Real-time control method, system and device for machining path of shaft linkage curved surface component

Abstract

The invention relates to a shaft linkage curved surface component machining path real-time control method, system and device, and the method comprises the steps: obtaining geometric data and machining process parameters of a shaft linkage curved surface component, and carrying out the shaft linkage decomposition to obtain a multi-shaft linkage machining path; real-time operation parameters of machining equipment and tool workpiece contact data are obtained, deviation matching is conducted on the multi-axis linkage machining path, and a real-time axis control instruction set and an axis position compensation trend curve are obtained; performing path optimization on the shaft position compensation trend curve according to the real-time shaft control instruction group to obtain an initial shaft linkage adjustment scheme; performing curved surface precision evaluation on the tool workpiece contact data, and performing curved surface prediction in combination with the axis position compensation trend curve to obtain a curved surface quality prediction area; and performing coordinated optimization on the initial axis linkage adjustment scheme according to the curved surface quality prediction area to obtain an axis linkage optimization processing strategy. It can be guaranteed that efficient operation can be achieved in different machining states, and path deviation in the machining process is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of shaft-linked curved surface components, and particularly relates to a real-time control method, system and device for the machining path of shaft-linked curved surface components. Background Technique

[0002] As one of the core technologies in the field of modern precision manufacturing, the machining technology of shaft-linked curved surface components has been widely applied in high-precision machining fields such as aerospace, mold manufacturing, and medical devices. With the continuous improvement of the requirements for product precision and efficiency in the manufacturing industry, how to achieve high-precision and high-efficiency machining of shaft-linked curved surface components has become one of the research focuses. The traditional machining path control method for shaft-linked curved surface components mainly relies on the preset machining path. This static control strategy cannot cope with the real-time changes during the machining process, such as tool wear, workpiece material inhomogeneity, and machine tool vibration. These factors will cause deviations between the actual machining path and the theoretical path, thus affecting the machining quality and precision. The traditional method lacks the effective utilization of real-time data during the machining process and cannot perform dynamic adjustment and optimization according to the real-time machining state. This lag makes it difficult for the machining system to respond promptly to abnormal situations during the machining process, affecting the machining efficiency and the stability of product quality. Summary of the Invention

[0003] The main object of the present invention is to provide a real-time control method, system and device for the machining path of shaft-linked curved surface components, which can ensure efficient operation under different machining states and reduce the path deviation during the machining process.

[0004] To achieve the above object, the present invention provides a real-time control method for the machining path of shaft-linked curved surface components, including: Obtaining the geometric data and machining process parameters of the shaft-linked curved surface component, and performing shaft-linked decomposition to obtain a multi-axis linked machining path; Obtaining the real-time operation parameters of the machining equipment and the tool-workpiece contact data, and performing deviation matching on the multi-axis linked machining path to obtain a real-time shaft control instruction set and an axis position compensation trend curve; Optimizing the path of the axis position compensation trend curve according to the real-time shaft control instruction set to obtain an initial axis-linked adjustment plan; Evaluating the surface accuracy of the tool-workpiece contact data, and combining with the axis position compensation trend curve to perform surface prediction to obtain a surface quality prediction region; Coordinating and optimizing the initial axis-linked adjustment plan according to the surface quality prediction region to obtain an axis-linked optimized machining strategy.

[0005] Further, the obtaining the geometric data and machining process parameters of the shaft-linked curved surface component, and performing shaft-linked decomposition to obtain a multi-axis linked machining path includes: Parametrize the geometric data to obtain a mathematical expression of the surface; Based on the mathematical expression of the surface and the machining process parameters, perform geometric feature analysis to obtain a classification result of the surface regions; According to the classification result of the surface regions and the machining process parameters, calculate the tool path density and feed rate to obtain tool motion control parameters; Generate tool paths for the surface regions based on the tool motion control parameters to obtain a machining trajectory dataset; Perform axial decomposition on the machining trajectory dataset and the machining process parameters to obtain preliminary axis linkage commands; Based on the preliminary axis linkage commands and the machining process parameters, calculate axis motion error compensation to obtain compensated axis motion data; Perform machining path analysis on the compensated axis motion data to obtain a multi-axis linkage machining path.

[0006] Furthermore, acquire the real-time operating parameters of the machining equipment and tool-workpiece contact data, and perform deviation matching on the multi-axis linkage machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve, including: Collect the position, speed, and acceleration of each axis of the machining equipment in real time to obtain an original operating parameter matrix; Perform filtering and data normalization on the original operating parameter matrix to obtain the real-time operating parameters; Collect tool cutting data of the machining equipment based on the real-time operating parameters to obtain the tool-workpiece contact data; Perform time-domain and frequency-domain analysis on the tool-workpiece contact data to obtain a cutting state feature spectrum; Perform correlation analysis on the real-time operating parameters and the cutting state feature spectrum, and perform registration calculation with the multi-axis linkage machining path to obtain the real-time axis control instruction set; Compare the deviation between the real-time axis control instruction set and the preset machining path target trajectory points to obtain a trajectory deviation vector field; Extract the trend of the trajectory deviation vector field and perform priority sorting according to the preset compensation requirements of each axis to obtain the axis position compensation trend curve.

[0007] Furthermore, the step of performing correlation analysis on the real-time operating parameters and the cutting state feature spectrum, and performing registration calculation with the multi-axis linkage machining path to obtain the real-time axis control instruction set includes: Construct a multi-layer correlation matrix based on the real-time operating parameters and the cutting state feature spectrum to obtain a process motion coupling data structure; Perform singular value decomposition on the process motion coupling data structure to obtain the main process motion feature components and the process motion weight coefficients; Perform piecewise interpolation and local coordinate system transformation on the multi-axis linkage machining path according to the main process motion feature components to obtain a set of path adaptation points; Optimize each axis for the set of path adaptation points and the process motion weight coefficients to obtain a matrix of axis compensation amounts; Modify the tool feed path according to the matrix of axis compensation amounts to obtain corrected tool pose parameters; Convert the corrected tool pose parameters into servo control instructions for each axis and perform dynamic constraint verification to obtain the real-time axis control instruction set.

[0008] Further, optimizing the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan, including: Perform spectral density analysis on the axis position compensation trend to obtain an axis position frequency spectrum matrix; Perform curve fitting on the real-time axis control instruction set according to the axis position frequency spectrum matrix to obtain an axis position compensation trend curve; Perform segmentation processing on the axis position compensation trend curve to obtain a set of segmented compensation curves; Construct a spline interpolation function based on the set of segmented compensation curves to obtain an axis position compensation function; Perform constraint optimization on the axis position compensation function to obtain an axis position compensation instruction; Perform vector calculation according to the axis position compensation instruction to obtain an axis position compensation vector; Perform axis linkage interference inspection on the axis position compensation vector to obtain a non-interference compensation plan; Fuse the instructions for the non-interference compensation plan according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan.

[0009] Further, evaluating the surface accuracy of the tool-workpiece contact data and combining with the axis position compensation trend curve to perform surface prediction to obtain a surface quality prediction region, including: Sample the normal cutting depth and tangential speed of the tool-workpiece contact data to obtain a surface quality feature vector; Perform local differential geometry calculation on the tool-workpiece contact data according to the surface quality feature vector to obtain a surface curvature distribution map; Perform surface topography grid subdivision on the tool-workpiece contact data according to the surface curvature distribution map to obtain a surface accuracy evaluation grid; Perform node deviation screening on the surface accuracy evaluation grid to obtain a set of target surface quality control points; Perform surface correlation mapping based on the set of target surface quality control points and the axis position compensation trend curve to obtain a compensation influence area; Perform iterative simulation calculations of surface quality on the compensation influence area to obtain a surface quality probability distribution cloud map; Classify the surface quality probability distribution cloud map according to the axis position compensation trend curve to obtain a surface quality prediction area; Extract the boundary and perform topological optimization on the surface quality prediction area to obtain the surface quality prediction area.

[0010] Furthermore, coordinating and optimizing the initial axis linkage adjustment plan based on the surface quality prediction area to obtain an axis linkage optimized machining strategy, including: Perform curvature analysis on the surface quality prediction area to obtain a local curvature feature distribution map; Extract control points from the surface quality prediction area according to the local curvature feature distribution map to obtain an axis linkage control node set; Judge the sensitivity of the axis linkage control node set to obtain an axial compensation sensitivity matrix; Perform sub-region compensation calculation on the initial axis linkage adjustment plan according to the axial compensation sensitivity matrix to obtain axis linkage sub-region compensation information; Smooth the boundary of the axis linkage sub-region compensation information to obtain axis linkage continuous transition control information; Perform speed planning on the axis linkage sub-region compensation information according to the axis linkage continuous transition control information to obtain axis linkage speed optimization parameters; Constrain the acceleration of the axis linkage speed optimization parameters to obtain an axis linkage smooth transition curve; Perform feedback correction on the axis linkage speed optimization parameters according to the axis linkage smooth transition curve to obtain an axis linkage real-time compensation instruction; Perform path synthesis conversion on the initial axis linkage adjustment plan according to the axis linkage real-time compensation instruction to obtain the axis linkage optimized machining strategy.

[0011] The present invention also provides an axis linkage surface component machining path real-time control device, which is applied to the axis linkage surface component machining path real-time control method described in any one of the above, including: An acquisition module, which is used to acquire the geometric data and machining process parameters of the axis linkage surface component, and perform axis linkage decomposition to obtain a multi-axis linkage machining path; An analysis module, which is used to obtain the real-time operation parameters of the processing equipment and the tool-workpiece contact data, and perform deviation matching on the multi-axis linkage machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve; An association module, which is used to optimize the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan; A processing module, which is used to evaluate the surface accuracy of the tool-workpiece contact data and perform surface prediction in combination with the axis position compensation trend curve to obtain a surface quality prediction area; A control module, which is used to coordinately optimize the initial axis linkage adjustment plan according to the surface quality prediction area to obtain an axis linkage optimized machining strategy.

[0012] The present invention also provides a real-time control system for the machining path of an axis linkage surface component, including: A memory, which is used to store programs; A processor, which is used to execute the program to implement each step of the real-time control method for the machining path of an axis linkage surface component described in any one of the above.

[0013] A real-time control method, system and device for the machining path of an axis linkage surface component provided by the present invention have the following beneficial effects: By obtaining the geometric data and machining process parameters of the axis linkage surface component and performing axis linkage decomposition, the characteristic requirements of complex surface machining can be evaluated more comprehensively, thereby improving the accuracy of machining path planning and providing more reliable basic data for the real-time control system. By obtaining the real-time operation parameters of the processing equipment and the tool-workpiece contact data in real time, and performing deviation matching to obtain a real-time axis control instruction set and a position compensation trend curve, the fine monitoring and management of the dynamic changes during the machining process are realized, which helps to achieve dynamic responsive machining control and avoid machining accuracy loss. Based on the real-time axis control instruction set, the path of the axis position compensation trend curve is optimized, which can ensure that the system can operate efficiently under different machining states, reduce the path deviation during the machining process, and improve the overall machining accuracy and stability of the system. By evaluating the surface accuracy of the tool-workpiece contact data and performing surface prediction in combination with the axis position compensation trend curve, a more reasonable surface quality prediction area is formulated, thereby effectively avoiding the possible surface quality problems during the machining process, giving early warnings and making adjustments. By coordinately optimizing the initial axis linkage adjustment plan according to the surface quality prediction area, the axis linkage control strategy can be flexibly adjusted according to the characteristics of different machining stages and the changes of real-time requirements, so that the machining system can better adapt to the diverse machining requirements of complex surface components, and significantly improve the machining efficiency and product quality. Description of the Drawings

[0014] Figure 1 is a flowchart of a real-time control method for the machining path of an axis-linked curved surface component provided by the present invention; Figure 2 is a structural diagram of a real-time control system for the machining path of an axis-linked curved surface component provided by the present invention; Figure 3 is a structural diagram of a real-time control device for the machining path of an axis-linked curved surface component provided by the present invention.

[0015] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0016] In order to make the object, technical solution, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] Next, the present invention will be further described in combination with the accompanying drawings and specific embodiments.

[0018] Referring to Figure 1 as shown, the present invention provides a real-time control method for the machining path of an axis-linked curved surface component, including: Step S1: Obtain the geometric data and machining process parameters of the axis-linked curved surface component, and perform axis-linked decomposition to obtain a multi-axis linked machining path; Step S2: Obtain the real-time operating parameters of the machining equipment and the tool-workpiece contact data, and perform deviation matching on the multi-axis linked machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve; Step S3: Optimize the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis-linked adjustment plan; Step S4: Evaluate the surface accuracy of the tool-workpiece contact data, and perform surface prediction in combination with the axis position compensation trend curve to obtain a surface quality prediction area; Step S5: Coordinate and optimize the initial axis-linked adjustment plan according to the surface quality prediction area to obtain an axis-linked optimized machining strategy.

[0019] Based on the above steps, the detailed step process is as follows: Step S1: Extract the geometric feature information of the axis-linked surface components from the CAD model, including the mathematical description, topological structure, and geometric properties of the surface. At the same time, collect machining process parameters such as cutting speed, feed rate, depth of cut, and tool parameters. The system converts the geometric data of the axis-linked surface components into discrete point clouds or parametric surface equations to establish a mathematical model suitable for numerical control machining. During the axis-linked decomposition process, the complex surface motion is decomposed into the coordinated motion of each axis, which involves establishing the conversion relationship between the workpiece coordinate system and the machine tool coordinate system, calculating the normal vectors and curvatures at each control point, and determining the tool posture at each position. The decomposition algorithm converts the tool path points in the workpiece coordinate system into the motion commands of each axis of the machine tool by solving the inverse kinematics equations. This process takes into account the kinematic characteristics of the machine tool, such as workspace limitations, singularity avoidance, and axis speed constraints. According to the surface geometric features, determine a suitable tool axis vector planning strategy, which may adopt methods such as normal following, fixed angle, or optimized angle. The decomposition result generates a complete multi-axis linkage machining path, including the spatial positions and tool posture information of each interpolation point, forming a G-code or other instruction sequence recognizable by the machine tool. This step ensures that the tool can move along the expected trajectory during the subsequent machining process and provides basic data for real-time control.

[0020] Step S2: Use the sensor system to collect real-time data during the machining process, including the actual position, speed, acceleration, motor load, and temperature and other operating parameters of each axis. The tool-workpiece contact data is obtained through force sensors, acoustic emission sensors, or process monitoring systems, and physical quantities such as cutting force, vibration, and temperature are recorded. The system compares and analyzes the collected real-time operating parameters with the theoretical machining path generated in Step S1, and calculates the deviation matrix between the actual trajectory and the theoretical trajectory. The deviation matching process uses a dynamic window matching algorithm to real-time identify deviation patterns and trends, and distinguish between systematic errors and random errors. Based on the deviation analysis results, the system generates a real-time axis control instruction set, including position compensation amounts, speed adjustment factors, and acceleration limit parameters. At the same time, an axis position compensation trend curve is constructed through regression analysis and filtering techniques, and this curve reflects the deviation change law of each axis over time or path progress. The trend curve is fitted with spline functions or polynomial functions to ensure smooth and continuous compensation actions and avoid machine tool vibration. The system performs spectral analysis on the trend curve to identify periodic deviation components and system rigidity problems, providing a basis for subsequent optimization. The real-time axis control instruction set and the axis position compensation trend curve together constitute the feedback information of the closed-loop control, providing data support for path optimization.

[0021] Step S3: Perform path optimization based on the obtained real-time axis control instruction set and the axis position compensation trend curve. The path optimization algorithm takes the axis position compensation trend curve as input and applies a feedforward control strategy to predict the future position deviations of each axis. The optimization process uses the dynamic programming method to minimize the energy consumption and processing time while meeting the machining accuracy requirements. The algorithm constructs a multi-objective optimization function, comprehensively considering factors such as trajectory accuracy, surface quality, tool life, and machining efficiency. The system analyzes the coupling relationship of the movements of each axis, identifies the key axes and subordinate axes, and establishes an inter-axis coordinated control model. According to the characteristics of the axis position compensation trend curve, the algorithm adaptively adjusts the look-ahead parameters and the acceleration and deceleration time constants to optimize the speed profile curve. For high-curvature regions and sharp direction changes, the system increases the interpolation point density and refines the motion control instructions. The initial axis linkage adjustment plan includes the optimized axis position sequence, speed curve, and acceleration curve, as well as special processing strategies at key feature points. The plan also includes inter-axis synchronization evaluation indicators and dynamic adjustment thresholds to provide a decision basis for real-time execution. The path optimization result ensures the smoothness and accuracy of the coordinated movement of each axis, and at the same time considers the machine tool dynamics characteristics to avoid exciting mechanical resonance.

[0022] Step S4: Use the tool-workpiece contact data obtained in the previous steps for in-depth analysis, and evaluate the accuracy status of the machined surface through static and dynamic accuracy evaluation methods. The evaluation process involves comparing the actual cutting trajectory of the tool with the theoretical trajectory and calculating the normal deviation and tangential deviation values at each sampling point. The system establishes a statistical model to analyze the deviation distribution characteristics and identify the systematic deviation and random deviation components. For different surface feature regions, the region segmentation technology is used to process the accuracy evaluation results by region to identify the accuracy fluctuation regions and stable regions. The system extracts information such as cutting force, vibration spectrum, and temperature gradient from the tool-workpiece contact data, and combines with the material removal rate model to establish a mathematical description of the surface formation mechanism. The axis position compensation trend curve is used as the key input, and the surface formation process on the future machining path is deduced through the forward evolution algorithm. The system uses physical simulation and machine learning methods to construct a mapping relationship between surface quality and machining parameters, and predicts the surface roughness, waviness, and shape error of the machining area. The prediction model considers the influence of factors such as tool wear, machine tool thermal deformation, and workpiece deformation, and generates a dynamically updated surface quality prediction area. The prediction area is represented in the form of a heat map, intuitively showing the quality distribution of the machined surface, including the identification of accuracy risk areas, stable areas, and over-machined areas. The prediction results serve as an important basis for the coordinated optimization in Step S5, providing target-oriented decision support for optimizing the machining strategy.

[0023] Step S5: Combine the surface quality prediction region with the initial axis linkage adjustment scheme for system-level coordinated optimization. The coordinated optimization adopts a multi-level optimization structure, including three levels: macro trajectory planning, meso parameter adjustment, and micro error compensation. The optimization algorithm dynamically adjusts the feed speed and acceleration constraints of each axis based on the risk assessment of the surface quality prediction region, reducing the machining speed in high-risk regions and increasing the efficiency in stable regions. The system adopts a feedback-feedforward hybrid control strategy, combining real-time monitoring data with the prediction model to achieve predictive compensation. For different surface feature regions, the optimization algorithm adjusts the tool axis vector and tilt angle to maximize cutting stability and surface quality. Special processing strategies are adopted at the surface junctions and feature transition regions to ensure smooth transition and continuous machining. The optimization process takes into account the motion characteristics of each axis and the machine tool dynamics constraints to avoid overshoot and vibration. The system virtually verifies the optimization results by simulating the optimized machining process through digital twin technology and evaluating the optimization effect. The finally formed axis linkage optimization machining strategy includes a complete axis position sequence, speed curve, acceleration curve, and special processing instructions. The strategy also includes a real-time monitoring and adjustment mechanism, allowing dynamic adjustment of optimization parameters according to actual conditions during the machining process. The axis linkage optimization machining strategy not only ensures machining accuracy and surface quality but also improves production efficiency, achieving adaptive real-time control of high-precision surface machining.

[0024] A real-time control method for the machining path of an axis linkage surface component provided by the present invention can more comprehensively evaluate the characteristic requirements of complex surface machining by obtaining the geometric data and machining process parameters of the axis linkage surface component and performing axis linkage decomposition, thereby improving the accuracy of machining path planning and providing more reliable basic data for the real-time control system. By obtaining the operating parameters of the machining equipment and the tool-workpiece contact data in real time and performing deviation matching to obtain the real-time axis control instruction set and the position compensation trend curve, it realizes the refined monitoring and management of the dynamic changes during the machining process, helps to achieve dynamic responsive machining control, and avoids machining accuracy loss. Based on the real-time axis control instruction set, path optimization is performed on the position compensation trend curve of the axis, which can ensure the efficient operation of the system under different machining states, reduce the path deviation during the machining process, and improve the overall machining accuracy and stability of the system. By evaluating the surface accuracy based on the tool-workpiece contact data and combining with the position compensation trend curve of the axis for surface prediction, a more reasonable surface quality prediction region is formulated, effectively avoiding possible surface quality problems during the machining process, giving early warnings and making adjustments. Coordinated optimization of the initial axis linkage adjustment scheme according to the surface quality prediction region can flexibly adjust the axis linkage control strategy according to the characteristics of different machining stages and the changes in real-time requirements, making the machining system more adaptable to the diverse machining requirements of complex surface components, and significantly improving the machining efficiency and product quality.

[0025] In one embodiment, geometric data and machining process parameters of the axis-linked surface component are obtained and axis-linked decomposition is performed to obtain a multi-axis linked machining path, including: Obtain the geometric data and machining process parameters of the axis-linked surface component. The geometric data includes the shape, size, and surface features of the component, which are usually obtained through methods such as 3D scanning and CAD models. The machining process parameters cover information such as cutting speed, feed rate, and cutting depth, and these parameters directly affect the efficiency and quality of machining.

[0026] Perform parametric processing on the geometric data to obtain a mathematical expression of the surface. Parametric processing is to convert geometric data into a mathematical model, and this model can represent the shape and features of the surface with functions or equations. Through parametric processing, complex surface shapes can be simplified into computable mathematical expressions.

[0027] Based on the surface mathematical expression and machining process parameters, perform geometric feature analysis. The purpose of geometric feature analysis is to identify different parts of the surface and divide it into different regions according to features such as the curvature and slope of the surface. These regions may include flat regions, regions with large curvature, irregular regions, etc. Different tool paths and process parameters may be required for each region during machining.

[0028] According to the surface region classification results and machining process parameters, calculate the tool path density and feed rate. The tool path density refers to the density of the tool's movement trajectory on the machining surface, which is usually determined according to the complexity of the surface and machining requirements. The feed rate is the speed at which the tool advances during machining, and the feed rate needs to be reasonably set according to material properties, tool characteristics, and process requirements. Through these calculations, tool movement control parameters are obtained.

[0029] After obtaining the tool movement control parameters, generate the tool path for each surface region. This step is to generate specific tool movement paths according to the geometric features and movement control parameters of each region. These paths need to consider the machining requirements of each region to ensure that the tool can effectively machine the surface during movement while avoiding over-cutting or under-cutting. The generated machining trajectory dataset contains the movement trajectories and steps of the tool in each region.

[0030] Perform axial decomposition on the machining trajectory dataset and machining process parameters. Axial decomposition is to decompose the overall movement trajectory into movement instructions for each axis because multi-axis linked machining requires the coordinated movement of multiple axes. The initial axis-linked instructions clarify the movement mode of each axis during machining, but these instructions still need further correction to ensure accuracy.

[0031] Perform axis motion error compensation calculation to ensure machining accuracy. Error compensation corrects the preliminary axis linkage commands based on the motion errors in actual machining. Errors may stem from mechanical errors, thermal deformations, etc. of the machine. Through error compensation calculation, more accurate compensated axis motion data can be obtained to ensure the accuracy of the machining trajectory.

[0032] Conduct machining path analysis on the compensated axis motion data. Through this analysis, ensure that the compensated motion data can form a coherent multi-axis linkage machining path. The multi-axis linkage machining path not only considers the motion and error compensation of each axis but also ensures the coherence and efficiency of the overall machining process.

[0033] Through the above steps, the generation of a multi-axis linkage machining path is finally achieved starting from the acquisition of geometric data and machining process parameters.

[0034] In this embodiment, through the accurate acquisition and analysis of the geometric data and machining process parameters of the axis linkage surface component, more precise machining path planning can be achieved. This method simplifies the description of the complex surface by parameterizing the geometric data and transforming it into a mathematical expression, making the generation of the machining path more efficient and accurate. By classifying the surface regions and calculating the tool path density and feed rate, appropriate machining plans can be formulated for different regions, optimizing the tool motion control, avoiding over-cutting or under-cutting, and ensuring the machining quality. During the axial decomposition and error compensation process, the motion error problem in the machining process can be effectively solved. By accurately compensating each axis, the machining accuracy and consistency are improved. Finally, through the analysis of the compensated multi-axis linkage machining path, an efficient and accurate machining process is realized, effectively improving the production efficiency and reducing the waste caused by errors.

[0035] In one embodiment, obtain the real-time operating parameters of the machining equipment and the tool-workpiece contact data, and perform deviation matching on the multi-axis linkage machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve, including: Perform real-time acquisition of the position, speed, and acceleration of each axis of the machining equipment. The key to this step lies in using high-precision sensors and data acquisition systems. Each axis is equipped with corresponding sensors, such as position sensors (encoders or laser rangefinders), speed sensors (tachogenerators or digital speed measurement systems), and acceleration sensors (accelerometers). These sensors monitor the motion states of each axis in real time, convert the collected signals into digital signals through the data acquisition system, and finally form a raw operating parameter matrix. This matrix contains the position, speed, and acceleration data of each axis at different times, providing comprehensive equipment operating information.

[0036] After obtaining the original operation parameter matrix, filtering processing and data normalization operations are carried out. The purpose of filtering processing is to remove noise and interference signals in the original data, which is usually achieved by using digital filters such as low-pass filters or Kalman filters. The filtered data is smoother and more accurate. Data normalization is to convert data with different dimensions into a dimensionless unified data format, which can be achieved by subtracting the mean value of the data and dividing it by the standard deviation. The normalized real-time operation parameters can more accurately reflect the actual operation state of the processing equipment, facilitating subsequent analysis and calculation.

[0037] According to the real-time operation parameters, tool cutting data of the processing equipment is collected. Here, high-precision force sensors (such as dynamic cutting force sensors) and other measuring devices (such as infrared temperature sensors) are used to monitor data such as cutting force and cutting temperature when the tool contacts the workpiece. These data are recorded to form tool-workpiece contact data. The cutting force data can reflect the load condition of the tool, and the cutting temperature data can indicate the thermal effects of the tool and the workpiece. All this information is an important basis for evaluating the cutting state.

[0038] Time-domain and frequency-domain analyses are performed on the collected tool-workpiece contact data. Time-domain analysis focuses on the variation of data over time, such as the fluctuation amplitude and frequency of the cutting force. Frequency-domain analysis, through methods such as Fourier transform, converts the time-domain signal into a frequency-domain signal, analyzes the characteristics of the data in the frequency domain, and identifies specific frequency components and their corresponding amplitudes. Through these analyses, a cutting state characteristic spectrum is formed. This spectrum diagram details the key characteristics during the tool cutting process, such as the main frequency component, harmonic components, etc., and is an important tool for evaluating the processing state.

[0039] Correlation analysis is carried out between the real-time operation parameters and the cutting state characteristic spectrum. By establishing a mathematical model or using machine learning algorithms such as linear regression, neural networks, etc., the relationship between the real-time operation parameters and the cutting state characteristic spectrum is analyzed. The purpose of correlation analysis is to find the mapping relationship between the two, and through registration calculation, match it with the multi-axis linkage processing path to obtain a real-time axis control instruction set. This instruction set contains specific instructions for controlling the movement of each axis, which is used to adjust the operation state of the processing equipment to ensure the accuracy of the processing path.

[0040] The real-time axis control instruction set is compared with the preset target trajectory points of the processing path. The preset target trajectory points of the processing path are ideal paths set in advance according to the design drawings and processing requirements. By comparing the difference between the actual processing path and the preset path, the trajectory deviation is calculated, and a trajectory deviation vector field is generated. This vector field shows the deviation amount and its direction of each axis from the ideal path during the processing, reflecting the problems and deviation distribution existing in the processing.

[0041] Extract the trend of the trajectory deviation vector field. By analyzing the change trend of the vector field, identify the main influencing factors and change rules of the trajectory deviation. According to the preset compensation requirements for each axis, prioritize the extracted trends to determine the compensation amount and compensation order required for each axis, and finally obtain the axis position compensation trend curve. This trend curve is used to guide the position adjustment of each axis in subsequent processing to ensure the accuracy and stability of the processing path.

[0042] In this embodiment, by collecting the position, speed, and acceleration of each axis of the processing equipment in real time, the timeliness and accuracy of the data are ensured, thus providing a solid data foundation for subsequent processing control. Through filtering and data normalization operations, noise can be removed and the data format can be unified, improving the reliability and consistency of data processing. By collecting tool cutting data through high-precision force sensors and measuring equipment, the cutting force and cutting temperature can be monitored in real time, forming detailed tool-workpiece contact data, thereby realizing the comprehensive monitoring of the processing process. Through time-domain and frequency-domain analysis, a cutting state characteristic spectrum is generated, which can display the key characteristics in the processing process in detail, helping to detect and solve potential problems in a timely manner. By correlating and analyzing the real-time operating parameters and the cutting state characteristic spectrum, a real-time axis control instruction set is generated, realizing the precise control of the processing equipment and ensuring the accuracy of the processing path. Through the trend extraction of the trajectory deviation vector field and the sorting of compensation requirements, the generated axis position compensation trend curve can guide the precise adjustment of each axis, further improving the accuracy and stability of the processing.

[0043] In one embodiment, the real-time operating parameters and the cutting state characteristic spectrum are correlated and analyzed, and registered and calculated with the multi-axis linkage processing path to obtain a real-time axis control instruction set, including: Through sensors and measuring equipment, collect multiple operating parameters and cutting state characteristic spectra in real time. The real-time operating parameters include data such as cutting speed, feed speed, spindle speed, cutting depth, tool vibration, and temperature. These data represent the dynamic state of the tool and the workpiece during the processing, and can reflect the real-time situation of the processing. The cutting state characteristic spectrum obtains the characteristic information in the processing process through the spectral analysis of vibration signals, noise signals, force sensor signals, etc., reflecting the interaction between the tool and the workpiece and the change of cutting performance. These data are the basis for subsequent processing and optimization.

[0044] Based on the operation parameters collected in real time and the cutting state characteristic spectrum, a multi-layer correlation matrix is constructed using mathematical methods. Through the analysis of multi-dimensional data relationships, this matrix reveals the coupling relationships between cutting parameters, between the workpiece and the tool, and even between different axes. The process motion coupling data structure is a structure that describes the mutual dependence of different motion characteristics during the machining process. By correlating multi-dimensional data, it is possible to identify which parameters have an important impact on the machining process, thereby constructing an accurate process motion coupling data structure and providing basic data support for subsequent optimization processing.

[0045] Perform singular value decomposition (SVD) on the process motion coupling data structure, which is a mathematical method for dimensionality reduction of complex data. Through singular value decomposition, the complex coupling data is decomposed into principal components (i.e., principal eigencomponents) and weight coefficients. The process motion principal eigencomponents represent the main motion characteristics during the machining process, reflecting the key patterns of the motion of each axis; while the process motion weight coefficients indicate the proportion and influence of these characteristics in the entire process. In this way, it is possible to identify which characteristics have an important impact on the multi-axis linkage machining path, and thus provide a basis for the optimization and correction of the path.

[0046] Based on the process motion principal eigencomponents, refine and adjust the multi-axis linkage machining path. First, perform piecewise interpolation. Piecewise interpolation refines the path between every two original data points in the path, generating more path points, thereby improving the smoothness and accuracy of the path. Local coordinate system transformation is to transform the path adaptation point set into the local coordinate systems of each motion axis, facilitating subsequent optimization adjustment and control. Through piecewise interpolation and coordinate system transformation, the path adaptation point set can more accurately reflect the position and posture of the tool in space during the machining process, ensuring that the path adapts to the machining environment and meets the requirements of multi-axis linkage machining.

[0047] Based on the path adaptation point set, combined with the process motion weight coefficients, optimize the motion of each axis. During the optimization process, compensation is performed for possible errors, vibrations, or imbalances in each axis. The motion errors of each axis are quantified in the form of a compensation amount matrix, and each element of the compensation amount matrix corresponds to the compensation amount of each axis at a specific point. The optimization aim is to make the motion of each axis as smooth as possible, reduce machining errors, and thus improve machining accuracy. This compensation amount matrix provides a direct basis for the subsequent correction of the tool path and the adjustment of motion commands.

[0048] Based on the axis compensation matrix, the feed path of the tool is corrected to ensure the accuracy of the tool motion trajectory. In this step, considering the compensation amounts of each axis during the machining process, the corrected tool feed path will effectively correct the deviations caused by machine tool motion errors, vibrations, etc. Correcting the tool pose parameters refers to the specific position and orientation of the tool at each moment during the machining process, including the spatial coordinates of the tool and its orientation. Through this step, the tool pose parameters can more accurately reflect the actual path during the machining process, making the contact between the tool and the workpiece more precise.

[0049] The corrected tool pose parameters are converted into servo control instructions for each axis. The servo control instructions are a set of instructions for precisely controlling the motion of each axis of the machine tool. By converting the corrected tool pose parameters into motion control signals for each axis, it guides the machine tool to perform precise path tracking. Then, a dynamic constraint check is carried out to ensure that the motion of each axis does not exceed the capabilities of the machine tool during actual operation, such as speed, acceleration, load and other limiting conditions. If the instructions exceed these limiting conditions, it may lead to damage to the machine tool or a decrease in machining accuracy, so the dynamic constraint check is crucial. Through the check, it is ensured that all control instructions meet the dynamic characteristics of the machine tool and can stably achieve the machining task.

[0050] Through the aforementioned steps, a real-time axis control instruction set is finally generated. This instruction set includes the servo control instructions for each axis and has passed the dynamic constraint check to ensure the effectiveness and executability of the instructions. The real-time axis control instruction set is the core in controlling the multi-axis linkage machining process. It can precisely control the motion of each axis, ensure the relative position between the tool and the workpiece is accurate without error, and thus achieve high-precision and high-efficiency machining.

[0051] In this embodiment, by collecting real-time operating parameters and cutting state characteristic spectra, the dynamic states of the tool and workpiece during the machining process can be comprehensively captured, providing a reliable data basis for subsequent analysis. By constructing a multi-layer correlation matrix and a process motion coupling data structure, the complex coupling relationships among various cutting parameters are revealed, which helps to accurately identify the motion characteristics that have important impacts on the machining process, thereby achieving more precise control. Using singular value decomposition technology, the complex coupling data is dimensionally reduced, and the main motion feature components and weight coefficients are extracted to ensure that the key features can be grasped during the optimization process, and the optimization effect is remarkable. The application of piecewise interpolation and local coordinate system transformation makes the path adaptation point set smoother and more accurate, ensuring a high degree of fit between the multi-axis linkage machining path and the actual machining environment. During the optimization process of each axis, by introducing a compensation amount matrix, the motion errors of each axis are effectively corrected, improving the machining accuracy and stability. The tool feed path correction step ensures the accuracy of the tool pose parameters, further improving the machining accuracy. Through the conversion of servo control commands and the verification of dynamic constraints, the feasibility and safety of the motion commands of each axis in actual operation are ensured.

[0052] In one embodiment, path optimization is performed on the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan, including: Perform spectral density analysis on the axis position compensation trend. Spectral density analysis is to perform spectral analysis on the axis position data to identify and separate different frequency components. The specific process is to obtain the time series data of the axis position and use algorithms such as the fast Fourier transform (FFT) to convert the time-domain data into frequency-domain data. The frequency-domain data shows the frequency distribution of the axis position changing with time. Through the spectral matrix, it can be clearly seen which frequency components have a greater impact on the axis position change. These frequency components will be used for subsequent curve fitting and compensation calculation.

[0053] Perform curve fitting on the real-time axis control instruction set according to the axis position spectral matrix. Curve fitting is to fit the change trend of the axis position with a mathematical model to obtain the compensation trend curve. The specific process is to select a suitable fitting method (such as polynomial fitting, exponential fitting, etc.), and construct a fitting function based on the frequency components in the spectral matrix. During the fitting process, through optimization algorithms such as the least squares method, the fitting parameters are adjusted to make the fitting curve as close as possible to the actual axis position change trend. The obtained axis position compensation trend curve describes the law of the axis position changing with time.

[0054] The axis position compensation trend curve is segmented. The segmentation is to improve the compensation accuracy by dividing the continuous compensation trend curve into multiple shorter intervals. The specific process is to divide the compensation trend curve into multiple intervals according to the severity of curve changes and processing requirements, and the compensation curve changes smoothly within each interval. After segmentation, a set of segmented compensation curves is obtained. Each segmented compensation curve corresponds to a time interval, and the compensation rules and conditions within these intervals are set according to the actual processing situation to ensure that the axis position compensation can be accurately executed within each time period.

[0055] Based on the set of segmented compensation curves, a spline interpolation function is constructed. The spline interpolation function is used to smoothly connect the segmented compensation curves to ensure the continuity and smoothness of the compensation curve within each interval. The specific process is to select a suitable spline interpolation method (such as cubic spline interpolation), and construct the spline interpolation function according to the starting and ending points of each segmented compensation curve. The spline interpolation function can smoothly transition within each interval, avoiding discontinuities or jumps in the compensation curve at the segmentation points. The finally obtained axis position compensation function is a comprehensive compensation function that smoothly connects each segmented compensation curve.

[0056] The axis position compensation function is constrained and optimized. The purpose of constraint optimization is to further improve the compensation accuracy on the basis of meeting the processing requirements and reduce the possible impacts caused by inappropriate constraint conditions. The specific process is to set constraint conditions (such as the physical limits of axis movement, processing accuracy requirements, etc.) according to the processing requirements and equipment performance, and use optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to perform constraint optimization on the compensation function. During the optimization process, by adjusting the parameters of the compensation function, its compensation accuracy reaches the highest while meeting the constraint conditions. The finally obtained axis position compensation instruction is the corrected value of the instruction group after optimization, ensuring the accuracy of the entire processing path.

[0057] According to the obtained axis position compensation instruction, vector calculation is performed. The purpose of vector calculation is to obtain the specific axis position compensation vector through comprehensive processing of the compensation instruction. The specific process is to convert the compensation instruction into vector form, representing the compensation amount of the axis at different time points. During the vector calculation process, through vector synthesis and decomposition, the specific compensation values of each axis at different time points are obtained. The finally obtained axis position compensation vector represents the compensation amount that the axis needs to perform at different time points, and is the basis for ensuring the accurate movement of each axis during the processing.

[0058] After completing the vector calculation, perform an axis linkage interference check on the axis position compensation vector. The main task of the axis linkage interference check is to determine whether the compensated axis positions will cause interference or collision between the axes. The specific process is as follows: Based on the compensation vector, perform a simulation calculation on the positions of each axis to determine whether interference will occur among the axis positions after the compensation is executed. If interference is found, adjust the compensation vector or optimize the compensation scheme to avoid interference. The finally obtained interference-free compensation scheme through the interference check is an optimized compensation scheme, ensuring that the coordinated movement of the axes during the machining process will not cause interference problems.

[0059] According to the real-time axis control instruction set, perform instruction fusion on the interference-free compensation scheme to obtain the initial axis linkage adjustment scheme. Instruction fusion is to integrate multiple real-time control instructions with the compensation scheme to ensure that the motion instructions of each axis can be fully adjusted to achieve the most accurate axis linkage path. The specific process is as follows: Integrate the real-time axis control instructions with the interference-free compensation scheme, and through fusion algorithms (such as weighted average method, linear combination, etc.), comprehensively obtain the adjusted axis linkage control instructions. The finally obtained initial axis linkage adjustment scheme will be used as the path control scheme during the actual machining process to ensure the smooth progress of the entire machining process.

[0060] In this embodiment, by performing real-time control on the machining path of the axis linkage curved surface component, the machining accuracy and efficiency can be effectively improved. By spectral density analysis, identify the frequency components of the axis position changes to ensure that the fitting of the compensation trend curve is more accurate, thereby reducing the axis position error. The application of segmented processing and spline interpolation makes the compensation curve continuous and smooth in different time intervals, avoiding sudden changes and discontinuities, and further improving the compensation accuracy. The constraint optimization process ensures that the compensation instructions reach the highest accuracy while meeting the actual machining requirements by setting physical and machining accuracy constraint conditions. The combination of vector calculation and axis linkage interference check ensures that no interference will occur among the axes after the compensation is executed, guaranteeing the coordinated movement of the axes and the smooth progress of the overall machining. Finally, through instruction fusion, comprehensively process the real-time axis control instructions and the interference-free compensation scheme to obtain the optimized axis linkage adjustment scheme, ensuring the precise control of the machining path.

[0061] In one embodiment, evaluate the surface accuracy of the tool-workpiece contact data and perform surface prediction in combination with the axis position compensation trend curve to obtain the surface quality prediction region, including: Sample and analyze the data of the tool-workpiece contact to obtain the normal cutting depth and tangential velocity data, and then obtain the surface quality feature vector. The contact data between the tool and the workpiece reflects the real-time state during the machining process. The normal cutting depth refers to the cutting depth of the tool in the vertical direction during machining, while the tangential velocity reflects the movement speed of the tool along the cutting trajectory during cutting. These data constitute the surface quality feature vector, which serves as the basis for subsequent analysis.

[0062] Perform local differential geometry calculations on the tool-workpiece contact data according to the surface quality feature vector to obtain the surface curvature distribution map. Local differential geometry calculations analyze the local curvature changes within the tool-workpiece contact area. Curvature reflects the degree of bending of the surface in a specific area. By deeply analyzing this data, the minor deformation of the workpiece surface during machining can be depicted, providing an important basis for the subsequent evaluation of surface accuracy.

[0063] Based on the surface curvature distribution map, perform surface topography grid subdivision on the tool-workpiece contact data to obtain the surface accuracy evaluation grid. The grid subdivision process divides the entire workpiece surface into multiple small areas, and each small area represents an independent accuracy evaluation unit. This grid structure can help control the surface topography more precisely and provide high-precision regional data during accuracy evaluation.

[0064] Perform node deviation screening on the surface accuracy evaluation grid to obtain the set of target surface quality control points. In this step, first calculate the deviation of each evaluation node, and screen out those nodes that exceed the preset quality standard to form the set of target surface quality control points. This process ensures that only those nodes that may affect the final machining quality are included in the scope of attention, thereby reducing unnecessary computational effort and concentrating efforts on precise control.

[0065] After obtaining the set of target surface quality control points, perform surface correlation mapping based on this point set and the axis position compensation trend curve to obtain the compensation influence area. The axis position compensation trend curve describes the possible error trend during machining based on the position and attitude changes of the axis, and the compensation influence area is accurately determined according to the influence area of these error trends on the surface quality. Through this mapping, it is possible to predict in real time which areas need compensation adjustment during machining, so as to maintain the accuracy of the workpiece surface.

[0066] Perform regional data analysis on the compensation influence area to obtain data including tool position, the initial state of the workpiece surface, and the error data generated in real time during machining. These data are the basis for iterative simulation calculations, ensuring that the initial conditions of the simulation are as close as possible to the actual machining situation.

[0067] Set the parameters and compensation schemes for iterative simulation calculations. These parameters usually include the compensation amount (such as the adjustment amount of the axis position), the compensation frequency (such as how many machining steps to perform a compensation once), and the compensation strategy (such as which algorithm to use for compensation calculation). The compensation scheme is the specific compensation measure, such as adjusting the tool path, changing the cutting speed, etc.

[0068] Conduct a preliminary simulation calculation. According to the set compensation scheme and parameters, conduct a preliminary simulation calculation on the compensation influence area to simulate the impact of the compensation measure on the surface quality. The simulation calculation will generate preliminary surface quality change data, which reflect the preliminary quality change of the workpiece surface after the implementation of the compensation measure.

[0069] Based on the preliminary simulation results, perform iterative adjustment. The iterative adjustment process is to optimize and adjust the compensation scheme and parameters according to the preliminary simulation results. Usually, by analyzing the preliminary simulation data, find the deficiencies in the compensation scheme, adjust the compensation amount, compensation frequency or compensation strategy, and conduct a secondary simulation calculation.

[0070] Repeat the iterative simulation calculation until the preset conditions are met. The iterative simulation calculation usually needs to be carried out multiple times. Each iteration is optimized based on the previous simulation results, gradually approaching the ideal surface quality state. The iterative process will continue until the simulation results reach the preset quality standard or error range.

[0071] Finally, generate a probability distribution cloud map of the surface quality. After multiple iterative simulation calculations, finally obtain a probability distribution cloud map of the surface quality. This cloud map shows the quality change trend of the compensation influence area under different conditions, reflecting the possible surface quality distribution of each area.

[0072] According to the axis position compensation trend curve, conduct quality grade division on the probability distribution cloud map of the surface quality to obtain the surface quality prediction area. The quality grade division is to divide different quality grades according to different areas in the surface quality cloud map, usually divided into several grades, such as excellent, qualified, unqualified, etc. Through this division, the actual machining quality of the workpiece can be more accurately grasped, and it can be decided whether further machining optimization is needed.

[0073] Extract the boundary and conduct topological optimization on the surface quality prediction area to obtain the final surface quality prediction area. The boundary extraction determines the areas that need special attention by analyzing the boundary of the surface quality prediction area. The topological optimization is based on the existing surface quality prediction results to further optimize the shape and quality control strategy of the area, ensuring that the final optimal surface quality prediction area is obtained. The optimized result provides an accurate basis for subsequent machining control.

[0074] In this embodiment, by performing surface quality iterative simulation calculations on the compensation influence area, the influence of different compensation schemes on the machining surface quality can be accurately simulated, thereby realizing real-time quality control during the machining process. Through multiple iterative adjustments, the optimal compensation scheme can be obtained under different machining conditions, ensuring that the surface quality of each area meets the predetermined standard, and greatly improving the machining accuracy. By generating a surface quality probability distribution cloud map, the quality change trend of each area can be intuitively predicted, providing a scientific basis for subsequent quality grading and optimization. By optimizing the compensation scheme, not only can the machining surface quality be improved, but also the quality fluctuations caused by uneven compensation can be effectively reduced, thereby improving the consistency and reliability of the workpiece. The precise adjustment during the iterative simulation calculation process avoids the problems of over-compensation or under-compensation that may be caused by a single compensation measure, ensures the efficiency and stability during the machining process, reduces the scrap rate in production, and improves the overall production efficiency.

[0075] In one embodiment, according to the surface quality prediction area, the initial axis linkage adjustment scheme is coordinated and optimized to obtain an axis linkage optimized machining strategy, including: The curvature analysis of the surface quality prediction area is an in-depth analysis of the geometric characteristics of the surface, aiming to understand the bending degree of different positions on the surface. This step uses the point cloud data on the surface or the existing surface model, and calculates the curvature value of each point through mathematical methods. Curvature reflects the bending strength of the surface at a certain point, usually expressed by Gaussian curvature or mean curvature.

[0076] The analysis result generates a local curvature feature distribution map, which can intuitively display the bending changes of each part of the surface. There may be large curvature changes in some areas of the surface, which means that these areas may be the difficulties during the machining process. Through this process, the manufacturing process can give priority to these areas, thereby ensuring the machining accuracy and surface quality.

[0077] By analyzing the local curvature feature distribution map, the key areas that have a greater impact on the surface quality are identified. For these areas, the control points of the surface are further extracted. These control points are usually the points on the surface where the curvature changes significantly, the geometric shape is special, or the machining accuracy requirements are high. When extracting the control points, certain rules can be followed, such as the threshold of the curvature value, and the points with larger curvature changes can be selected as the control nodes. The selection of the control nodes can ensure the fine control of the key areas during the machining process, thereby improving the overall machining quality.

[0078] These extracted control points form an axis linkage control node set, which constitutes the basis for subsequent compensation and adjustment. This control node set provides the key reference points during the axis linkage process. By controlling the movement of these nodes, precise machining of the surface can be achieved.

[0079] After obtaining the axis linkage control node set, the next step is to evaluate the sensitivity of these nodes. The purpose of sensitivity evaluation is to assess the degree of influence of each control node on the machining result. In actual machining, different control nodes may have different effects on the forming quality, shape, and size of the surface. Through evaluation, the force exerted by each control point during axis linkage can be quantified.

[0080] The result of sensitivity evaluation generates an axial compensation sensitivity matrix, which reflects the influence of different control nodes on axis linkage compensation. Each item in the matrix represents the response degree of a certain node to the change of axial compensation. The purpose of this step is to provide a basis for subsequent sub-region compensation calculation, ensuring that key nodes receive sufficient adjustment while reducing adjustment for nodes with less influence, thereby improving machining efficiency.

[0081] Perform sub-region compensation calculation on the initial axis linkage adjustment scheme according to the sensitivity matrix. The goal of this process is to implement local axis linkage compensation according to the machining accuracy requirements of different regions. First, the sensitivity matrix can help identify which regions have greater compensation requirements and which regions are insensitive to axis linkage adjustment. Based on this information, different compensation strategies are adopted to achieve the best machining effect.

[0082] Among them, the calculation formula for compensation calculation is: ; is the compensation value for the i-th control node, which is the adjustment amount that finally needs to be input into the machine tool control system. The magnitude of this value determines the axis linkage adjustment during machining and directly affects machining accuracy and surface quality.

[0083] is the initial axis linkage adjustment value of the i-th control node, which is the adjustment amount obtained according to the initial machining path planning before compensation calculation. The initial adjustment value is usually preset according to the standard machining path and machining experience.

[0084] is the curvature value at the i-th control node, which reflects the degree of curvature of the surface at this point. The larger the curvature value, the more severe the bending of the surface at this point, and more compensation is required to ensure machining accuracy. The curvature value can be obtained through geometric analysis of the surface.

[0085] is the sensitivity coefficient of the i-th control node, indicating the sensitivity of this node to compensation adjustment. The sensitivity coefficient is obtained through sensitivity evaluation of the control node set and reflects the importance of each control node in machining. The higher the sensitivity coefficient, the greater the influence of this node on machining quality and the more compensation is required.

[0086] The sub-region compensation calculation can divide the entire surface into several small regions, and for each region, compensation is carried out according to its sensitivity to the machining quality. This means that in the compensation calculation, regions with larger surface changes will receive more compensation, while regions with smaller changes will have the compensation amount reduced.

[0087] After obtaining the compensation information for each region, boundary smoothing is performed. The main goal of this step is to solve the connection problem between compensation regions and avoid sudden changes or discontinuities at the compensation boundaries. If there are drastic changes at the compensation boundaries, it will cause vibrations during the machining process and affect the machining quality.

[0088] Through boundary smoothing, the change of compensation information will be smoother, and the control signal will not have a sudden jump between different regions. This smoothing process is usually based on mathematical interpolation methods or surface fitting algorithms, making the transition between different regions more natural and avoiding machining errors in the transition region.

[0089] After ensuring the smooth transition of axis linkage, the next step is speed planning. This process is to optimize the axis movement speed in the machining path to ensure that each path segment can be completed smoothly and efficiently.

[0090] Speed planning is mainly carried out based on the surface shape, the distribution of control points, and the compensation information. For some regions, a slower speed may be required to ensure high-precision machining, while in other regions, a faster speed can be selected to improve machining efficiency. The core of speed planning is to improve machining efficiency without sacrificing machining accuracy.

[0091] The axis linkage speed optimization parameters are a set of parameters obtained through speed planning, and these parameters provide precise speed control standards for the machining equipment to guide the movement of the axes during the machining process.

[0092] After speed planning, acceleration constraints also need to be imposed on the speed optimization parameters. The purpose of acceleration constraint is to limit the speed change rate during axis linkage and avoid excessive changes during acceleration and deceleration, which will affect the machining smoothness. Excessive acceleration will cause vibrations, affect the surface quality of the workpiece, and may even damage the equipment.

[0093] Through acceleration constraint, a smooth transition curve for axis linkage is generated, and this curve describes the changes of speed and acceleration over different time periods. Ensuring the smooth transition of the curve makes the movement of the equipment neither too intense nor too slow during the entire machining process.

[0094] After the smooth transition curve is generated, feedback correction is performed on the axis linkage speed optimization parameters. The purpose of feedback correction is to fine-tune the speed optimization parameters according to the real-time feedback information during the machining process. During the machining process, the operating state of the machine may be affected by various factors, such as changes in materials and machine wear. Therefore, feedback correction can adjust the parameters in real time to ensure that the machining process is always in the optimal state.

[0095] Through this correction process, a real-time compensation instruction for axis linkage is obtained. This instruction is a signal used to control the machining equipment, guiding the machine to precisely adjust the axis linkage path. The real-time compensation instruction ensures that after each adjustment, the machine always processes according to the predetermined path and speed, avoiding errors.

[0096] Based on the real-time compensation instruction, path synthesis conversion is performed on the initial axis linkage adjustment plan. Path synthesis conversion is to apply the real-time compensation information to the adjustment of the machining path, and finally obtain an optimized machining path. This path incorporates all adjustment factors, including curvature analysis, compensation calculation, speed optimization, and acceleration constraints, ensuring the optimality of the machining path.

[0097] Through this complete set of optimization solutions, the finally obtained axis linkage optimization machining strategy can ensure that every step in the machining process is under control, guaranteeing high-precision and high-efficiency machining results.

[0098] In this embodiment, by performing curvature analysis on the surface quality prediction area, the changing characteristics of the surface geometry can be accurately grasped, so as to make targeted adjustments during the machining process. This method ensures the machining quality and avoids machining errors caused by the complex surface morphology. By generating the local curvature feature distribution map, the key control areas can be effectively identified, and the control points can be accurately selected, thereby improving the accuracy of surface machining. During the sensitivity evaluation process of the axis linkage control node set, the influence of each node on the machining accuracy can be quantified, making the compensation in different areas more reasonable and efficient. This avoids over-compensation in insensitive areas, thereby improving the efficiency and stability of the machining process. Through sub-region compensation calculation and boundary smoothing, it can be ensured that the transition between different regions is smoother, avoiding vibration or surface quality problems caused by poor compensation transition in traditional methods. Speed planning and acceleration constraints further optimize the machining path, improving the machining efficiency while ensuring the smoothness and accuracy during the machining process.

[0099] Refer to Figure 2 As shown, the present invention also provides a real-time control device for the machining path of an axis linkage surface component, which is applied to the real-time control method for the machining path of an axis linkage surface component in any one of the above, and includes: The acquisition module is used to obtain the geometric data and processing technology parameters of the axis-linked surface component, and perform axis-linked decomposition to obtain the multi-axis linkage machining path; The analysis module is used to obtain the real-time operation parameters of the processing equipment and the tool-workpiece contact data, and perform deviation matching on the multi-axis linkage machining path to obtain the real-time axis control instruction set and the axis position compensation trend curve; The association module is used to optimize the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain the initial axis-linked adjustment plan; The processing module is used to evaluate the surface accuracy of the tool-workpiece contact data, and perform surface prediction in combination with the axis position compensation trend curve to obtain the surface quality prediction area; The control module is used to coordinately optimize the initial axis-linked adjustment plan according to the surface quality prediction area to obtain the axis-linked optimized machining strategy.

[0100] A real-time control system for the machining path of an axis-linked surface component provided by the present invention can more comprehensively evaluate the characteristic requirements of complex surface machining by obtaining the geometric data and processing technology parameters of the axis-linked surface component and performing axis-linked decomposition, thereby improving the accuracy of machining path planning and providing more reliable basic data for the real-time control system. By obtaining the real-time operation parameters of the processing equipment and the tool-workpiece contact data in real time, and performing deviation matching to obtain the real-time axis control instruction set and the position compensation trend curve, it realizes the refined monitoring and management of the dynamic changes during the machining process, helps to achieve dynamic responsive machining control, and avoids machining accuracy loss. Optimizing the path of the axis position compensation trend curve based on the real-time axis control instruction set can ensure the efficient operation of the system in different machining states, reduce the path deviation during the machining process, and improve the overall machining accuracy and stability of the system. By evaluating the surface accuracy of the tool-workpiece contact data and performing surface prediction in combination with the axis position compensation trend curve, a more reasonable surface quality prediction area is formulated, thereby effectively avoiding the possible surface quality problems during the machining process, giving early warnings and making adjustments. Coordinately optimizing the initial axis-linked adjustment plan according to the surface quality prediction area can flexibly adjust the axis-linked control strategy according to the characteristics of different machining stages and the changes in real-time requirements, making the machining system more adaptable to the diverse machining requirements of complex surface components, and significantly improving the machining efficiency and product quality.

[0101] Refer to Figure 3 As shown in A memory for storing programs; A processor for executing programs to implement the steps of the real-time control method for the machining path of an axis-linked surface component according to any one of the above.

[0102] In this embodiment, the processor and the memory can be connected through a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0103] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0104] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A real-time control method for the machining path of an axis-linked curved surface component, characterized in that, Including: Obtain the geometric data and machining process parameters of the axis-linked surface component, and perform axis-linked decomposition to obtain a multi-axis linked machining path; Obtain the real-time operating parameters of the machining equipment and the tool-workpiece contact data, and perform deviation matching on the multi-axis linked machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve; Optimize the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis-linked adjustment plan; Evaluate the surface accuracy of the tool-workpiece contact data, and combine it with the axis position compensation trend curve to perform surface prediction to obtain a surface quality prediction area; Coordinate and optimize the initial axis-linked adjustment plan according to the surface quality prediction area to obtain an axis-linked optimized machining strategy.

2. The real-time control method for the machining path of the shaft-linked curved surface component according to claim 1, characterized in that The obtaining of the geometric data and machining process parameters of the axis-linked surface component, and performing axis-linked decomposition to obtain a multi-axis linked machining path includes: Perform parametric processing on the geometric data to obtain a surface mathematical expression; Perform geometric feature analysis based on the surface mathematical expression and the machining process parameters to obtain a surface area classification result; Calculate the tool path density and feed rate according to the surface area classification result and the machining process parameters to obtain tool motion control parameters; Generate a tool path for the surface area based on the tool motion control parameters to obtain a machining trajectory data set; Perform axial decomposition on the machining trajectory data set and the machining process parameters to obtain preliminary axis-linked instructions; Perform axis motion error compensation calculation based on the preliminary axis-linked instructions and the machining process parameters to obtain compensated axis motion data; Perform machining path analysis on the compensated axis motion data to obtain a multi-axis linked machining path.

3. The real-time control method for the machining path of the shaft-linked curved surface component according to claim 1, characterized in that, The obtaining of the real-time operating parameters of the machining equipment and the tool-workpiece contact data, and performing deviation matching on the multi-axis linked machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve includes: Perform real-time acquisition of the position, speed, and acceleration of each axis of the machining equipment to obtain an original operating parameter matrix; Perform filtering processing and data normalization on the original operating parameter matrix to obtain the real-time operating parameters; Collect tool cutting data of the machining equipment according to the real-time operating parameters to obtain the tool-workpiece contact data; Perform time-domain and frequency-domain analysis on the tool-workpiece contact data to obtain a cutting state characteristic spectrum; Perform correlation analysis on the real-time operating parameters and the cutting state characteristic spectrum, and perform registration calculation with the multi-axis linked machining path to obtain the real-time axis control instruction set; Compare the deviation between the real-time axis control instruction set and the preset machining path target trajectory points to obtain a trajectory deviation vector field; Extract the trend of the trajectory deviation vector field, and perform priority sorting according to the preset compensation requirements of each axis to obtain the axis position compensation trend curve.

4. The real-time control method for the machining path of the shaft-linked curved surface component according to claim 3, wherein The performing of correlation analysis on the real-time operating parameters and the cutting state characteristic spectrum, and performing registration calculation with the multi-axis linked machining path to obtain the real-time axis control instruction set includes: Construct a multi-layer correlation matrix based on the real-time operating parameters and the cutting state feature spectrum to obtain a process motion coupling data structure; Perform singular value decomposition on the process motion coupling data structure to obtain the main process motion feature components and the process motion weight coefficients; Perform piecewise interpolation and local coordinate system transformation on the multi-axis linkage machining path according to the main process motion feature components to obtain a set of path adaptation points; Optimize each axis for the set of path adaptation points and the process motion weight coefficients to obtain a matrix of axis compensation amounts; Modify the tool feed path according to the matrix of axis compensation amounts to obtain modified tool pose parameters; Convert the modified tool pose parameters into servo control instructions for each axis and perform dynamic constraint verification to obtain the real-time axis control instruction set.

5. The real-time control method for the machining path of the shaft-linked curved surface component according to claim 1, wherein, Optimize the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan, including: Perform spectral density analysis on the axis position compensation trend to obtain an axis position frequency spectrum matrix; Perform curve fitting on the real-time axis control instruction set according to the axis position frequency spectrum matrix to obtain an axis position compensation trend curve; Perform segmentation processing on the axis position compensation trend curve to obtain a set of segmented compensation curves; Construct a spline interpolation function based on the set of segmented compensation curves to obtain an axis position compensation function; Perform constraint optimization on the axis position compensation function to obtain an axis position compensation instruction; Perform vector calculation according to the axis position compensation instruction to obtain an axis position compensation vector; Perform axis linkage interference inspection on the axis position compensation vector to obtain a non-interference compensation plan; Fuse the instructions for the non-interference compensation plan according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan.

6. The real-time control method for the machining path of the shaft-linked curved surface component according to claim 1, characterized in that Evaluate the surface accuracy of the tool-workpiece contact data and perform surface prediction in combination with the axis position compensation trend curve to obtain a surface quality prediction region, including: Sample the normal cutting depth and tangential speed of the tool-workpiece contact data to obtain a surface quality feature vector; Perform local differential geometry calculation on the tool-workpiece contact data according to the surface quality feature vector to obtain a surface curvature distribution map; Perform surface topography grid subdivision on the tool-workpiece contact data according to the surface curvature distribution map to obtain a surface accuracy evaluation grid; Screen the node deviations of the surface accuracy evaluation grid to obtain a set of target surface quality control points; Perform surface correlation mapping according to the set of target surface quality control points and the axis position compensation trend curve to obtain a compensation influence region; Perform iterative simulation calculation of surface quality on the compensation influence region to obtain a surface quality probability distribution cloud map; Perform quality level division on the surface quality probability distribution cloud map according to the axis position compensation trend curve to obtain a surface quality prediction region; Extract the boundary and perform topological optimization on the surface quality prediction region to obtain the surface quality prediction region.

7. The real-time control method for the machining path of the shaft-linked curved surface component according to claim 1, characterized in that, Coordinate and optimize the initial axis linkage adjustment plan according to the surface quality prediction region to obtain an axis linkage optimized machining strategy, including: Perform curvature analysis on the surface quality prediction region to obtain a local curvature feature distribution map; Extract control points from the surface quality prediction region according to the local curvature feature distribution map to obtain an axis linkage control node set; Evaluate the sensitivity of the axis linkage control node set to obtain an axial compensation sensitivity matrix; Perform sub-region compensation calculation on the initial axis linkage adjustment plan according to the axial compensation sensitivity matrix to obtain axis linkage sub-region compensation information; Smooth the boundaries of the axis linkage sub-region compensation information to obtain axis linkage continuous transition control information; Perform speed planning on the axis linkage sub-region compensation information according to the axis linkage continuous transition control information to obtain axis linkage speed optimization parameters; Apply acceleration constraints to the axis linkage speed optimization parameters to obtain an axis linkage smooth transition curve; Perform feedback correction on the axis linkage speed optimization parameters according to the axis linkage smooth transition curve to obtain an axis linkage real-time compensation instruction; Perform path synthesis conversion on the initial axis linkage adjustment plan according to the axis linkage real-time compensation instruction to obtain the axis linkage optimized machining strategy.

8. A real-time control device for the machining path of an axis-linked curved surface component, characterized in that, Applied to the real-time control method for the machining path of the axis linkage surface component according to any one of claims 1-7, it includes: An acquisition module, which is used to obtain the geometric data and machining process parameters of the axis linkage surface component, and perform axis linkage decomposition to obtain a multi-axis linkage machining path; An analysis module, which is used to obtain the real-time operation parameters of the machining equipment and the tool-workpiece contact data, and perform deviation matching on the multi-axis linkage machining path to obtain a real-time axis control instruction set and an axis position compensation trend curve; An association module, which is used to optimize the path of the axis position compensation trend curve according to the real-time axis control instruction set to obtain an initial axis linkage adjustment plan; A processing module, which is used to evaluate the surface accuracy of the tool-workpiece contact data, and combine the axis position compensation trend curve to perform surface prediction to obtain a surface quality prediction region; A control module, which is used to coordinately optimize the initial axis linkage adjustment plan according to the surface quality prediction region to obtain an axis linkage optimized machining strategy.

9. A real-time control system for the machining path of an axis-linked curved surface component, characterized in that, It includes: A memory for storing programs; A processor for executing the program to implement each step of a real-time control method for the machining path of an axis linkage surface component according to any one of claims 1-7.

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