cutting force time series variation data and the cutting force spatial regularity data to obtain a spatiotemporal dynamic model of cutting force;

[0029] Step S24: performing cutting heat conduction analysis and cutting vibration analysis according to the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting heat and vibration data; constructing a stable cutting domain model based on the cutting force spatiotemporal dynamic model and the cutting heat and vibration data to obtain a stable cutting domain model;

[0030] Step S25: Define the range of stable cutting parameters according to the stable cutting domain model to obtain stable cutting parameter range data; construct a stable cutting domain exploration model based on machine learning technology and the stable cutting domain model to obtain a stable cutting domain exploration model; explore the optimal cutting parameter combination according to the stable cutting domain exploration model and the stable cutting parameter range data to obtain the tool cutting parameters.

[0031] The present invention uses a preset cutting performance prediction model, combined with workpiece material performance parameters, to accurately predict cutting performance parameters such as cutting force, cutting temperature, tool wear, etc. during the cutting process, and provides data support for subsequent spatiotemporal dynamic modeling of cutting force and cutting parameter optimization. By analyzing the time series change and spatial law of cutting force, the dynamic change characteristics of cutting force during the cutting process are deeply understood, providing a basis for dynamic modeling and optimization of the cutting process; combining the workpiece surface perception data with the cutting performance prediction data for analysis, the changing law of cutting force under different workpiece surface conditions is revealed, providing more comprehensive data support for subsequent spatiotemporal dynamic modeling of cutting force. Through dynamic modeling of time series change and spatial law data, a spatiotemporal dynamic model of cutting force is formed, which can comprehensively describe the changing trend and law of cutting force during the cutting process, and provide a basis for subsequent cutting heat conduction analysis and stable cutting domain model construction. The spatiotemporal dynamic model of cutting force is used to perform cutting heat conduction analysis and cutting vibration analysis, revealing the heat release and vibration during the cutting process, and providing a basis for the stability analysis of the tool and the workpiece; a stable cutting domain model is constructed based on the spatiotemporal dynamic model of cutting force, and the stable range of cutting parameters is determined, providing guidance for subsequent optimal cutting parameter exploration. The stable range of cutting parameters is defined according to the stable cutting domain model to avoid poor processing quality or equipment damage caused by selecting cutting parameters within the unstable range. The stable cutting domain exploration model is constructed using machine learning technology to automatically explore the optimal cutting parameter combination and improve the efficiency and stability of the cutting process.

[0032] Preferably, step S24 comprises the following steps:

[0033] Step S241: performing cutting temperature analysis according to the cutting force spatiotemporal dynamic model and cutting performance prediction data to obtain a cutting temperature distribution diagram;

[0034] Step S242: Acquire workpiece required data; perform cutting temperature control processing according to the cutting temperature distribution map and the workpiece required data to obtain cutting temperature control parameters; perform heat conduction path analysis according to the cutting temperature distribution map and the cutting temperature control parameters to obtain heat conduction path data;

[0035] Step S243: performing thermal deformation analysis according to the heat conduction path data to obtain workpiece thermal deformation data; performing energy consumption analysis according to the cutting temperature distribution diagram and the cutting temperature control parameters to obtain cutting energy-saving parameters;

[0036] Step S244: performing cutting vibration analysis based on the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting vibration data; performing vibration modal analysis on the cutting vibration data to obtain vibration frequency data and stable cutting data;

[0037] Step S245: construct a stable cutting domain model based on the cutting force spatiotemporal dynamic model, the cutting temperature distribution diagram, the workpiece thermal deformation data, the cutting energy-saving parameters, the vibration frequency data and the stable cutting data to obtain a stable cutting domain model.

[0038] The present invention can obtain the temperature distribution diagram of the workpiece during the cutting process through the cutting force spatiotemporal dynamic model and cutting performance prediction data, and intuitively display the temperature change of the cutting area; according to the cutting temperature distribution diagram, the high temperature area generated during the cutting process can be analyzed to provide a basis for subsequent temperature control and thermal deformation analysis. According to the workpiece requirement data and the cutting temperature distribution diagram, the cutting temperature control parameters are determined to ensure that the temperature range that the workpiece can withstand is not exceeded during the cutting process to ensure the processing quality; through the heat conduction path analysis, the heat transfer and distribution in the workpiece are understood to provide data support for thermal deformation analysis and energy consumption analysis. Thermal deformation analysis is performed based on the heat conduction path data to understand the deformation of the workpiece caused by thermal expansion during the cutting process, providing a basis for process optimization and workpiece shape accuracy control; energy consumption analysis is performed based on the cutting temperature distribution diagram and the cutting temperature control parameters to find out the energy consumption focus during the cutting process, and cutting energy-saving parameters are proposed to reduce the energy consumption cost during the processing process. According to the spatiotemporal dynamic model of cutting force, cutting vibration analysis is performed to understand the vibration conditions that may occur during the cutting process, providing data support for the construction of a stable cutting domain model; stable cutting data is obtained through vibration modal analysis and vibration frequency data, and the stable process parameter range during the cutting process is determined to ensure processing quality and equipment stability. The spatiotemporal dynamic model of cutting force, cutting temperature distribution diagram, workpiece thermal deformation data, cutting energy-saving parameters, vibration frequency data, and stable cutting data are combined to construct a stable cutting domain model to achieve a comprehensive analysis of all aspects of the cutting process; through the stable cutting domain model, the stable range of cutting parameters is determined to provide guidance for achieving a stable processing process and optimized processing results.

[0039] Preferably, step S3 comprises the following steps:

[0040] Step S31: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters;

[0041] Step S32: adjusting the dynamic machining path according to the workpiece surface curvature data, the cutting force spatiotemporal dynamic model and the surface quality optimization parameters to obtain dynamic machining path adjustment data;

[0042] Step S33: extracting path deviation control parameters according to the dynamic processing path adjustment data to obtain path deviation control parameters; marking key processing areas according to the surface quality optimization parameters to obtain key processing area data; and sorting the key processing area data by path priority to obtain path priority data;

[0043] Step S34: generating a dynamic machining path based on the dynamic machining path adjustment data, the path deviation control parameter and the path priority data to obtain dynamic machining path data;

[0044] Step S35: using the tool cutting parameters to perform adaptive machining path planning on the dynamic machining path data to obtain adaptive machining path data.

[0045] The present invention extracts surface quality optimization parameters through surface perception data, and can adjust cutting parameters according to the actual surface conditions of the workpiece, thereby optimizing the processing process, improving the surface quality and precision of the workpiece, and reducing surface defects and defects in the processing process. The surface curvature data of the workpiece and the spatiotemporal dynamic model of the cutting force are used, and the surface quality optimization parameters are combined to perform dynamic processing path adjustment, and the processing path can be adjusted according to the actual situation of the workpiece and the change of the cutting force, thereby improving the processing efficiency and the quality of the workpiece. According to the dynamic processing path adjustment data, the path deviation control parameters are extracted to ensure the accuracy and stability of the processing path, and avoid the processing errors caused by the path deviation; key processing area marking and path priority sorting: by marking the key processing area and performing path priority sorting, the key areas of the workpiece can be processed preferentially, the processing accuracy and surface quality of the key areas can be improved, and the overall processing efficiency can be improved. A dynamic processing path is generated according to the dynamic processing path adjustment data, the path deviation control parameters and the path priority data, to ensure the dynamicity and stability of the processing path, and to improve the processing efficiency and processing quality. According to the tool cutting parameters, the dynamic processing path data is adaptively planned, and the processing path can be dynamically adjusted according to the cutting conditions and the characteristics of the workpiece, so as to realize adaptive processing and improve the processing efficiency and processing quality.

[0046] The present invention can decompose a complex dynamic processing path into multiple sections through path segmentation and segment optimization, and optimize each section according to the tool cutting parameters to adapt to different cutting conditions and workpiece shapes, so as to improve processing accuracy and efficiency. A speed curve is generated according to the segment optimization data, and the acceleration upper limit data is determined under the condition of considering the acceleration limit, so as to realize the smooth control of speed and acceleration during the processing process, improve the processing quality and tool life. Through the smooth transition between path segments and real-time processing state monitoring, the smooth transition and real-time control of the processing path can be guaranteed, the impact and vibration during the processing process can be avoided, and the processing quality and stability can be improved. By calculating the cutting speed adjustment amount in real time and performing speed adjustment, the cutting speed can be adjusted in real time according to the cutting conditions and processing state, and the stability and cutting quality of the cutting process can be ensured. By performing adaptive path planning based on optimization data and speed control data, the processing path can be dynamically adjusted according to the real-time processing situation and cutting conditions, the processing efficiency and processing quality can be improved, and the stability and controllability of the processing process can be ensured.

[0047] Preferably, step S4 comprises the following steps:

[0048] Step S41: performing cutting simulation and virtual collision detection according to the adaptive machining path data to obtain cutting simulation and collision simulation data;

[0049] Step S42: performing real-time processing perception on the cutting simulation and collision simulation data through sensors to obtain real-time processing perception data; performing collision prediction on the cutting simulation and collision simulation data and the real-time processing perception data to obtain collision prediction data;

[0050] Step S43: formulating a dynamic path correction strategy according to the collision prediction data and the real-time processing perception data to obtain a dynamic path correction strategy;

[0051] Step S44: extracting the tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data;

[0052] Step S45: using the dynamic path correction strategy and the tool correction amount data to perform tool radius correction on the adaptive machining path data to obtain adaptive machining correction path data.

[0053] In the embodiment of the present invention, by cutting simulation, the processing process can be verified and evaluated before actual processing, and potential processing problems and optimization space can be discovered in advance, thereby reducing the number of processing trial and error; by using virtual collision detection, possible collision problems in the processing process can be discovered, equipment damage and processing quality problems can be prevented in advance, and processing safety and stability can be improved. The state of the processing process, including cutting conditions, equipment operation status, etc., can be sensed in real time by sensors, providing an accurate data basis for subsequent dynamic correction and prediction; by using real-time perception data for collision prediction, possible collision risks can be discovered in advance, and timely measures can be taken to avoid the occurrence of collision events, thereby ensuring the safety and stability of the processing process. Based on collision prediction and real-time perception data, a dynamic path correction strategy is formulated, and the processing path can be adjusted in real time during the processing process to avoid collisions and improve processing accuracy; by dynamic path correction, processing interruptions and pause times can be reduced, and processing efficiency and productivity can be improved. By extracting the tool radius correction amount according to the dynamic path correction strategy, accurate control of the tool trajectory can be achieved to ensure the accuracy of the cutting depth and processing trajectory; by extracting the tool radius correction amount in real time, the impact of tool wear can be compensated in time, the tool life can be extended, and the processing quality can be maintained. Adaptive correction of the machining path based on dynamic path correction strategy and tool correction data can achieve real-time optimization and adjustment of the machining path, improve machining accuracy and stability; through adaptive machining correction path, errors and repeated machining times in machining can be reduced, reducing machining costs and resource consumption.

[0054] The present invention can fully understand the shape, characteristics and curvature changes of the workpiece through surface perception data, and provide an important basis for subsequent processing path planning, cutting parameter optimization and surface quality improvement. The construction of the spatiotemporal dynamic model of cutting force and the cutting impact analysis can help optimize the use parameters of the tool, improve cutting efficiency and tool life; the exploration of the stable cutting domain model helps to determine safe and stable cutting conditions, reduce vibration and thermal deformation during processing, and improve processing accuracy and surface quality. The extraction of surface quality optimization parameters helps to optimize the surface quality during processing; dynamic processing path planning and adaptive processing path planning can dynamically adjust the processing path according to the real-time shape and cutting conditions of the workpiece, and improve processing efficiency and accuracy. Dynamic path correction and tool radius correction can adjust the processing path and tool parameters according to real-time conditions during processing, improve processing accuracy and stability, and reduce processing errors and damage. The analysis of multi-axis motion data and synchronous motion path compensation can realize the precise control and optimization of the CNC machine tool processing process, improve processing efficiency and finished product quality; the generation of CNC code is based on the optimized processing path data, which can accurately guide the CNC machine tool to perform processing operations, ensure that the processing process is carried out as expected, reduce human errors and processing time, and improve production efficiency. Therefore, the present invention provides a method for planning a machining path for a CNC machine tool. By analyzing the dynamic changes between the tool and the workpiece and the shape of the workpiece during the cutting process, the workpiece surface perception data and the tool cutting parameters are obtained to plan an adaptive machining path. The adaptive path planning can dynamically adjust the machining path according to actual conditions to adapt to the changing workpiece shape or surface conditions; the local difference in tool radius is corrected on the adaptively planned path to improve the machining quality and efficiency as well as the machining accuracy and surface quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic flow chart of the steps of a method for planning a machining path for a CNC machine tool;

[0056] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0057] Figure 3 for Figure 2 Detailed implementation steps of step S24;

[0058] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0059] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0060] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0062] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0063] To achieve this, please refer to Figures 1 to 4 , a method for planning a machining path of a CNC machine tool, comprising the following steps:

[0064] Step S1: Scanning the workpiece surface by a laser scanner to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing workpiece surface sensing according to the workpiece surface scanning data and the workpiece surface curvature data to obtain workpiece surface sensing data;

[0065] Step S2: construct a spatiotemporal dynamic model of cutting force based on the workpiece surface scanning data and the workpiece surface sensing data to obtain the spatiotemporal dynamic model of cutting force; perform spatiotemporal influence analysis of cutting force based on the spatiotemporal dynamic model of cutting force to obtain cutting heat energy and vibration data; explore a stable cutting domain model based on the spatiotemporal dynamic model of cutting force and the cutting heat energy and vibration data; and obtain tool cutting parameters;

[0066] Step S3: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters; performing dynamic machining path planning according to the workpiece surface curvature data and the cutting force spatiotemporal dynamic model to obtain dynamic machining path data; performing adaptive machining path planning on the dynamic machining path data using tool cutting parameters to obtain adaptive machining path data;

[0067] Step S4: performing dynamic path correction according to the adaptive machining path data; obtaining a dynamic path correction strategy; extracting a tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data; performing tool radius correction on the adaptive machining path data using the dynamic path correction strategy and the tool correction amount data to obtain adaptive machining correction path data;

[0068] Step S5: Perform multi-axis motion analysis on the CNC machine tool to obtain multi-axis motion data; perform synchronous motion path compensation based on the multi-axis motion data to obtain CNC machine tool processing path data; generate CNC code based on the CNC machine tool processing path data to obtain CNC code data.

[0069] The present invention can fully understand the shape, characteristics and curvature changes of the workpiece through surface perception data, and provide an important basis for subsequent processing path planning, cutting parameter optimization and surface quality improvement. The construction of the spatiotemporal dynamic model of cutting force and the cutting impact analysis can help optimize the use parameters of the tool, improve cutting efficiency and tool life; the exploration of the stable cutting domain model helps to determine safe and stable cutting conditions, reduce vibration and thermal deformation during processing, and improve processing accuracy and surface quality. The extraction of surface quality optimization parameters helps to optimize the surface quality during processing; dynamic processing path planning and adaptive processing path planning can dynamically adjust the processing path according to the real-time shape and cutting conditions of the workpiece, and improve processing efficiency and accuracy. Dynamic path correction and tool radius correction can adjust the processing path and tool parameters according to real-time conditions during processing, improve processing accuracy and stability, and reduce processing errors and damage. The analysis of multi-axis motion data and synchronous motion path compensation can realize the precise control and optimization of the CNC machine tool processing process, improve processing efficiency and finished product quality; the generation of CNC code is based on the optimized processing path data, which can accurately guide the CNC machine tool to perform processing operations, ensure that the processing process is carried out as expected, reduce human errors and processing time, and improve production efficiency. Therefore, the present invention provides a method for planning a machining path for a CNC machine tool. By analyzing the dynamic changes between the tool and the workpiece and the shape of the workpiece during the cutting process, the workpiece surface perception data and the tool cutting parameters are obtained to plan an adaptive machining path. The adaptive path planning can dynamically adjust the machining path according to actual conditions to adapt to the changing workpiece shape or surface conditions; the local difference in tool radius is corrected on the adaptively planned path to improve the machining quality and efficiency as well as the machining accuracy and surface quality.

[0070] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of the method for planning a machining path of a CNC machine tool of the present invention. In this example, the method for planning a machining path of a CNC machine tool includes the following steps:

[0071] Step S1: Scanning the workpiece surface by a laser scanner to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing workpiece surface sensing according to the workpiece surface scanning data and the workpiece surface curvature data to obtain workpiece surface sensing data;

[0072] In an embodiment of the present invention, a laser scanner is moved along the surface of a workpiece to obtain three-dimensional point cloud data. Use professional three-dimensional data processing software to import the point cloud data obtained by laser scanning; perform topological analysis on the point cloud data to extract surface feature points, edges, bumps and other information; generate the topological structure of the workpiece surface based on the extracted features, including curved surfaces, boundaries, etc. In the three-dimensional data processing software, the curvature of the workpiece surface is calculated using the topological analysis results; the curvature data may include information such as the principal curvature and the average curvature, which is used to describe the curvature characteristics of the workpiece surface. Combine the workpiece surface scanning data and the curvature data to perform workpiece surface perception analysis.

[0073] Step S2: construct a spatiotemporal dynamic model of cutting force based on the workpiece surface scanning data and the workpiece surface sensing data to obtain the spatiotemporal dynamic model of cutting force; perform spatiotemporal influence analysis of cutting force based on the spatiotemporal dynamic model of cutting force to obtain cutting heat energy and vibration data; explore a stable cutting domain model based on the spatiotemporal dynamic model of cutting force and the cutting heat energy and vibration data; and obtain tool cutting parameters;

[0074] In an embodiment of the present invention, a cutting force model with spatiotemporal dynamic characteristics is constructed by taking into account possible changes that may occur during the cutting process, such as changes in cutting depth and cutting speed. Based on the spatiotemporal dynamic model of cutting force, a spatiotemporal impact analysis of cutting force is performed to predict the cutting heat energy and vibration data generated during the cutting process; the heat and vibration that may be generated during the cutting process are analyzed by taking into account factors such as the contact between the tool and the workpiece, friction and wear during the cutting process. Based on the spatiotemporal dynamic model of cutting force and the cutting heat energy and vibration data, a stable cutting domain model is explored; the range of cutting parameters is determined so that the cutting process remains stable without excessive vibration or overheating. The optimal tool cutting parameters are determined through simulation or experimental verification to ensure the stability of the cutting process and the processing quality.

[0075] Step S3: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters; performing dynamic machining path planning according to the workpiece surface curvature data and the cutting force spatiotemporal dynamic model to obtain dynamic machining path data; performing adaptive machining path planning on the dynamic machining path data using tool cutting parameters to obtain adaptive machining path data;

[0076] In an embodiment of the present invention, the workpiece surface perception data is used to analyze the relevant parameters of the surface quality, such as surface roughness, flatness, and curvature change; the surface quality optimization parameters are extracted according to the analysis results, such as setting the target surface roughness, flatness and other indicators. Dynamic processing path planning is performed in combination with the workpiece surface curvature data and the spatiotemporal dynamic model of the cutting force; the feed direction and depth of the tool are adjusted to achieve the purpose of optimizing the surface quality by considering the influence of the cutting force on the processing path; preliminary dynamic processing path data are generated, including the movement trajectory of the tool and the order of the processing points. According to the tool cutting parameters, the preliminary generated dynamic processing path data is adaptively planned; the processing path is adjusted to achieve the best processing effect by considering the cutting speed, feed speed, cutting depth and other parameters of the tool; the final adaptive processing path data is generated to ensure that the tool movement during the processing meets the expected surface quality optimization requirements. .

[0077] Step S4: performing dynamic path correction according to the adaptive machining path data; obtaining a dynamic path correction strategy; extracting a tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data; performing tool radius correction on the adaptive machining path data using the dynamic path correction strategy and the tool correction amount data to obtain adaptive machining correction path data;

[0078] In the embodiment of the present invention, the possible errors or uncertainties in the processing path are first checked; the actual shape and surface feature changes of the workpiece during the processing are monitored using real-time sensor data or advanced visual systems; based on the actual conditions monitored, the adaptive processing path is dynamically adjusted and corrected to ensure that the processing path is consistent with the actual workpiece shape and surface conditions, thereby reducing processing errors. A dynamic path correction strategy is formulated according to the needs and actual conditions of dynamic path correction; the strategy may include adjusting parameters such as the speed, direction, and cutting depth of the processing path according to real-time monitoring data, and triggering local correction or overall adjustment of the path according to specific conditions. According to the dynamic path correction strategy, the situation and correction amount of the tool radius that need to be corrected are determined; by analyzing the real-time monitoring data or a pre-set correction algorithm, the tool radius correction amount data is extracted for subsequent processing correction. The tool radius is dynamically corrected for the adaptive processing path data; after the correction is implemented, the final adaptive processing correction path data is obtained, which is used to guide the CNC machine tool to perform processing operations. .

[0079] Step S5: performing multi-axis motion analysis on the CNC machine tool to obtain multi-axis motion data; performing synchronous motion path compensation according to the multi-axis motion data to obtain CNC machine tool processing path data; performing CNC code generation according to the CNC machine tool processing path data to obtain CNC code data;

[0080] In an embodiment of the present invention, the motion state data of each axis, including position, speed, acceleration and other information, is recorded by the sensor or encoder of the CNC machine tool; the relationship and motion law between the axes are analyzed using a kinematic model or a motion control system, taking into account the machine tool structure, motion range and motion constraints. According to the multi-axis motion data and the processing path requirements, the synchronous motion path is compensated to ensure the coordinated motion between the axes; the processing path data of the CNC machine tool is generated based on the data after the synchronous motion path compensation. Based on the processing path data of the CNC machine tool, the corresponding CNC code is generated using the CNC programming software.

[0081] Preferably, step S1 comprises the following steps:

[0082] Step S11: scanning the workpiece surface by a laser scanner to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing linear feature extraction on the workpiece surface curvature data to obtain workpiece surface linear feature data;

[0083] Step S12: performing local area segmentation according to the workpiece surface scanning data to obtain local area segmentation data; performing local area feature extraction on the local area segmentation data according to the workpiece surface curvature data to obtain local area feature data;

[0084] Step S13: performing surface shape modeling according to the local area feature data and the workpiece surface linear feature data to obtain the workpiece surface shape model data;

[0085] Step S14: performing surface defect anomaly analysis based on the workpiece surface linear feature data and the workpiece surface curvature data to obtain abnormal area marking data;

[0086] Step S15: topologically connecting the surface feature points according to the workpiece surface shape model data and the workpiece surface linear feature data to obtain a feature point topological map; topologically linking the local area according to the workpiece surface shape model data and the local area feature data to obtain a local area topological map; performing surface accessibility analysis according to the feature point connection data and the local area connection data to obtain a surface accessibility map;

[0087] Step S16: Integrate the workpiece surface perception data according to the workpiece surface shape model data, abnormal area marking data, feature point topology map, local area topology map and surface accessibility map to obtain the workpiece surface perception data.

[0088] The present invention obtains the scanning data of the workpiece surface through a laser scanner, and can realize high-precision acquisition of the workpiece surface shape, which is helpful for subsequent processing path planning and surface feature analysis; at the same time, surface topology analysis and curvature data extraction are performed to obtain the geometric feature information of the workpiece surface, and provide basic data for subsequent workpiece surface shape modeling and abnormal analysis. Through local area segmentation and feature extraction, the workpiece surface can be segmented into different local areas, and its feature information is extracted for each area; it is helpful to conduct in-depth analysis of the local features of the workpiece surface, such as surface unevenness, angular features, etc., to provide more detailed data support for subsequent surface shape modeling and defect abnormal analysis. Through surface shape modeling, the geometric shape of the workpiece surface can be described in the form of a mathematical model, thereby realizing an abstract representation of the workpiece surface; it is helpful for processing path planning and workpiece design evaluation, and improving processing accuracy and efficiency. Through abnormal analysis, defects and abnormal areas on the workpiece surface, such as cracks, bumps, etc., can be detected; it is helpful to discover quality problems on the workpiece surface in advance, repair or adjust processing parameters, and ensure processing quality. Through the topological connection of feature points and the topological link of local areas, the feature points or local areas on the workpiece surface can be connected to form a topological map structure, which helps to have a deeper understanding of the structure and correlation of the workpiece surface; at the same time, surface accessibility analysis can determine the accessibility relationship between different areas on the workpiece surface, providing a reference for subsequent processing path planning and process optimization. By integrating various data, comprehensive perception data of the workpiece surface can be obtained, including shape characteristics, defect anomalies, structural topology and other information; it helps to formulate more accurate processing path planning strategies, improve processing quality and efficiency, and provide important reference for workpiece design and quality control.

[0089] In the embodiment of the present invention, a laser scanner is used to scan the surface of the workpiece to be processed to obtain a large amount of discrete point cloud data; the point cloud data is processed and filtered to remove noise and invalid data to obtain clear workpiece surface scanning data; the surface topology analysis is performed on the workpiece surface scanning data, and the curvature data of the workpiece surface is obtained by calculating parameters such as normal and curvature; linear feature extraction is performed on the workpiece surface curvature data, such as the main curvature and the main curvature direction, to obtain the linear feature data of the workpiece surface; linear feature extraction is performed on the workpiece surface curvature data, such as the main curvature and the main curvature direction, to obtain the linear feature data of the workpiece surface. Local area segmentation is performed according to the workpiece surface scanning data, and the workpiece surface is segmented into different local areas using regional growth or curvature-based methods; local area feature extraction is performed on the local area segmentation data, including feature data such as curvature, normal direction, and surface flatness of the local area. A shape model of the workpiece surface is established using methods such as surface fitting or triangular mesh generation; and workpiece surface shape model data is obtained, including surface parameterized description or three-dimensional model data. According to the linear feature data and curvature data of the workpiece surface, the surface defect anomaly analysis is performed to detect the abnormal area on the workpiece surface; the abnormal area is marked and classified to obtain the abnormal area marking data. The topological connection of the feature points is performed to construct the feature point topological map; the topological connection of the local area is performed to obtain the local area topological map; the surface accessibility map is obtained by combining the feature point topological map, the local area topological map and the workpiece surface accessibility analysis. According to the workpiece surface shape model data, the abnormal area marking data, the feature point topological map, the local area topological map and the surface accessibility map, the workpiece surface perception data is integrated; after integration, the workpiece surface perception data is obtained, including the shape characteristics of the workpiece, the abnormal area information, the topological connection relationship and the accessibility analysis results.

[0090] Preferably, step S14 comprises the following steps:

[0091] Step S141: performing surface defect image detection on the linear feature data of the workpiece surface to obtain surface defect influence degree data; performing defect area image segmentation on the linear feature data of the workpiece surface to obtain defect area contour data;

[0092] Step S142: performing defect penetration depth analysis according to the defect area contour data to obtain defect penetration depth data; generating a defect thermal map according to the defect area contour data and the defect penetration depth data to obtain a defect thermal map;

[0093] Step S143: performing surface defect impact analysis based on the defect penetration depth data and the defect thermal map to obtain surface defect impact degree data; performing surface quality assessment based on the surface defect impact degree data and the workpiece surface curvature data to obtain surface quality index data;

[0094] Step S144: marking abnormal regions of the workpiece surface shape model data according to the surface quality index data to obtain abnormal region marking data.

[0095] The present invention can detect defects on the surface of the workpiece, such as scratches, bumps, etc., through surface defect image detection, obtain defect impact degree data, and understand the impact degree of defects on the quality of the workpiece; perform defect area image segmentation, separate the defect area from the normal area, obtain defect area contour data, and provide basic data for subsequent defect analysis. Through defect area contour data, defect penetration depth analysis can be performed to understand the depth information of the defect on the workpiece surface, obtain defect penetration depth data, and provide more accurate data support for evaluating the impact of defects; through the generation of defect heat maps, the distribution and degree of defects on the workpiece surface can be intuitively displayed, which is helpful for quantitative and qualitative analysis of defects. Through surface defect impact analysis, the penetration depth and distribution of defects can be comprehensively considered to obtain surface defect impact degree data to help evaluate the actual impact degree of defects on the quality of the workpiece; surface quality assessment based on surface defect impact degree data and workpiece surface curvature data can quantitatively evaluate the quality status of the workpiece surface and provide a basis for formulating subsequent quality improvement measures. By marking abnormal areas of the workpiece surface shape model data, the abnormal areas reflected by the surface quality index data can be marked on the model to facilitate subsequent quality improvement and repair work; the abnormal area marking data can guide operators or automation systems to perform corresponding processing to improve the quality and reliability of the workpiece surface.

[0096] In an embodiment of the present invention, an image processing algorithm is used to process the linear feature data of the workpiece surface, detect surface defects and quantify their influence, and simultaneously perform image segmentation of the defect area to obtain contour data of the defect area. The defect penetration depth is calculated using the defect area contour data, and a defect heat map is generated, which can intuitively display the degree and depth of defects in various areas of the workpiece surface. Combined with the defect penetration depth data and the defect heat map, the influence of surface defects on the workpiece quality is analyzed, and the surface quality is evaluated based on the workpiece surface curvature data to obtain surface quality index data. Abnormal areas on the workpiece surface are determined based on the surface quality index data, and these abnormal areas are marked to obtain abnormal area marking data for subsequent surface quality control and correction.

[0097] Preferably, step S2 comprises the following steps:

[0098] Step S21: analyzing the material characteristics of the workpiece to be processed according to the workpiece surface scanning data to obtain the workpiece material performance parameters; predicting the cutting performance using a preset cutting performance prediction model and the workpiece material performance parameters to obtain cutting performance prediction data;

[0099] Step S22: performing a cutting force time series change analysis based on the workpiece material performance parameters and the cutting performance prediction data to obtain cutting force time series change data; performing a cutting force spatial law analysis based on the workpiece surface perception data and the cutting performance prediction data to obtain cutting force spatial law data;

[0100] Step S23: performing spatiotemporal dynamic modeling of cutting force based on the cutting force time series variation data and the cutting force spatial regularity data to obtain a spatiotemporal dynamic model of cutting force;

[0101] Step S24: performing cutting heat conduction analysis and cutting vibration analysis according to the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting heat and vibration data; constructing a stable cutting domain model based on the cutting force spatiotemporal dynamic model and the cutting heat and vibration data to obtain a stable cutting domain model;

[0102] Step S25: Define the range of stable cutting parameters according to the stable cutting domain model to obtain stable cutting parameter range data; construct a stable cutting domain exploration model based on machine learning technology and the stable cutting domain model to obtain a stable cutting domain exploration model; explore the optimal cutting parameter combination according to the stable cutting domain exploration model and the stable cutting parameter range data to obtain the tool cutting parameters.

[0103] The present invention uses a preset cutting performance prediction model, combined with workpiece material performance parameters, to accurately predict cutting performance parameters such as cutting force, cutting temperature, tool wear, etc. during the cutting process, and provides data support for subsequent spatiotemporal dynamic modeling of cutting force and cutting parameter optimization. By analyzing the time series change and spatial law of cutting force, the dynamic change characteristics of cutting force during the cutting process are deeply understood, providing a basis for dynamic modeling and optimization of the cutting process; combining the workpiece surface perception data with the cutting performance prediction data for analysis, the changing law of cutting force under different workpiece surface conditions is revealed, providing more comprehensive data support for subsequent spatiotemporal dynamic modeling of cutting force. Through dynamic modeling of time series change and spatial law data, a spatiotemporal dynamic model of cutting force is formed, which can comprehensively describe the changing trend and law of cutting force during the cutting process, and provide a basis for subsequent cutting heat conduction analysis and stable cutting domain model construction. The spatiotemporal dynamic model of cutting force is used to perform cutting heat conduction analysis and cutting vibration analysis, revealing the heat release and vibration during the cutting process, and providing a basis for the stability analysis of the tool and the workpiece; a stable cutting domain model is constructed based on the spatiotemporal dynamic model of cutting force, and the stable range of cutting parameters is determined, providing guidance for subsequent optimal cutting parameter exploration. The stable range of cutting parameters is defined according to the stable cutting domain model to avoid poor processing quality or equipment damage caused by selecting cutting parameters within the unstable range. The stable cutting domain exploration model is constructed using machine learning technology to automatically explore the optimal cutting parameter combination and improve the efficiency and stability of the cutting process.

[0104] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0105] Step S21: analyzing the material characteristics of the workpiece to be processed according to the workpiece surface scanning data to obtain the workpiece material performance parameters; predicting the cutting performance using a preset cutting performance prediction model and the workpiece material performance parameters to obtain cutting performance prediction data;

[0106] In an embodiment of the present invention, material property analysis is performed on the workpiece surface scanning data; this includes analysis of the physical properties of the workpiece material, such as hardness, strength, and thermal conductivity; the sample can be tested by a metallographic microscope, a hardness tester, and other equipment to obtain material performance parameters. Cutting performance prediction is performed based on a pre-set cutting performance prediction model in combination with the workpiece material performance parameters; this model may be based on factors such as material mechanical properties, cutting process parameters, and tool characteristics, and is used to predict parameters such as cutting force, cutting temperature, and cutting surface roughness during the cutting process. Cutting performance prediction data is calculated and generated in combination with the workpiece material performance parameters and the cutting performance prediction model; these data may include information such as cutting force size, cutting temperature change, and cutting surface quality prediction.

[0107] Step S22: performing a cutting force time series change analysis based on the workpiece material performance parameters and the cutting performance prediction data to obtain cutting force time series change data; performing a cutting force spatial law analysis based on the workpiece surface perception data and the cutting performance prediction data to obtain cutting force spatial law data;

[0108] In the embodiment of the present invention, the expected cutting force variation curve is calculated based on the workpiece material performance parameters and the predicted cutting performance, including the magnitude, direction and change rate of the cutting force; the axial movement of different tools in the cutting process is considered, and the change of the cutting force over time under different working conditions is analyzed to obtain the cutting force time series change data. The workpiece surface perception data is converted into the actual working coordinate system in the cutting process; based on the predicted cutting performance data, combined with the working coordinate system, the magnitude and direction of the cutting force at different positions are analyzed to obtain the distribution law of the cutting force in space, that is, the cutting force space law data.

[0109] Step S23: performing spatiotemporal dynamic modeling of cutting force based on the cutting force time series variation data and the cutting force spatial regularity data to obtain a spatiotemporal dynamic model of cutting force;

[0110] In the embodiment of the present invention, the cutting force time series variation data and the cutting force spatial regularity data are used to construct the model; the structure and parameters of the model are adjusted according to the actual situation to obtain a more accurate cutting force spatiotemporal dynamic model. The constructed cutting force spatiotemporal dynamic model is verified and evaluated, and the model is trained using a partial data set, and then the model is verified using an unused data set to verify the prediction performance and generalization ability of the model.

[0111] Step S24: performing cutting heat conduction analysis and cutting vibration analysis according to the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting heat and vibration data; constructing a stable cutting domain model based on the cutting force spatiotemporal dynamic model and the cutting heat and vibration data to obtain a stable cutting domain model;

[0112] In the embodiment of the present invention, a spatiotemporal dynamic model of cutting force is used, combined with cutting performance prediction data, to conduct a conduction analysis of the heat energy generated during the cutting process; the instantaneous cutting force, cutting speed, cutting depth and other parameters during cutting are considered to calculate the heat energy conduction in the cutting area; the cutting vibration is analyzed, including the vibration frequency and amplitude during the cutting process; the vibration data is obtained through a vibration sensor or accelerometer, and the vibration characteristics are analyzed. The heat energy distribution, vibration conditions and spatiotemporal changes of the cutting force during the cutting process are considered, and the influence of these factors on the cutting stability is comprehensively considered to construct a stable cutting domain model. Using the stable cutting domain model, it can be determined under what cutting conditions (such as cutting speed, cutting depth, cutting force, etc.) the cutting process can remain stable, avoid excessive heat energy and vibration, and ensure processing quality and efficiency.

[0113] Step S25: Define the range of stable cutting parameters according to the stable cutting domain model to obtain stable cutting parameter range data; construct a stable cutting domain exploration model based on machine learning technology and the stable cutting domain model to obtain a stable cutting domain exploration model; explore the optimal cutting parameter combination according to the stable cutting domain exploration model and the stable cutting parameter range data to obtain the tool cutting parameters.

[0114] In an embodiment of the present invention, a stable cutting domain model is used to define the range of stable cutting parameters based on factors such as cutting force, cutting heat, and vibration; for example, based on model analysis, the minimum and maximum ranges of parameters such as cutting speed, feed speed, and cutting depth are determined. Based on machine learning techniques, such as neural networks and decision trees, combined with the knowledge of the stable cutting domain model, a stable cutting domain exploration model is constructed; historical data and simulation data are used to train the model to predict stable cutting parameter combinations. Based on the stable cutting domain exploration model and the stable cutting parameter range data, the optimal cutting parameter combination is explored; through model prediction or optimization algorithm search, the most suitable cutting parameter combination for the current workpiece and processing conditions is found; the optimal cutting speed, feed speed, cutting depth, and other parameter combinations are obtained.

[0115] Preferably, step S24 comprises the following steps:

[0116] Step S241: performing cutting temperature analysis according to the cutting force spatiotemporal dynamic model and cutting performance prediction data to obtain a cutting temperature distribution diagram;

[0117] Step S242: Acquire workpiece required data; perform cutting temperature control processing according to the cutting temperature distribution map and the workpiece required data to obtain cutting temperature control parameters; perform heat conduction path analysis according to the cutting temperature distribution map and the cutting temperature control parameters to obtain heat conduction path data;

[0118] Step S243: performing thermal deformation analysis according to the heat conduction path data to obtain workpiece thermal deformation data; performing energy consumption analysis according to the cutting temperature distribution diagram and the cutting temperature control parameters to obtain cutting energy-saving parameters;

[0119] Step S244: performing cutting vibration analysis based on the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting vibration data; performing vibration modal analysis on the cutting vibration data to obtain vibration frequency data and stable cutting data;

[0120] Step S245: construct a stable cutting domain model based on the cutting force spatiotemporal dynamic model, the cutting temperature distribution diagram, the workpiece thermal deformation data, the cutting energy-saving parameters, the vibration frequency data and the stable cutting data to obtain a stable cutting domain model.

[0121] The present invention can obtain the temperature distribution diagram of the workpiece during the cutting process through the cutting force spatiotemporal dynamic model and cutting performance prediction data, and intuitively display the temperature change of the cutting area; according to the cutting temperature distribution diagram, the high temperature area generated during the cutting process can be analyzed to provide a basis for subsequent temperature control and thermal deformation analysis. According to the workpiece requirement data and the cutting temperature distribution diagram, the cutting temperature control parameters are determined to ensure that the temperature range that the workpiece can withstand is not exceeded during the cutting process to ensure the processing quality; through the heat conduction path analysis, the heat transfer and distribution in the workpiece are understood to provide data support for thermal deformation analysis and energy consumption analysis. Thermal deformation analysis is performed based on the heat conduction path data to understand the deformation of the workpiece caused by thermal expansion during the cutting process, providing a basis for process optimization and workpiece shape accuracy control; energy consumption analysis is performed based on the cutting temperature distribution diagram and the cutting temperature control parameters to find out the energy consumption focus during the cutting process, and cutting energy-saving parameters are proposed to reduce the energy consumption cost during the processing process. According to the spatiotemporal dynamic model of cutting force, cutting vibration analysis is performed to understand the vibration conditions that may occur during the cutting process, providing data support for the construction of a stable cutting domain model; stable cutting data is obtained through vibration modal analysis and vibration frequency data, and the stable process parameter range during the cutting process is determined to ensure processing quality and equipment stability. The spatiotemporal dynamic model of cutting force, cutting temperature distribution diagram, workpiece thermal deformation data, cutting energy-saving parameters, vibration frequency data, and stable cutting data are combined to construct a stable cutting domain model to achieve a comprehensive analysis of all aspects of the cutting process; through the stable cutting domain model, the stable range of cutting parameters is determined to provide guidance for achieving a stable processing process and optimized processing results.

[0122] As an example of the present invention, refer to Figure 3 As shown, in this example, step S24 includes:

[0123] Step S241: performing cutting temperature analysis according to the cutting force spatiotemporal dynamic model and cutting performance prediction data to obtain a cutting temperature distribution diagram;

[0124] In the embodiment of the present invention, a cutting temperature model is established based on a cutting force spatiotemporal dynamic model and a cutting performance prediction model; a mathematical modeling method, such as finite element analysis, computational fluid dynamics (CFD) simulation, etc., can be used to take cutting parameters and cutting force as input to predict the temperature distribution during the cutting process. A simulation or experimental test of the cutting temperature is performed to obtain cutting temperature data; numerical simulation is used to input the model parameters and input data into a computing software for simulation. A cutting temperature distribution diagram is obtained based on the simulation or experimental results.

[0125] Step S242: Acquire workpiece required data; perform cutting temperature control processing according to the cutting temperature distribution map and the workpiece required data to obtain cutting temperature control parameters; perform heat conduction path analysis according to the cutting temperature distribution map and the cutting temperature control parameters to obtain heat conduction path data;

[0126] In the embodiment of the present invention, workpiece requirement data is obtained, including requirements for cutting temperature, such as maximum allowable temperature, temperature uniformity requirements, etc. Cutting temperature control processing is performed according to the cutting temperature distribution diagram and workpiece requirement data, and cutting temperature control parameters are determined; for example, if the workpiece requires the cutting temperature not to exceed 200°C and requires good temperature uniformity, the cutting speed, cutting depth and other parameters can be adjusted to control the cutting temperature.

[0127] According to the cutting temperature distribution diagram and cutting temperature control parameters, the heat conduction path analysis is carried out to obtain the heat conduction path data; through numerical simulation or experimental testing, the heat conduction path and conduction speed of the heat in the cutting process are analyzed; for example, the heat conduction path and thermal conductivity coefficient in the workpiece, as well as factors such as the surface area in contact with the coolant during the cutting process are determined.

[0128] Step S243: performing thermal deformation analysis according to the heat conduction path data to obtain workpiece thermal deformation data; performing energy consumption analysis according to the cutting temperature distribution diagram and the cutting temperature control parameters to obtain cutting energy-saving parameters;

[0129] In the embodiment of the present invention, the engineering simulation method such as finite element analysis is used to calculate the thermal impact on the workpiece during the cutting process and analyze the thermal deformation; the workpiece thermal deformation data is obtained, including deformation amount, deformation shape, deformation position and other information. The energy consumption analysis is performed using the cutting temperature distribution diagram and the cutting temperature control parameters; the energy consumption required in the cutting process is estimated according to the cutting temperature control parameters, including electrical energy, thermal energy and other aspects; the influencing factors of energy consumption, such as the influence of cutting speed, cutting depth, cutting material and the like on energy consumption are analyzed; the cutting energy-saving parameters are obtained, that is, the measures and parameters for optimizing energy utilization and reducing energy consumption.

[0130] Step S244: performing cutting vibration analysis based on the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting vibration data; performing vibration modal analysis on the cutting vibration data to obtain vibration frequency data and stable cutting data;

[0131] In the embodiment of the present invention, numerical simulation or experimental test methods are used to obtain vibration data during the cutting process, including information such as vibration amplitude and frequency, to obtain cutting vibration data. Vibration modal analysis is performed on the cutting vibration data; signal processing technology or modal analysis software is used to process the vibration data to extract the modal characteristics of vibration; and information such as the modal frequency and modal shape of vibration is analyzed to obtain vibration frequency data and stable cutting data.

[0132] Step S245: constructing a stable cutting domain model based on the cutting force spatiotemporal dynamic model, the cutting temperature distribution diagram, the workpiece thermal deformation data, the cutting energy-saving parameters, the vibration frequency data and the stable cutting data to obtain a stable cutting domain model;

[0133] In an embodiment of the present invention, stable cutting conditions during the cutting process are determined, such as the optimal cutting speed, feed speed, cutting depth, etc. According to the cutting temperature control parameters, the temperature control strategy during the cutting process is determined, such as the use of coolant, the cooling method of the tool, etc. The thermal deformation data of the workpiece is used to analyze the thermal deformation of the workpiece during the cutting process, including the expansion, contraction, and distortion of the workpiece; the influence of the thermal deformation of the workpiece on the processing accuracy and shape is evaluated by combining the spatiotemporal dynamic model of the cutting force and the cutting temperature distribution diagram. According to the cutting energy-saving parameters, the energy consumption during the cutting process is analyzed, including the cutting energy consumption, the degree of tool wear, etc.; energy-saving optimization suggestions are put forward, such as reasonably selecting cutting parameters and optimizing cutting paths, so as to reduce energy consumption and improve processing efficiency. Vibration modal analysis is performed using vibration frequency data to understand the vibration problems that may exist during the cutting process; according to the stable cutting data and the vibration frequency analysis results, the vibration control strategy under stable cutting conditions is determined, such as tool damping, workpiece clamping method optimization, etc. The above-obtained data and analysis results are integrated; based on comprehensive analysis and modeling, a stable cutting domain model is constructed, which contains information such as stable cutting parameter range, temperature control strategy, thermal deformation prediction, energy-saving optimization suggestions, and vibration control strategy.

[0134] Preferably, step S3 comprises the following steps:

[0135] Step S31: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters;

[0136] Step S32: adjusting the dynamic machining path according to the workpiece surface curvature data, the cutting force spatiotemporal dynamic model and the surface quality optimization parameters to obtain dynamic machining path adjustment data;

[0137] Step S33: extracting path deviation control parameters according to the dynamic processing path adjustment data to obtain path deviation control parameters; marking key processing areas according to the surface quality optimization parameters to obtain key processing area data; and sorting the key processing area data by path priority to obtain path priority data;

[0138] Step S34: generating a dynamic machining path based on the dynamic machining path adjustment data, the path deviation control parameter and the path priority data to obtain dynamic machining path data;

[0139] Step S35: using the tool cutting parameters to perform adaptive machining path planning on the dynamic machining path data to obtain adaptive machining path data.

[0140] The present invention extracts surface quality optimization parameters through surface perception data, and can adjust cutting parameters according to the actual surface conditions of the workpiece, thereby optimizing the processing process, improving the surface quality and precision of the workpiece, and reducing surface defects and defects in the processing process. The surface curvature data of the workpiece and the spatiotemporal dynamic model of the cutting force are used, and the surface quality optimization parameters are combined to perform dynamic processing path adjustment, and the processing path can be adjusted according to the actual situation of the workpiece and the change of the cutting force, thereby improving the processing efficiency and the quality of the workpiece. According to the dynamic processing path adjustment data, the path deviation control parameters are extracted to ensure the accuracy and stability of the processing path, and avoid the processing errors caused by the path deviation; key processing area marking and path priority sorting: by marking the key processing area and performing path priority sorting, the key areas of the workpiece can be processed preferentially, the processing accuracy and surface quality of the key areas can be improved, and the overall processing efficiency can be improved. A dynamic processing path is generated according to the dynamic processing path adjustment data, the path deviation control parameters and the path priority data, to ensure the dynamicity and stability of the processing path, and to improve the processing efficiency and processing quality. According to the tool cutting parameters, the dynamic processing path data is adaptively planned, and the processing path can be dynamically adjusted according to the cutting conditions and the characteristics of the workpiece, so as to realize adaptive processing and improve the processing efficiency and processing quality.

[0141] In an embodiment of the present invention, the surface curvature data of the workpiece and the spatiotemporal dynamic model of the cutting force are used in combination with the surface quality optimization parameters to determine the dynamic processing path adjustment strategy; the initial processing path is adjusted according to the strategy to ensure that the processing path meets the surface quality requirements. According to the dynamic processing path adjustment data, the path deviation control parameters are extracted, such as the adjustment speed, processing depth, etc.; according to the surface quality optimization parameters, the key processing areas are marked on the surface of the workpiece to determine the areas that need to be focused on and prioritized for processing; the path priority of the key processing areas is sorted to determine the processing priority of different areas. According to the dynamic processing path adjustment data, the path deviation control parameters and the path priority data, the dynamic processing path is generated; considering the path deviation control and processing priority, the processing path is adjusted and sorted to ensure the optimization of the workpiece surface quality and processing efficiency. In combination with the tool cutting parameters, the dynamic processing path data is adaptively planned for the processing path; according to the cutting characteristics of the tool, the processing speed and other factors, the processing path is optimized to ensure a stable and efficient cutting process.

[0142] Preferably, step S3 comprises the following steps:

[0143] Step S351: segmenting the dynamic processing path to obtain processing path segment data; optimizing the processing path segment data by segment using tool cutting parameters to obtain processing path segment optimization data;

[0144] Step S352: Generate a speed curve according to the segmented optimization data of the processing path to obtain path speed curve data; perform acceleration limit processing according to the path speed curve data to obtain acceleration upper limit data;

[0145] Step S353: performing smooth transition between path segments according to the acceleration upper limit data to obtain tool processing path optimization data; performing real-time processing state monitoring according to the tool processing path optimization data to obtain real-time processing state data;

[0146] Step S354: performing speed control calculation according to the tool processing path optimization data, the tool cutting parameters and the real-time processing status data to obtain a cutting speed adjustment amount; performing speed adjustment using the cutting speed adjustment amount to obtain cutting speed control data;

[0147] Step S355: Perform adaptive path planning based on the tool processing path optimization data and the cutting speed control data to obtain adaptive processing path data.

[0148] The present invention can decompose a complex dynamic processing path into multiple sections through path segmentation and segment optimization, and optimize each section according to the tool cutting parameters to adapt to different cutting conditions and workpiece shapes, so as to improve processing accuracy and efficiency. A speed curve is generated according to the segment optimization data, and the acceleration upper limit data is determined under the condition of considering the acceleration limit, so as to realize the smooth control of speed and acceleration during the processing process, improve the processing quality and tool life. Through the smooth transition between path segments and real-time processing state monitoring, the smooth transition and real-time control of the processing path can be guaranteed, the impact and vibration during the processing process can be avoided, and the processing quality and stability can be improved. By calculating the cutting speed adjustment amount in real time and performing speed adjustment, the cutting speed can be adjusted in real time according to the cutting conditions and processing state, and the stability and cutting quality of the cutting process can be ensured. By performing adaptive path planning based on optimization data and speed control data, the processing path can be dynamically adjusted according to the real-time processing situation and cutting conditions, the processing efficiency and processing quality can be improved, and the stability and controllability of the processing process can be ensured.

[0149] In the embodiment of the present invention, the dynamic processing path is segmented into a plurality of processing path segment data; each processing path segment is segmented and optimized according to the tool cutting parameters, and the speed and depth of the processing path are adjusted to obtain the processing path segment optimization data. According to the processing path segment optimization data, a path speed curve is generated to determine the processing speed of each path segment; according to the path speed curve data, acceleration limitation processing is performed to determine the acceleration upper limit of each path segment to ensure the smooth progress of the processing process. According to the acceleration upper limit data, a smooth transition is performed between the path segments to ensure that there is no sudden change in the tool during the conversion process between the path segments to ensure the processing quality; according to the tool processing path optimization data, real-time processing status monitoring is performed to monitor the real-time data such as the position and speed of the tool to ensure the stability and safety of the processing process. According to the tool processing path optimization data, the tool cutting parameters and the real-time processing status data, speed control calculation is performed to determine the cutting speed adjustment amount; according to the cutting speed adjustment amount, speed adjustment is performed to adjust the cutting speed of the tool to obtain the cutting speed control data. Based on the tool processing path optimization data and the cutting speed control data, adaptive path planning is performed; according to the cutting speed control data, the processing path is optimized to ensure the stability and efficiency of the cutting speed and the processing process.

[0150] Preferably, step S4 comprises the following steps:

[0151] Step S41: performing cutting simulation and virtual collision detection according to the adaptive machining path data to obtain cutting simulation and collision simulation data;

[0152] Step S42: performing real-time processing perception on the cutting simulation and collision simulation data through sensors to obtain real-time processing perception data; performing collision prediction on the cutting simulation and collision simulation data and the real-time processing perception data to obtain collision prediction data;

[0153] Step S43: formulating a dynamic path correction strategy according to the collision prediction data and the real-time processing perception data to obtain a dynamic path correction strategy;

[0154] Step S44: extracting the tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data;

[0155] Step S45: using the dynamic path correction strategy and the tool correction amount data to perform tool radius correction on the adaptive machining path data to obtain adaptive machining correction path data.

[0156] In the embodiment of the present invention, by cutting simulation, the processing process can be verified and evaluated before actual processing, and potential processing problems and optimization space can be discovered in advance, thereby reducing the number of processing trial and error; by using virtual collision detection, possible collision problems in the processing process can be discovered, equipment damage and processing quality problems can be prevented in advance, and processing safety and stability can be improved. The state of the processing process, including cutting conditions, equipment operation status, etc., can be sensed in real time by sensors, providing an accurate data basis for subsequent dynamic correction and prediction; by using real-time perception data for collision prediction, possible collision risks can be discovered in advance, and timely measures can be taken to avoid the occurrence of collision events, thereby ensuring the safety and stability of the processing process. Based on collision prediction and real-time perception data, a dynamic path correction strategy is formulated, and the processing path can be adjusted in real time during the processing process to avoid collisions and improve processing accuracy; by dynamic path correction, processing interruptions and pause times can be reduced, and processing efficiency and productivity can be improved. By extracting the tool radius correction amount according to the dynamic path correction strategy, accurate control of the tool trajectory can be achieved to ensure the accuracy of the cutting depth and processing trajectory; by extracting the tool radius correction amount in real time, the impact of tool wear can be compensated in time, the tool life can be extended, and the processing quality can be maintained. Adaptive correction of the machining path based on dynamic path correction strategy and tool correction data can achieve real-time optimization and adjustment of the machining path, improve machining accuracy and stability; through adaptive machining correction path, errors and repeated machining times in machining can be reduced, reducing machining costs and resource consumption.

[0157] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0158] Step S41: performing cutting simulation and virtual collision detection according to the adaptive machining path data to obtain cutting simulation and collision simulation data;

[0159] In the embodiment of the present invention, according to the adaptive machining path data, cutting parameters such as tool type, cutting speed, feed speed, etc. are set; the workpiece model and tool model are loaded into the cutting simulation software; the cutting process of the tool along the adaptive machining path is simulated, and the influencing factors such as cutting force and cutting heat in the cutting process are considered; and the cutting force, cutting depth, cutting speed and other related data in the cutting simulation process are recorded. A collision detection module is added to the cutting simulation software to detect the collision between the tool and the workpiece; the workpiece model, tool model and fixture model are loaded into the collision detection module; the tool is simulated to move along the adaptive machining path, and whether there is a collision between the tool and the workpiece or fixture is detected in real time; when a collision is detected, the location, time and collision force of the collision are recorded, and the collision is processed according to the set collision processing strategy (such as stopping processing, adjusting the tool path, etc.).

[0160] Step S42: performing real-time processing perception on the cutting simulation and collision simulation data through sensors to obtain real-time processing perception data; performing collision prediction on the cutting simulation and collision simulation data and the real-time processing perception data to obtain collision prediction data;

[0161] In an embodiment of the present invention, collision sensors and processing perception sensors are installed on a CNC machine tool. These sensors can monitor the distance, speed and other information between the tool and the workpiece in real time, as well as the real-time processing situation during the processing. Computer software is used to model the workpiece and tool to be processed, and cutting simulation and collision simulation are performed to simulate the motion trajectory of the tool and possible collision situations during the processing. The cutting simulation data, collision simulation data and real-time processing perception data are input into the collision prediction algorithm; the collision prediction algorithm analyzes and calculates based on the sensor data and simulation data, predicts the possible collision situation, and generates collision prediction data; the collision prediction data includes the predicted collision time, position, possible collision force and other information. The computer or control system analyzes and processes the real-time processing perception data and collision prediction data; the analysis results include real-time processing status, tool position changes, possible collision situations, etc.

[0162] Step S43: formulating a dynamic path correction strategy according to the collision prediction data and the real-time processing perception data to obtain a dynamic path correction strategy;

[0163] In an embodiment of the present invention, based on the collision prediction data, the cause of the possible collision is analyzed to determine the possibility of the collision and the degree of impact; based on the results of the collision prediction analysis, a corresponding collision prevention strategy is formulated, such as adjusting the tool path, slowing down or stopping processing, etc. The real-time processing perception data is monitored and analyzed in real time to understand the current processing status and dynamic changes; based on the real-time processing perception data, various parameters in the current processing process, such as cutting speed, feed speed, tool position, etc. are extracted. In combination with the results of collision prediction analysis and real-time processing perception analysis, a dynamic path correction strategy is formulated; according to the possibility of collision, the tool path is adjusted, the processing speed is adjusted, the cutting depth is reduced, the cutting direction is adjusted, and other measures are taken to ensure the safety and stability of the processing process. The formulated dynamic path correction strategy is applied to the actual processing process.

[0164] Step S44: extracting the tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data;

[0165] In an embodiment of the present invention, the radius correction amount required for the tool is calculated according to the formulated dynamic path correction strategy; the change of the tool path and the influence of the correction amount on the processing effect are considered to determine the final tool radius correction amount data. The calculated tool radius correction amount data is recorded as reference data for correcting the tool in the subsequent processing process. The recorded tool radius correction amount data is applied to the adaptive processing path data to realize the dynamic correction of the tool path; ensure that the tool moves according to the corrected path during the processing process, and improve the processing accuracy and stability.

[0166] Step S45: using the dynamic path correction strategy and the tool correction amount data to correct the tool radius of the adaptive machining path data to obtain adaptive machining correction path data;

[0167] In an embodiment of the present invention, the adaptive machining path data is corrected according to a dynamic path correction strategy. For example, if the collision prediction data indicates that a collision may occur, the tool path is adjusted according to the strategy to avoid the collision; if the path needs to be adjusted according to the real-time machining perception data, it is also processed here. According to the tool correction amount data, the tool radius is corrected for the corrected machining path; this involves fine-tuning or re-planning the path to ensure that there is enough clearance between the tool and the workpiece to avoid collisions or quality problems. The corrected machining path data is saved as adaptive machining correction path data for subsequent CNC code generation or actual machining operations.

[0168] Preferably, step S4 comprises the following steps:

[0169] Step S441: performing path continuity analysis according to the dynamic path correction strategy to obtain path continuity data; performing path stability analysis according to the dynamic path correction strategy and tool cutting parameters to obtain path stability data; performing cutting quality analysis according to the dynamic path correction strategy and surface quality optimization parameters to obtain cutting quality data;

[0170] Step S442: constructing a tool radius variation model using mathematical modeling technology to obtain a tool radius variation model; monitoring the tool radius in real time using a sensor to obtain real-time tool radius monitoring data;

[0171] Step S443: performing a tool radius local difference change analysis according to the tool radius change model and the real-time tool radius monitoring data to obtain tool radius local difference data;

[0172] Step S444: Calculate the tool radius correction amount according to the tool radius local difference data, path continuity data, path stability data and cutting quality data to obtain the tool radius correction amount data.

[0173] The present invention can evaluate the smoothness and continuity of the processing path through path continuity analysis, optimize the path design, reduce vibration and impact during the processing, and improve the processing accuracy and surface quality; use the path stability data for analysis, evaluate the stability during the processing, adjust the path parameters and cutting parameters, maintain the stable contact between the tool and the workpiece, and improve the processing efficiency and cutting quality; through cutting quality analysis, the cutting quality during the processing can be evaluated, the processing parameters and path design can be adjusted, the cutting process can be optimized, and the processing surface quality and accuracy can be improved. By establishing a tool radius change model, the wear of the tool during the processing can be predicted, and measures can be taken in advance to compensate, and the processing accuracy and cutting quality can be maintained; by monitoring the tool radius in real time through a sensor, the tool wear can be obtained in time, the tool compensation amount can be adjusted, and the stability and safety of the tool state during the processing can be ensured. Through local difference data analysis, the tool wear can be understood more finely, the local wear area of ​​the tool can be identified, the tool compensation amount can be adjusted in a targeted manner, and the tool service life can be extended; according to the local difference data, the processing path and cutting parameters can be adjusted in time to maintain the stability and consistency of the cutting process and improve the processing quality and efficiency. By combining a variety of data to calculate the tool radius correction, it is possible to achieve precise control of the tool trajectory and ensure the accuracy of the cutting depth and processing trajectory; by comprehensively considering factors such as tool wear, processing stability, path continuity and cutting quality, the tool radius correction can be optimized to improve the stability, accuracy and efficiency of the processing process.

[0174] In the embodiment of the present invention, for the dynamic path correction strategy, the path continuity analysis is performed on the corrected processing path to evaluate the smoothness and continuity of the path at the connection; the stability of the processing path is analyzed, the stability of the tool under different conditions is considered, and the path stability data is obtained; the cutting quality is analyzed, the quality of the surface quality during the processing is evaluated, and the cutting quality data is obtained. Using mathematical modeling technology, a model of the change of tool radius over time is established, and factors such as tool wear are considered to obtain a tool radius change model; the tool is monitored in real time using a sensor to obtain real-time radius data of the tool during the processing process, and real-time tool radius monitoring data is obtained. Combined with the tool radius change model and real-time monitoring data, the radius difference of the tool at different parts is analyzed to obtain local tool radius difference data; combined with the local tool radius difference data, path continuity data, path stability data and cutting quality data, the radius correction amount of the tool at different positions is calculated using a calculation or optimization algorithm to obtain tool radius correction amount data.

[0175] Preferably, step S5 comprises the following steps:

[0176] Step S51: performing multi-axis motion analysis on the CNC machine tool to obtain multi-axis motion data; performing motion freedom analysis and motion condition restriction analysis based on the multi-axis motion data to obtain constraint condition data;

[0177] Step S52: constructing a machine tool kinematics model based on the multi-axis motion data and constraint condition data to obtain a machine tool kinematics model;

[0178] Step S53: performing dynamic load prediction according to the machine tool kinematics model to obtain dynamic load prediction data;

[0179] Step S54: performing multi-axis synchronous motion path compensation according to the dynamic load prediction data and the adaptive processing correction path data to obtain CNC machine tool processing path data;

[0180] Step S55: Generate NC code according to the NC machine tool processing path data to obtain NC code data.

[0181] Through multi-axis motion analysis and motion freedom analysis, the present invention can fully understand the working state of the CNC machine tool under multi-axis motion, including the motion range, speed limit, acceleration limit and other information of each axis; the acquisition of constraint condition data can help optimize the processing path planning and ensure that the machine tool is processed under safe and stable motion conditions. The construction of the machine tool kinematic model can accurately describe the motion characteristics of the CNC machine tool, including the relationship between axes, motion laws, motion freedom, etc., and provide an accurate mathematical model basis for subsequent dynamic load prediction, motion path planning, etc. Through dynamic load prediction, the load conditions of each axis of the CNC machine tool during the processing process can be predicted, including inertial load, dynamic load, etc., which is helpful to evaluate the stability and load bearing capacity of the machine tool motion, and provide an important reference for subsequent path compensation and motion control. Through multi-axis synchronous motion path compensation, the processing path can be adjusted according to real-time load prediction data, dynamic control and optimization of machine tool motion can be achieved, and processing accuracy and efficiency can be improved. The generation of NC code is based on optimized machining path data, which can directly guide the CNC machine tool to perform machining operations, ensure that the machining process proceeds as expected, reduce human errors and machining time, and improve machining quality and production efficiency.

[0182] In the embodiment of the present invention, sensors are used to record the motion state data of each axis of the CNC machine tool, including information such as position, speed, acceleration, etc.; according to the structure and motion mode of the machine tool, the motion freedom of each axis is analyzed to determine the motion range and restriction conditions of each axis; in combination with the technical specifications and processing requirements of the machine tool, the restrictions of the motion conditions, such as the upper speed limit, the upper acceleration limit, the stroke limit, etc., are analyzed. According to the multi-axis motion data and the constraint condition data, a mathematical modeling method is used to construct a kinematic model of the CNC machine tool, including joint motion equations, kinematic constraint equations, etc.; the parameters of the kinematic model of the machine tool are calculated, including parameters such as joint speed and acceleration limit. The dynamic load conditions of each axis of the machine tool during the cutting process are predicted using the machine tool kinematic model and the cutting process parameters, including inertial load, dynamic load, etc.; the predicted dynamic load data is recorded, including the load value of the time series. According to the dynamic load prediction data, the processing path is adjusted to realize the synchronous compensation of the multi-axis motion of the machine tool to reduce the influence of the dynamic load on the processing accuracy; according to the compensated processing path data, the CNC machine tool processing path is generated, including information such as tool path and feed speed. According to the CNC machine tool processing path data, use CNC programming software to generate corresponding CNC codes, including G code, M code, etc., which are used to control the CNC machine tool to perform processing operations.

[0183] The present invention can fully understand the shape, characteristics and curvature changes of the workpiece through surface perception data, and provide an important basis for subsequent processing path planning, cutting parameter optimization and surface quality improvement. The construction of the spatiotemporal dynamic model of cutting force and the cutting impact analysis can help optimize the use parameters of the tool, improve cutting efficiency and tool life; the exploration of the stable cutting domain model helps to determine safe and stable cutting conditions, reduce vibration and thermal deformation during processing, and improve processing accuracy and surface quality. The extraction of surface quality optimization parameters helps to optimize the surface quality during processing; dynamic processing path planning and adaptive processing path planning can dynamically adjust the processing path according to the real-time shape and cutting conditions of the workpiece, and improve processing efficiency and accuracy. Dynamic path correction and tool radius correction can adjust the processing path and tool parameters according to real-time conditions during processing, improve processing accuracy and stability, and reduce processing errors and damage. The analysis of multi-axis motion data and synchronous motion path compensation can realize the precise control and optimization of the CNC machine tool processing process, improve processing efficiency and finished product quality; the generation of CNC code is based on the optimized processing path data, which can accurately guide the CNC machine tool to perform processing operations, ensure that the processing process is carried out as expected, reduce human errors and processing time, and improve production efficiency. Therefore, the present invention provides a method for planning a machining path for a CNC machine tool. By analyzing the dynamic changes between the tool and the workpiece and the shape of the workpiece during the cutting process, the workpiece surface perception data and the tool cutting parameters are obtained to plan an adaptive machining path. The adaptive path planning can dynamically adjust the machining path according to actual conditions to adapt to the changing workpiece shape or surface conditions; the local difference in tool radius is corrected on the adaptively planned path to improve the machining quality and efficiency as well as the machining accuracy and surface quality.

[0184] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0185] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for planning a machining path of a CNC machine tool, characterized in that: The following steps are involved: Step S1: Scanning the surface of the workpiece to be processed by a laser scanner to obtain workpiece surface scanning data; Perform surface topology analysis on the workpiece surface scanning data to obtain the workpiece surface curvature data; Perform workpiece surface perception according to workpiece surface scanning data and workpiece surface curvature data to obtain workpiece surface perception data; Step S2: construct a spatiotemporal dynamic model of cutting force based on the workpiece surface scanning data and the workpiece surface sensing data to obtain the spatiotemporal dynamic model of cutting force; perform spatiotemporal influence analysis of cutting force based on the spatiotemporal dynamic model of cutting force to obtain cutting heat energy and vibration data; explore a stable cutting domain model based on the spatiotemporal dynamic model of cutting force and the cutting heat energy and vibration data; and obtain tool cutting parameters; Step S3: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters; performing dynamic machining path planning according to the workpiece surface curvature data and the cutting force spatiotemporal dynamic model to obtain dynamic machining path data; performing adaptive machining path planning on the dynamic machining path data using tool cutting parameters to obtain adaptive machining path data; Step S4: Perform dynamic path correction according to the adaptive processing path data; obtain a dynamic path correction strategy; Extract tool radius correction amount according to dynamic path correction strategy to obtain tool radius correction amount data; use dynamic path correction strategy and tool correction amount data to correct tool radius on adaptive machining path data to obtain adaptive machining correction path data; Step S5: Perform multi-axis motion analysis on the CNC machine tool to obtain multi-axis motion data; perform synchronous motion path compensation based on the multi-axis motion data to obtain CNC machine tool processing path data; generate CNC code based on the CNC machine tool processing path data to obtain CNC code data.

2. The method for planning a machining path of a CNC machine tool according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: scanning the workpiece surface by a laser scanner to obtain workpiece surface scanning data; performing surface topology analysis on the workpiece surface scanning data to obtain workpiece surface curvature data; performing linear feature extraction on the workpiece surface curvature data to obtain workpiece surface linear feature data; Step S12: performing local area segmentation according to the workpiece surface scanning data to obtain local area segmentation data; performing local area feature extraction on the local area segmentation data according to the workpiece surface curvature data to obtain local area feature data; Step S13: performing surface shape modeling according to the local area feature data and the workpiece surface linear feature data to obtain the workpiece surface shape model data; Step S14: performing surface defect anomaly analysis based on the workpiece surface linear feature data and the workpiece surface curvature data to obtain abnormal area marking data; Step S15: topologically connecting the surface feature points according to the workpiece surface shape model data and the workpiece surface linear feature data to obtain a feature point topological map; topologically linking the local area according to the workpiece surface shape model data and the local area feature data to obtain a local area topological map; performing surface accessibility analysis according to the feature point connection data and the local area connection data to obtain a surface accessibility map; Step S16: Integrate the workpiece surface perception data according to the workpiece surface shape model data, abnormal area marking data, feature point topology map, local area topology map and surface accessibility map to obtain the workpiece surface perception data.

3. The method for planning a machining path of a CNC machine tool according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing surface defect image detection on the linear feature data of the workpiece surface to obtain surface defect influence degree data; performing defect region image segmentation on the linear feature data of the workpiece surface to obtain defect region contour data; Step S142: performing defect penetration depth analysis according to the defect area contour data to obtain defect penetration depth data; generating a defect thermal map according to the defect area contour data and the defect penetration depth data to obtain a defect thermal map; Step S143: performing surface defect impact analysis based on the defect penetration depth data and the defect thermal map to obtain surface defect impact degree data; performing surface quality assessment based on the surface defect impact degree data and the workpiece surface curvature data to obtain surface quality index data; Step S144: marking abnormal regions of the workpiece surface shape model data according to the surface quality index data to obtain abnormal region marking data.

4. The method for planning a machining path of a CNC machine tool according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: analyzing the material characteristics of the workpiece to be processed according to the workpiece surface scanning data to obtain the workpiece material performance parameters; predicting the cutting performance using a preset cutting performance prediction model and the workpiece material performance parameters to obtain cutting performance prediction data; Step S22: performing a cutting force time series change analysis based on the workpiece material performance parameters and the cutting performance prediction data to obtain cutting force time series change data; performing a cutting force spatial law analysis based on the workpiece surface perception data and the cutting performance prediction data to obtain cutting force spatial law data; Step S23: performing spatiotemporal dynamic modeling of cutting force based on the cutting force time series variation data and the cutting force spatial regularity data to obtain a spatiotemporal dynamic model of cutting force; Step S24: performing cutting heat conduction analysis and cutting vibration analysis according to the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting heat and vibration data; constructing a stable cutting domain model based on the cutting force spatiotemporal dynamic model and the cutting heat and vibration data to obtain a stable cutting domain model; Step S25: Define the range of stable cutting parameters according to the stable cutting domain model to obtain stable cutting parameter range data; construct a stable cutting domain exploration model based on machine learning technology and the stable cutting domain model to obtain a stable cutting domain exploration model; explore the optimal cutting parameter combination according to the stable cutting domain exploration model and the stable cutting parameter range data to obtain the tool cutting parameters.

5. The method for planning a machining path of a CNC machine tool according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: performing cutting temperature analysis according to the cutting force spatiotemporal dynamic model and cutting performance prediction data to obtain a cutting temperature distribution diagram; Step S242: Acquire workpiece required data; perform cutting temperature control processing according to the cutting temperature distribution map and the workpiece required data to obtain cutting temperature control parameters; perform heat conduction path analysis according to the cutting temperature distribution map and the cutting temperature control parameters to obtain heat conduction path data; Step S243: performing thermal deformation analysis according to the heat conduction path data to obtain workpiece thermal deformation data; performing energy consumption analysis according to the cutting temperature distribution diagram and the cutting temperature control parameters to obtain cutting energy-saving parameters; Step S244: performing cutting vibration analysis based on the cutting force spatiotemporal dynamic model and the cutting performance prediction data to obtain cutting vibration data; performing vibration modal analysis on the cutting vibration data to obtain vibration frequency data and stable cutting data; Step S245: construct a stable cutting domain model based on the cutting force spatiotemporal dynamic model, the cutting temperature distribution diagram, the workpiece thermal deformation data, the cutting energy-saving parameters, the vibration frequency data and the stable cutting data to obtain a stable cutting domain model.

6. The method for planning a machining path of a CNC machine tool according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting surface quality optimization parameters according to the workpiece surface sensing data to obtain surface quality optimization parameters; Step S32: adjusting the dynamic machining path according to the workpiece surface curvature data, the cutting force spatiotemporal dynamic model and the surface quality optimization parameters to obtain dynamic machining path adjustment data; Step S33: extracting path deviation control parameters according to the dynamic processing path adjustment data to obtain path deviation control parameters; marking key processing areas according to the surface quality optimization parameters to obtain key processing area data; and sorting the key processing area data by path priority to obtain path priority data; Step S34: generating a dynamic machining path based on the dynamic machining path adjustment data, the path deviation control parameter and the path priority data to obtain dynamic machining path data; Step S35: using the tool cutting parameters to perform adaptive machining path planning on the dynamic machining path data to obtain adaptive machining path data.

7. The method for planning a machining path of a CNC machine tool according to claim 6, characterized in that: Step S3 includes the following steps: Step S351: segmenting the dynamic processing path to obtain processing path segment data; optimizing the processing path segment data by segment using tool cutting parameters to obtain processing path segment optimization data; Step S352: Generate a speed curve according to the segmented optimization data of the processing path to obtain path speed curve data; perform acceleration limit processing according to the path speed curve data to obtain acceleration upper limit data; Step S353: performing smooth transition between path segments according to the acceleration upper limit data to obtain tool processing path optimization data; performing real-time processing state monitoring according to the tool processing path optimization data to obtain real-time processing state data; Step S354: performing speed control calculation according to the tool processing path optimization data, the tool cutting parameters and the real-time processing status data to obtain a cutting speed adjustment amount; performing speed adjustment using the cutting speed adjustment amount to obtain cutting speed control data; Step S355: Perform adaptive path planning based on the tool processing path optimization data and the cutting speed control data to obtain adaptive processing path data.

8. The method for planning a machining path of a CNC machine tool according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing cutting simulation and virtual collision detection according to the adaptive machining path data to obtain cutting simulation and collision simulation data; Step S42: performing real-time processing perception on the cutting simulation and collision simulation data through sensors to obtain real-time processing perception data; performing collision prediction on the cutting simulation and collision simulation data and the real-time processing perception data to obtain collision prediction data; Step S43: formulating a dynamic path correction strategy according to the collision prediction data and the real-time processing perception data to obtain a dynamic path correction strategy; Step S44: extracting the tool radius correction amount according to the dynamic path correction strategy to obtain tool radius correction amount data; Step S45: using the dynamic path correction strategy and the tool correction amount data to perform tool radius correction on the adaptive machining path data to obtain adaptive machining correction path data.

9. The method for planning a machining path of a CNC machine tool according to claim 8, characterized in that: Step S4 includes the following steps: Step S441: performing path continuity analysis according to the dynamic path correction strategy to obtain path continuity data; performing path stability analysis according to the dynamic path correction strategy and tool cutting parameters to obtain path stability data; performing cutting quality analysis according to the dynamic path correction strategy and surface quality optimization parameters to obtain cutting quality data; Step S442: constructing a tool radius variation model using mathematical modeling technology to obtain a tool radius variation model; monitoring the tool radius in real time using a sensor to obtain real-time tool radius monitoring data; Step S443: performing a tool radius local difference change analysis according to the tool radius change model and the real-time tool radius monitoring data to obtain tool radius local difference data; Step S444: Calculate the tool radius correction amount according to the tool radius local difference data, path continuity data, path stability data and cutting quality data to obtain the tool radius correction amount data.

10. The method for planning a machining path of a CNC machine tool according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing multi-axis motion analysis on the CNC machine tool to obtain multi-axis motion data; performing motion freedom analysis and motion condition restriction analysis based on the multi-axis motion data to obtain constraint condition data; Step S52: constructing a machine tool kinematics model based on the multi-axis motion data and constraint condition data to obtain a machine tool kinematics model; Step S53: performing dynamic load prediction according to the machine tool kinematics model to obtain dynamic load prediction data; Step S54: performing multi-axis synchronous motion path compensation according to the dynamic load prediction data and the adaptive processing correction path data to obtain CNC machine tool processing path data; Step S55: Generate NC code according to the NC machine tool processing path data to obtain NC code data.

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