Milling cutter machining method and system based on CAM simulation technology

Abstract

The invention relates to a milling cutter machining method and system based on a CAM simulation technology, and the method comprises the steps: obtaining three-dimensional data of a workpiece mold and milling cutter parameters, and carrying out the process planning, and obtaining an initial workpiece machining strategy; cutter path planning and contact point calculation are conducted on the initial workpiece machining strategy, and a corresponding cutter movement path is obtained; carrying out CAM simulation analysis on the initial workpiece machining strategy to obtain corresponding real-time simulation data, and carrying out collision interference detection on the tool movement track according to the real-time simulation data to obtain a corresponding safe movement path; carrying out cutting force and cutting temperature analysis on the safe motion path to obtain a corresponding process verification result; and carrying out machining analysis on the safe motion path and the cutter predicted use data, and carrying out NC machining code optimization on the initial workpiece machining strategy to obtain a corresponding optimized workpiece machining scheme. The machining strategy can be flexibly adjusted according to the characteristics and machining requirements of different workpieces.

Description

Technical Field

[0001] The invention relates to the technical field of milling cutter processing, and in particular to a milling cutter processing method and system based on CAM simulation technology. Background Art

[0002] As an important technical means of modern manufacturing, CNC machining plays a key role in industrial production. With the continuous improvement of the manufacturing industry's requirements for machining accuracy and efficiency, how to accurately predict and optimize the milling process to achieve high-quality and high-efficiency machining has become one of the key research directions. The current milling machining method mainly relies on traditional process parameter settings and experience accumulation, and often only focuses on a single machining parameter, such as cutting speed or feed rate, while ignoring the dynamic characteristics of the machining process and the coupling relationship between multiple process parameters. This simplified method is prone to unstable machining quality and inaccurate tool life prediction, and it is also difficult to effectively avoid collision and interference problems during the machining process. In addition, due to the lack of systematic simulation analysis and optimization methods, the existing machining methods are difficult to achieve precise control of process parameters, which in turn affects the final machining effect and production efficiency. Summary of the invention

[0003] The main purpose of the present invention is to provide a milling cutter processing method and system based on CAM simulation technology, which can flexibly adjust the processing strategy according to the characteristics and processing requirements of different workpieces.

[0004] To achieve the above object, the present invention provides a milling cutter processing method based on CAM simulation technology, comprising: Obtain the workpiece mold 3D data and milling cutter parameters, and perform process planning to obtain the initial workpiece processing strategy; Performing tool trajectory planning and contact point calculation on the initial workpiece machining strategy to obtain a corresponding tool motion trajectory; Performing CAM simulation analysis on the initial workpiece machining strategy to obtain corresponding real-time simulation data, and performing collision interference detection on the tool motion trajectory based on the real-time simulation data to obtain a corresponding safe motion path; Perform cutting force and cutting temperature analysis on the safe motion path to obtain corresponding process verification results; Performing workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, and performing tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool prediction usage data; The safe motion path and the tool prediction usage data are processed and analyzed, and the initial workpiece processing strategy is optimized by NC processing code to obtain a corresponding optimized workpiece processing plan.

[0005] Furthermore, the acquisition of workpiece mold three-dimensional data and milling cutter parameters, and process planning to obtain an initial workpiece processing strategy includes: Performing surface analysis on the three-dimensional data of the workpiece mold to obtain corresponding workpiece surface data; Performing model analysis on the workpiece mold three-dimensional data based on the workpiece surface data to obtain corresponding mold three-dimensional data; Performing preliminary construction based on the three-dimensional mold data and the workpiece surface data to obtain a corresponding initial workpiece mold model; Performing a processing difficulty analysis on the initial workpiece mold model to obtain regional processing complexity data; Matching and calculating the regional processing complexity data according to the milling cutter parameters to obtain a processing specification parameter set; Performing a partition processing plan on the initial workpiece mold model according to the processing specification parameter set to obtain a regional processing order; Calculating the tool entry and exit paths for the processing sequence of the area to obtain the tool's spatial conversion trajectory; Calculating the machining feed rate and the spindle speed according to the spatial conversion trajectory to obtain corresponding process parameters; The process parameters and the processing sequence are combined and optimized to obtain an initial workpiece processing strategy.

[0006] Furthermore, the tool trajectory planning and contact point calculation are performed on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory, including: Performing machining feature analysis on the initial workpiece machining strategy to obtain contour data of the area to be machined; Performing parameter curve discretization processing on the contour data to obtain a surface sampling point set; Performing spline curve fitting on the surface sampling point set to obtain a tool path curve; Calculate the curvature radius of the sampling point according to the tool path curve to obtain corresponding turning angle parameter data; Performing speed calculation on the rotation angle parameter data to obtain the feed speed of each track segment; Sampling the tool path curve at time intervals according to the feed speed to obtain a tool position point sequence; Performing tool axis vector calculation on the tool position point sequence to obtain spatial posture data; Performing tool contact area interference check based on the spatial posture data to obtain an effective cutting boundary; Performing local trajectory optimization on the effective cutting boundary to obtain a contact point sequence; The contact point sequence and the feed speed are combined and mapped to obtain the tool motion trajectory.

[0007] Further, the CAM simulation analysis is performed on the initial workpiece processing strategy to obtain corresponding real-time simulation data, and collision interference detection is performed on the tool motion trajectory according to the real-time simulation data to obtain a corresponding safe motion path, including: Performing dynamic simulation processing on the initial workpiece processing strategy to obtain the real-time simulation data; Performing path analysis on the tool motion trajectory according to the real-time simulation data to obtain corresponding tool position sequence data; Performing envelope surface calculation on the tool position sequence data to obtain corresponding tool swept volume data; Performing Boolean operation on the real-time simulation data according to the tool sweep volume data to obtain corresponding material removal volume data; Performing spatial collision detection on the material removal volume data to obtain corresponding collision detection data; Performing path optimization calculation on the tool position sequence data according to the collision detection data to obtain corresponding obstacle avoidance path data; Performing smoothing processing on the obstacle avoidance path data to obtain a corresponding smooth transition path; The speed of the tool motion trajectory is planned according to the smooth transition path to obtain a corresponding safe motion path.

[0008] Furthermore, the cutting force and cutting temperature analysis of the safe motion path to obtain corresponding process verification results includes: Sampling the tool contact point trajectory on the safe motion path to obtain corresponding sampling data point coordinates; Calculate the instantaneous cutting angle of the tool according to the coordinates of the sampling data points to obtain corresponding cutting-in and cutting-out angle data; Performing geometric dimension cutting calculation on the cutting-in and cutting-out angle data to obtain corresponding cutting cross-sectional area data; Calculate the unit cutting amount according to the cutting cross-sectional area data to obtain the corresponding cutting force value; Performing moment decomposition operation on the cutting force value to obtain corresponding three-dimensional cutting force components; Calculate cutting power according to the three-dimensional cutting force components to obtain corresponding cutting heat data; Performing heat distribution calculation on the cutting heat data to obtain a corresponding cutting temperature value; A machining simulation verification analysis is performed based on the three-dimensional cutting force components and the cutting temperature values ​​to obtain the process verification result.

[0009] Further, the performing workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, and performing tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool prediction usage data, including: Performing three-dimensional surface analysis on the real-time simulation data to obtain three-dimensional surface morphology data; Performing workpiece surface roughness analysis based on the three-dimensional surface topography data to obtain workpiece surface roughness distribution data; Performing characteristic statistics on the workpiece surface roughness distribution data to obtain workpiece surface quality characteristic parameters; Verifying and associating the workpiece surface quality characteristic parameters according to the process verification results to obtain tool surface quality associated parameters; Performing wear prediction on the tool surface quality associated parameters to obtain tool wear prediction parameters; Performing temperature correction on the tool wear prediction parameters according to the process verification results to obtain temperature compensation data; The tool wear prediction parameters and the temperature compensation data are comprehensively calculated and processed to obtain the tool prediction usage data.

[0010] Furthermore, the processing analysis of the safe motion path and the tool prediction usage data is performed, and the NC processing code of the initial workpiece processing strategy is optimized to obtain a corresponding optimized workpiece processing plan, including: Performing curvature analysis on the safe motion path to obtain corresponding path curvature data; Performing distribution correlation calculation on the tool prediction usage data according to the path curvature data to obtain a corresponding tool load distribution diagram; Performing threshold stratification on the tool load distribution diagram to obtain corresponding machining load areas; Optimizing the feed speed parameters of the safe motion path according to the machining load area to obtain a corresponding speed optimization trajectory; Performing motion characteristic analysis on the speed optimization trajectory to obtain corresponding axial motion parameters; Performing jitter correction calculation on the speed optimization trajectory according to the axial motion parameters to obtain a corresponding stable motion path; Performing tool posture compensation processing on the stable motion path to obtain a corresponding posture compensation trajectory; Generate a machining instruction sequence according to the posture compensation trajectory to obtain a corresponding optimized NC code; The initial workpiece machining strategy is post-optimized according to the optimized NC code to obtain the optimized workpiece machining plan.

[0011] Furthermore, the processing instruction sequence is generated according to the posture compensation trajectory to obtain the corresponding optimized NC code, including: Processing the posture compensation trajectory in segments to obtain corresponding trajectory segment data; Performing speed planning calculation on the trajectory segment data to obtain a corresponding feed speed curve; Performing machining trajectory interpolation calculation according to the feed speed curve to obtain a corresponding interpolation point sequence; Performing tool compensation calculation on the interpolation point sequence to obtain corresponding tool position coordinates; Performing an inverse calculation on the axial motion of the machine tool according to the tool position coordinates to obtain the corresponding displacement of each axis; Performing encoding conversion processing on the motion displacement of each axis to obtain a corresponding machine tool code sequence; Integrate the machining parameters according to the machine tool code sequence to obtain the corresponding optimized NC code; The inverse solution formulas include: ; ; ; ; ; A and B are the rotation axis angles, X, Y, and Z are the axial positions of the machine tool, i, j, and k represent the tool direction vectors, L is the tool length, x is the target position of the tool tip in the X-axis direction of the workpiece coordinate system, y is the target position of the tool tip in the Y-axis direction of the workpiece coordinate system, z is the target position of the tool tip in the Z-axis direction of the workpiece coordinate system, and atan2 is the inverse tangent function.

[0012] The present invention further provides a milling cutter processing system based on CAM simulation technology, which is applied to any of the above-mentioned milling cutter processing methods based on CAM simulation technology, comprising: An acquisition module, which is used to obtain the three-dimensional data of the workpiece mold and the parameters of the milling cutter, and to perform process planning to obtain an initial workpiece processing strategy; An analysis module, the analysis module is used to perform tool trajectory planning and contact point calculation on the initial workpiece machining strategy to obtain a corresponding tool motion trajectory; An association module, the association module is used to perform CAM simulation analysis on the initial workpiece processing strategy to obtain corresponding real-time simulation data, and perform collision interference detection on the tool motion trajectory according to the real-time simulation data to obtain a corresponding safe motion path; A processing module, the processing module is used to analyze the cutting force and cutting temperature of the safe motion path to obtain a corresponding process verification result; A control module, the control module is used to perform workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, perform tool prediction on the workpiece surface quality information based on the process verification result, and obtain corresponding tool prediction usage data; An execution module is used to perform processing analysis on the safe motion path and the tool prediction usage data, and to optimize the NC processing code of the initial workpiece processing strategy to obtain a corresponding optimized workpiece processing plan.

[0013] The present invention provides a milling cutter processing method and system based on CAM simulation technology, which has the following beneficial effects: By acquiring the three-dimensional data of the workpiece mold and the milling cutter parameters for process planning, combined with tool trajectory planning and contact point calculation, the various parameters in the processing process can be evaluated more accurately, thereby improving the reliability of the processing plan and providing a more scientific basis for actual processing. Through CAM simulation analysis to obtain real-time simulation data and perform collision interference detection, the precise control and optimization of the tool motion trajectory are achieved, effectively avoiding collision problems during the processing process and ensuring processing safety. Process verification based on cutting force and cutting temperature analysis can ensure stable operation under different processing conditions, reduce processing quality fluctuations, and improve processing accuracy. By analyzing the workpiece surface quality information and combining the tool prediction usage data, a more reasonable tool usage strategy is formulated, and the optimized operation of the entire processing process is achieved through NC processing code optimization, thereby effectively improving processing efficiency and extending tool life. By comprehensively considering the coupling relationship between multiple process parameters, the processing strategy can be flexibly adjusted according to the characteristics and processing requirements of different workpieces, which can better adapt to diverse processing needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a milling cutter processing method based on CAM simulation technology provided by the present invention; Figure 2 It is a structural diagram of a milling cutter processing system based on CAM simulation technology provided by the present invention.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides a milling cutter processing method based on CAM simulation technology, comprising: Step S1: Obtain workpiece mold three-dimensional data and milling cutter parameters, and perform process planning to obtain an initial workpiece processing strategy; Step S2: performing tool trajectory planning and contact point calculation on the initial workpiece processing strategy to obtain the corresponding tool motion trajectory; Step S3: Perform CAM simulation analysis on the initial workpiece processing strategy to obtain corresponding real-time simulation data, perform collision interference detection on the tool motion trajectory based on the real-time simulation data, and obtain a corresponding safe motion path; Step S4: Analyze the cutting force and cutting temperature of the safe motion path to obtain the corresponding process verification results; Step S5: performing workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, performing tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool prediction usage data; Step S6: Perform processing analysis on the safe motion path and the tool prediction usage data, and optimize the NC processing code of the initial workpiece processing strategy to obtain the corresponding optimized workpiece processing plan.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Obtain a complete three-dimensional data model of the workpiece mold through three-dimensional modeling software or three-dimensional scanning equipment, including basic information such as the workpiece's geometric dimensions, shape characteristics, and material properties. At the same time, collect key parameters of the milling cutter, such as tool type, tool material, tool geometry (tool diameter, number of blades, front angle, back angle, etc.), and cutting parameters (spindle speed, feed speed, cutting depth, etc.). Based on the acquired workpiece and tool information, carry out detailed process planning, including determining the processing benchmark, process arrangement, fixture design, roughing and finishing plans, etc. In the process of process planning, factors such as workpiece material characteristics, processing accuracy requirements, and surface quality requirements are involved, and cutting parameters and processing methods are reasonably selected. By analyzing the structural characteristics of the workpiece, different processing areas are divided, and corresponding processing strategies are formulated for each area, such as determining the appropriate tool combination, optimizing the cutting path, and adjusting the cutting parameters. Finally, an initial workpiece processing strategy plan containing complete processing information is formed.

[0020] Step S2: Based on the initial processing strategy, detailed tool trajectory planning is performed. According to the geometric features and processing requirements of the workpiece, the appropriate cutting method is determined, such as forward milling or reverse milling, equal height processing or equal parameter processing. Then the tool path is generated using CAM software, including determining parameters such as feed direction, step size, and cutting-in and cutting-out methods. In the trajectory planning process, the focus is on calculating the contact point position between the tool and the workpiece surface, which requires considering the spatial relationship between the actual geometric shape of the tool and the workpiece surface. The tool posture at each processing point position is determined by numerical calculation methods, including the position coordinates and direction vector of the tool. At the same time, the connection transition between adjacent tool paths needs to be considered to ensure the continuity and stability of the cutting process. For the processing of complex surfaces, local curvature analysis is also required to adjust the cutting parameters and tool posture according to the surface characteristics to ensure processing accuracy and efficiency.

[0021] Step S3: After completing the tool path planning, use CAM simulation software to perform virtual simulation of the entire machining process. During the simulation process, various parameters of the tool movement process are calculated and displayed in real time, including tool position, posture, cutting status and other information. Through simulation, the relative position relationship between the tool and the workpiece and the fixture can be intuitively observed, and possible collision interference problems can be discovered in time. Collision detection and analysis are performed on each tool position point, including interference checks on multiple aspects such as tool and workpiece, tool and fixture, and tool handle and workpiece. When a potential collision risk is found, the original trajectory needs to be optimized and adjusted. Possible adjustment methods include changing the tool feed direction, adjusting the tool inclination angle, and adding transition actions. The optimized path needs to be simulated and verified again to ensure that all motion trajectories are safe and reliable. The final generated safe motion path must not only ensure the processing quality, but also ensure the safety and reliability of the entire processing process.

[0022] Step S4: Based on the safe motion path, analyze the cutting force and cutting temperature. First, establish a mechanical model of the cutting process, consider factors such as workpiece material properties, tool geometry parameters and cutting parameters, and calculate the main cutting force, feed force and back force in the cutting process. Use finite element analysis software to simulate the stress distribution and deformation in the cutting process and evaluate the stability of the cutting process. At the same time, establish a cutting thermal coupling model to analyze the temperature field distribution in the cutting area, including temperature changes in the tool-workpiece contact area, heat distribution ratio, cooling effect, etc. It is necessary to consider the effects of parameters such as cutting speed, feed rate, and cutting depth on cutting temperature, and evaluate the effects of high temperature on tool wear and workpiece surface quality. Based on the analysis results, complete process verification data can be obtained, including the cutting force change curve over time, temperature field distribution diagram, stress strain data, etc., which will provide an important basis for subsequent process optimization.

[0023] Step S5: Analyze the surface of the workpiece after processing, including the evaluation of quality indicators such as surface roughness, shape accuracy, and dimensional accuracy. By processing the simulation data, the microscopic morphological characteristics of the workpiece surface, such as cutting texture, surface defects and other information, can be obtained. Combined with the process verification results, a tool wear prediction model is established to analyze the influence of cutting force and cutting temperature on tool life. The prediction model needs to consider multiple factors, such as cutting time, cutting conditions, and workpiece material properties. Through data analysis and pattern recognition methods, the correlation between tool wear and processing quality is established to predict the tool status at different processing stages. These prediction data can be used to estimate the remaining service life of the tool and determine when the tool needs to be replaced or the cutting parameters need to be adjusted, thereby ensuring the stability of the processing quality.

[0024] Step S6: Comprehensively evaluate all the data and analysis results obtained previously. Combine the safe motion path with the tool prediction usage data to evaluate the feasibility and economy of the entire machining process. Based on the evaluation results, optimize the initial workpiece machining strategy, including adjusting cutting parameters, optimizing tool paths, improving machining procedures, etc. The focus is on optimizing the NC machining code, which includes optimizing feed speed distribution, adjusting cutting parameters, improving tool advance and retract strategies, etc. The optimization process needs to consider multiple goals, such as improving machining efficiency, extending tool life, and ensuring machining accuracy. Through the multi-objective optimization algorithm, the final optimized machining plan is generated, which should be able to balance the requirements of multiple aspects such as machining efficiency, machining quality and machining cost. Finally, the optimized NC code and complete process files are output to provide detailed operation guidance for actual machining.

[0025] The present invention provides a milling cutter processing method based on CAM simulation technology. By acquiring workpiece mold three-dimensional data and milling cutter parameters for process planning, combined with tool trajectory planning and contact point calculation, various parameters in the processing process can be more accurately evaluated, thereby improving the reliability of the processing plan and providing a more scientific basis for actual processing. Real-time simulation data is obtained through CAM simulation analysis, and collision interference detection is performed, so that accurate control and optimization of the tool motion trajectory are achieved, collision problems in the processing process are effectively avoided, and processing safety is guaranteed. Process verification based on cutting force and cutting temperature analysis can ensure stable operation under different processing conditions, reduce processing quality fluctuations, and improve processing accuracy. By analyzing the workpiece surface quality information and combining the tool prediction usage data, a more reasonable tool use strategy is formulated, and the optimized operation of the entire processing process is achieved through NC processing code optimization, thereby effectively improving processing efficiency and extending the tool life. By comprehensively considering the coupling relationship between multiple process parameters, the processing strategy can be flexibly adjusted according to the characteristics and processing requirements of different workpieces, and it can be more adaptable to diversified processing needs.

[0026] In one embodiment, the three-dimensional data of the workpiece mold and the parameters of the milling cutter are obtained, and process planning is performed to obtain an initial workpiece processing strategy, including: The workpiece mold is scanned and modeled in all directions by a 3D scanner to generate point cloud data, which is then converted into a 3D mesh model. The milling cutter parameters include basic parameters such as tool diameter, tool length, number of blades, and helix angle, which are determined by the specifications provided by the tool manufacturer.

[0027] When analyzing the workpiece surface, the curvature analysis method is used to extract the surface features of the three-dimensional mesh model, calculate the Gaussian curvature and average curvature of each mesh point, and identify different types of surface features such as planes, convex surfaces, and concave surfaces. Based on the surface feature data and combined with the topological structure of the mesh model, a workpiece surface feature map is constructed to mark the surface type and geometric features of each area.

[0028] In the model analysis stage, the functional areas of the mold are divided according to the surface feature map of the workpiece, and the geometric dimensions and tolerance analysis of different areas are carried out. The mathematical model of each area is established by fitting the surface equation through the least squares method to generate a complete three-dimensional data model of the mold.

[0029] In the process of constructing the initial workpiece mold model, the mold 3D data is registered with the workpiece surface data to establish a coordinate transformation relationship. The Boolean operation method is used to calculate the intersection volume of the workpiece and the mold, determine the material removal area, and generate a blank model with machining allowance.

[0030] The acquisition of regional processing complexity data is based on the following rules: the greater the surface curvature, the higher the processing complexity; the processing complexity of the deep cavity area is higher than that of the open area; the smaller the detail feature size, the higher the processing complexity. By establishing a processing complexity evaluation index system, each area is quantitatively scored.

[0031] The matching calculation of the machining specification parameter set is based on the geometric parameters and cutting ability of the milling cutter to establish a tool-workpiece matching rule library. The rules include: the tool diameter should be smaller than the minimum feature size; the tool length should meet the machining depth requirements; the number of blades should be adapted to the feed speed. According to these rules, the appropriate machining parameter combination is selected for each machining area.

[0032] The planning of regional processing sequence follows the principle of processing from coarse to fine, from simple to difficult. By calculating the topological relationship and processing constraints between regions, constructing a processing sequence dependency graph, and using graph theory algorithms to optimize the processing sequence, the continuity and efficiency of the processing process are ensured.

[0033] The calculation of the tool space conversion trajectory uses the isoparametric curve method to generate tool path points, and the motion parameters of each axis are calculated through the inverse kinematics algorithm. In the trajectory planning, factors such as avoiding interference, reducing empty cutting and protecting the quality of the machined surface are considered to generate an optimized tool motion trajectory.

[0034] The process parameters are determined based on the cutting force model and surface quality requirements, and the appropriate feed rate and spindle speed are calculated. A cutting parameter optimization model is established, and the optimal process parameter combination is solved by comprehensively considering constraints such as material removal rate, tool life, and machining accuracy.

[0035] By combining and optimizing the process parameters and the processing sequence, a multi-objective optimization model is established. The optimization objectives include maximizing processing efficiency, optimizing processing quality, maximizing tool life, etc. An intelligent optimization algorithm is used to solve the optimal processing strategy solution and generate a complete processing process file.

[0036] This embodiment obtains workpiece data through all-round scanning modeling, and combines the curvature analysis method to accurately extract surface features, thereby achieving high-precision digital modeling of complex workpieces. A multi-dimensional processing complexity evaluation system is used to divide the workpiece into regions, and a tool-workpiece matching rule library is established to optimize parameters, which effectively improves the rationality and feasibility of the processing plan. In trajectory planning, the inverse kinematics algorithm and cutting force model are introduced, and multiple constraint factors such as avoiding interference and reducing air cutting are comprehensively considered, which significantly optimizes the tool motion trajectory and ensures the processing quality. By establishing a multi-objective optimization model to combine and optimize process parameters and processing order, while ensuring processing accuracy, maximizing processing efficiency and tool life, the dual goals of improving processing quality and reducing production costs are achieved.

[0037] In one embodiment, tool trajectory planning and contact point calculation are performed on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory, including: Based on the initial workpiece processing strategy, the feature analysis of the area to be processed is performed, and the geometric features of the workpiece surface, including planes, curved surfaces, concave and convex features, etc., are extracted through feature recognition algorithms to obtain the contour data of the area to be processed. The contour data contains the coordinate information and topological relationship of the key points on the workpiece surface.

[0038] The acquired contour data is processed by the parameter curve discretization method. Under the premise of ensuring the accuracy of the curve, the parameter curve is uniformly sampled according to the set step size to generate a surface sampling point set. The density of the sampling point set is determined by the processing accuracy requirements. The higher the accuracy requirements, the denser the sampling points.

[0039] The sampling point set is fitted using the B-spline curve fitting algorithm to construct a smooth and continuous tool path curve. During the fitting process, the control point position and node vector distribution are adjusted to ensure that the error between the fitting curve and the sampling points meets the machining accuracy requirements.

[0040] Calculate the curvature radius at each sampling point on the tool path curve, and combine the tool geometry parameters and process parameters to solve the corresponding angle parameter data. The angle parameter reflects the change law of the tool posture during the processing process.

[0041] According to the angle parameter data, the tool performance parameters, workpiece material characteristics and processing requirements are comprehensively considered to calculate the reasonable feed speed of each trajectory. The calculation of the feed speed must meet the process conditions such as cutting force constraints and surface roughness constraints.

[0042] According to the calculated feed rate, the tool path curve is sampled at equal time intervals to obtain the tool position sequence. The sampling time interval is selected to match the interpolation cycle of the CNC system.

[0043] The tool axis vector is calculated for the tool position sequence to determine the spatial posture of the tool at each position. The tool axis vector is calculated based on the tool geometry model and the workpiece surface normal vector information to ensure that the tool maintains the correct cutting contact relationship with the workpiece surface.

[0044] Combined with the tool geometry model and the workpiece feature model, the tool is subjected to interference check in the cutting area under various postures to determine the effective cutting boundary. The interference check includes local interference check and overall interference check between the tool and the workpiece.

[0045] For the detected interference area, the local trajectory is optimized by adjusting the tool posture and path to generate a non-interference contact point sequence. The optimization process is based on ensuring the processing accuracy and minimizing the tool posture adjustment.

[0046] The optimized contact point sequence is combined with the feed speed data to generate a complete tool motion trajectory. The motion trajectory data contains tool position, posture and speed information for subsequent CNC machining and simulation verification.

[0047] This embodiment achieves accurate identification and positioning of the processing area by accurately analyzing the surface features of the workpiece and extracting contour data. The use of parametric curve discretization combined with the B-spline curve fitting algorithm ensures the continuity and smoothness of the tool path and effectively improves the quality of the processed surface. By calculating the radius of curvature and optimizing the feed speed in combination with process parameters, the processing efficiency is guaranteed and the damage to the tool and workpiece caused by excessive cutting force is avoided. Through the tool axis vector calculation and interference check mechanism, the intelligent adjustment of the tool posture is realized, effectively avoiding interference problems during the processing process. The local trajectory is optimized and the final motion trajectory is generated, which significantly improves the processing efficiency while ensuring the processing accuracy, making the entire processing process more reliable and stable. The overall method not only ensures the processing quality, but also improves the processing efficiency, providing strong support for actual production applications.

[0048] In one embodiment, a CAM simulation analysis is performed on the initial workpiece processing strategy to obtain corresponding real-time simulation data, and collision interference detection is performed on the tool motion trajectory based on the real-time simulation data to obtain a corresponding safe motion path, including: The dynamic simulation processing of the initial workpiece processing strategy uses discrete sampling points for real-time simulation. The dynamic simulation system samples the processing process with a sampling period of 0.001 seconds to obtain real-time simulation data including process parameters such as tool posture, feed speed, and spindle speed. The system uses cubic spline interpolation to interpolate the discrete data points obtained by sampling to form a continuous motion trajectory curve.

[0049] The path analysis of the tool motion trajectory constructs the tool position sequence based on the real-time simulation data. The path analysis module discretizes the interpolated trajectory curve with equal distances in steps of 0.1 mm to generate the tool center point position sequence. The transformation matrix between the tool coordinate system and the workpiece coordinate system is calculated for each position point to form a complete tool posture sequence data structure.

[0050] The calculation of the tool swept volume uses the envelope method to process the position sequence. The system moves the tool geometry model under the control of the posture sequence, and obtains the moving swept volume by calculating the envelope surface of the tool surface family. The envelope surface is solved by the characteristic line method, and the characteristic curve on the envelope surface is obtained by solving the characteristic equation group, and then a complete swept volume model is constructed.

[0051] The calculation of material removal volume adopts Boolean operation method. The system performs Boolean difference operation on the workpiece blank model and the tool sweep volume to obtain the volume model of the material removal area. Boolean operation adopts boundary representation method to divide the workpiece into the retention area and the removal area by calculating the interface between the two entities.

[0052] Spatial collision detection is based on interference analysis of material removal volume model. The detection module uses hierarchical bounding box algorithm to decompose complex collision detection problems into intersection tests of simple geometric bodies. The system records the detected collision points and generates detection data containing information such as collision position and collision depth.

[0053] The optimization of the obstacle avoidance path adopts the artificial potential field method. The optimization module constructs a repulsive field based on the collision detection data, and combines the attractive potential field on the workpiece surface to make local adjustments to the original tool position sequence. The optimal obstacle avoidance path is solved by minimizing the potential field energy function to ensure that the safe distance between the tool and the workpiece is greater than 5mm.

[0054] The path smoothing process uses a fifth-order polynomial fitting method. The smoothing module identifies the mutation points of the obstacle avoidance path and inserts a transition curve in the mutation area. The transition curve uses a fifth-order polynomial function to ensure the continuity of position, velocity, and acceleration, and achieve a smooth transition of the trajectory.

[0055] The speed planning of the safe motion path adopts the "look-ahead-deceleration" strategy. The planning module calculates the maximum allowable feed speed of each segment based on the curvature distribution of the smooth path. The advance deceleration method is adopted at the corner to ensure that the dynamic characteristics of the processing process meet the performance constraints of the machine tool. The speed planning result contains complete processing information such as the position, posture, and feed speed of the path point.

[0056] This embodiment performs CAM simulation analysis on the initial workpiece processing strategy, implements dynamic simulation using discrete sampling points, and combines cubic spline interpolation to form a continuous motion trajectory, which significantly improves the simulation accuracy. The tool sweep volume is calculated based on the envelope surface method, and the boundary representation method is used for Boolean operations to accurately obtain the material removal volume, thereby improving the reliability of the processing process. The hierarchical bounding box algorithm is used for spatial collision detection, and the obstacle avoidance path is optimized in combination with the artificial potential field method, which effectively avoids interference collisions during the processing process. Path smoothing is achieved through quintic polynomial fitting, and the "look-ahead-deceleration" strategy is used for speed planning to ensure the continuity and stability of the processing trajectory. This method not only optimizes the tool motion trajectory, but also realizes precise control of the processing process, reduces processing errors, improves processing efficiency and processing quality, and provides effective technical support for the improvement and optimization of milling cutter processing technology.

[0057] In one embodiment, cutting force and cutting temperature analysis are performed on the safe motion path to obtain corresponding process verification results, including: When analyzing the cutting force and cutting temperature of the safe motion path, the three-dimensional spatial coordinate data is obtained by sampling the tool contact point trajectory. The sampling method adopts the principle of equal arc length sampling, and the sampling point coordinates are extracted at a fixed interval on the tool motion trajectory. The sampling interval is set to 0.1mm to ensure that the sampling accuracy meets the requirements of subsequent analysis.

[0058] Based on the acquired sampling point coordinate data, the instantaneous entry angle and exit angle of the tool during the machining process are calculated. During the calculation process, the angle between the tool axial vector and the workpiece surface normal vector is used as the entry angle reference, and the entry and exit angle values ​​are obtained through vector cross multiplication. The entry angle value ranges from 0° to 180°, and the exit angle value ranges from 180° to 360°.

[0059] The cross-sectional area is calculated based on the calculated cutting-in and cutting-out angle data and the tool geometric parameters. The calculation method is based on the tool tooth profile equation, discretizing the cutting area into micro-element areas, and obtaining the total cutting area by numerical integration. The cutting cross-sectional area data directly affects the cutting force and is an important basis for optimizing process parameters.

[0060] The unit cutting force coefficient method is used to calculate the cutting force based on the cutting cross-sectional area data. The cutting force coefficient is obtained through an empirical formula that takes into account factors such as the workpiece material characteristics, tool material type, and machining process parameters. The calculation result is expressed in Newton (N) as the unit of cutting force.

[0061] The calculated cutting force is decomposed into three components to obtain the values ​​of tangential force, radial force and axial force respectively. The decomposition process is established based on the tool coordinate system, and the cutting force is projected to the corresponding direction through the coordinate transformation matrix. The three-dimensional force component data is used to evaluate the tool force state and processing stability.

[0062] The cutting power is calculated using the three-dimensional cutting force component data, and the cutting power is converted into cutting heat. During the calculation process, 90% of the cutting power is converted into heat, and the remaining energy is lost in the form of vibration, noise, etc. The cutting heat data is expressed in joules (J).

[0063] The cutting heat distribution is calculated to determine the heat ratio flowing into the tool, workpiece and chips. The heat distribution ratio is determined by the thermophysical properties of the material, and the temperature field distribution in the cutting area is obtained by solving the heat conduction equation. The calculation result is expressed in degrees Celsius (℃) as the unit of the cutting temperature value.

[0064] The process verification is carried out by combining the three-dimensional cutting force components and cutting temperature values. The verification standards include: the cutting force should be less than 80% of the maximum allowable cutting force of the tool, the cutting temperature should not exceed 70% of the heat resistance temperature of the tool material, and the cutting power should be within the rated power range of the machine tool. The process verification results serve as an important basis for evaluating the feasibility of the processing plan.

[0065] This embodiment realizes comprehensive process verification of the milling cutter machining process by analyzing the cutting force and cutting temperature of the safe motion path. The tool contact point trajectory coordinate data is obtained based on the equal arc length sampling principle, and the accuracy of the process parameter calculation is ensured by combining the cut-in and cut-out angle calculation and cross-sectional area analysis. The unit cutting force coefficient method is used to calculate the cutting force and decompose it into three-dimensional components, which accurately reflects the actual force state of the tool. Through the calculation of cutting power and heat distribution, the temperature field distribution during the machining process is accurately predicted.

[0066] In one embodiment, the workpiece surface analysis is performed on the real-time simulation data to obtain the corresponding workpiece surface quality information, and the tool prediction is performed on the workpiece surface quality information based on the process verification result to obtain the corresponding tool prediction usage data, including: After obtaining the real-time simulation data of milling cutter processing, the workpiece surface is scanned and measured point by point through a 3D laser scanning device, with a scanning accuracy of 0.1μm and a scanning interval of 0.5mm. The point cloud data obtained by scanning is filtered and reconstructed to generate 3D morphology data of the workpiece surface. The 3D morphology data contains the height information, shape characteristics and microstructure characteristics of the workpiece surface.

[0067] Based on the reconstructed 3D topography data, the arithmetic mean deviation Ra, maximum profile height Rz and other parameters in the ISO4287 standard are used to quantitatively evaluate the workpiece surface roughness. By sampling and analyzing different areas of the workpiece surface, a roughness distribution cloud map is drawn, and the roughness values ​​and distribution laws of each area are recorded to form the workpiece surface roughness distribution data. The roughness evaluation uses 16 sampling lengths, each of which is 4 mm.

[0068] The workpiece surface roughness distribution data was statistically analyzed to extract characteristic parameters such as the mean, standard deviation, skewness, and kurtosis of the surface roughness. The characteristic parameters were extracted using the regional cumulative statistical method. The workpiece surface was divided into 36 evaluation areas, each with an area of ​​10 mm × 10 mm. The overall machining quality level of the workpiece was quantitatively characterized by the characteristic parameters.

[0069] Combined with the process verification experimental data, the corresponding relationship between the workpiece surface quality characteristic parameters and the tool wear state is established. The verification experiment is carried out under the same cutting amount, and the surface quality characteristic parameters of the workpiece after processing with tools of different wear degrees are recorded. The variation law of surface roughness with tool wear is obtained by least squares fitting, and a tool surface quality correlation parameter model is established.

[0070] According to the associated parameter model, the BP neural network algorithm is used to predict tool wear. The surface quality characteristic parameters of the workpiece are used as the input variables of the neural network, and the tool wear amount is used as the output variable. The network weight is optimized through training samples. The hidden layer of the prediction model uses 15 neurons, and the training error is less than 0.01mm. The prediction results include the tool flank wear width VB and the tool tip arc radius r.

[0071] On the basis of tool wear prediction, cutting temperature is introduced to correct the prediction results. The temperature of the cutting area is measured by thermocouples, and a correction model of temperature and tool wear is established. The temperature correction coefficient increases with the increase of cutting temperature. When the cutting temperature exceeds 600℃, the correction coefficient reaches a maximum value of 1.5. The temperature compensation data reflects the effect of high temperature on the aggravation of tool wear.

[0072] The tool wear prediction parameters and temperature compensation data are weighted and superimposed to obtain the final tool prediction usage data. The weighting coefficient is determined by genetic algorithm optimization, where the weight of the wear prediction parameters is 0.7 and the weight of the temperature compensation data is 0.3. The predicted usage data includes the remaining service life of the tool and the recommended replacement time, providing a basis for formulating a tool replacement plan.

[0073] This embodiment uses a three-dimensional laser scanning device to perform high-precision scanning and measurement on the workpiece surface, combined with data reconstruction technology, to achieve accurate characterization of the workpiece surface micro-morphology, providing a reliable data basis for subsequent analysis. The regional cumulative statistical method is used to extract the surface roughness features, which overcomes the limitations of the traditional single-point measurement method and fully reflects the overall processing quality level of the workpiece. The tool wear prediction model based on the BP neural network establishes a correlation between the workpiece surface quality and the tool state, thereby improving the prediction accuracy. The cutting temperature correction mechanism is introduced to comprehensively consider the impact of temperature on tool life, making the prediction results more accurate and reliable. By optimizing the weighting coefficient through the genetic algorithm, the reasonable integration of wear prediction and temperature compensation is achieved, which provides a scientific basis for tool replacement decisions, effectively avoids the deterioration of workpiece quality due to excessive tool wear, and improves processing efficiency and product qualification rate.

[0074] In one embodiment, the safe motion path and the tool prediction usage data are processed and analyzed, and the initial workpiece processing strategy is optimized by NC processing code to obtain a corresponding optimized workpiece processing plan, including: The processing analysis process of safe motion path and tool prediction usage data realizes the optimization of workpiece processing plan through multiple links. This process uses CAM simulation technology to dynamically simulate and optimize the milling cutter processing method.

[0075] The curvature analysis of the safe motion path obtains the path curvature data through numerical calculation methods. The analysis uses the three-point method to calculate the curvature value of each point on the path, discretizes the path into a series of point sets, and calculates the curvature of the arc formed by three adjacent points. The curvature data reflects the changes in the geometric characteristics of the path and provides basic data support for subsequent analysis.

[0076] The distribution correlation calculation of path curvature data and tool usage prediction data is based on the correlation analysis method. This calculation maps the curvature data to the tool usage prediction data, establishes a mathematical model to describe the corresponding relationship between the two, and generates a load distribution diagram that reflects the tool force distribution. The load distribution diagram intuitively displays the force conditions of various parts of the tool during the machining process.

[0077] The threshold stratification of the tool load distribution map adopts a multi-level threshold segmentation method. This method sets multiple load threshold levels and divides the load distribution map into different levels of machining load areas. The threshold setting is based on the tool material characteristics and machining process requirements to ensure that the stratification results meet the actual machining requirements.

[0078] Feed speed parameter optimization is adaptively adjusted according to the processing load area. The optimization process maps different load areas to corresponding feed speed parameters, with lower feed speeds used in high-load areas and higher feed speeds used in low-load areas, forming a speed optimization trajectory.

[0079] The motion characteristic analysis of the speed optimization trajectory focuses on the motion parameters of each axis. This analysis obtains parameters such as axial acceleration and velocity change rate through dynamic calculation to evaluate the stability and continuity of the motion trajectory. The axial motion parameters reflect the motion state of each axis of the machine tool during the machining process.

[0080] The jitter correction calculation optimizes the speed optimization trajectory based on the axial motion parameters. The calculation uses a filtering algorithm to eliminate high-frequency vibration components in the trajectory, smooth the motion trajectory, reduce mechanical vibration during processing, and obtain a stable motion path.

[0081] Tool attitude compensation processing is achieved through spatial geometric transformation. This processing calculates the inclination angle of the tool during the processing, compensates for the processing error caused by tool deformation, ensures that the processing accuracy meets the requirements, and forms a posture compensation trajectory.

[0082] Machining instruction sequence generation converts the posture compensation trajectory into NC machining code. The conversion process follows specific post-processing rules to generate a G-code instruction sequence that meets the requirements of the NC system and achieves optimized NC code output.

[0083] The post-optimization of the workpiece processing strategy comprehensively improves the initial processing plan. This optimization integrates the optimization results of the above-mentioned links, adjusts the process parameters such as cutting amount and feed speed, and formulates the final optimized workpiece processing plan. The optimization plan comprehensively considers multiple factors such as processing efficiency, surface quality, tool life, etc. to achieve overall optimization of the processing process.

[0084] This embodiment realizes precise control of the machining process based on the machining analysis method of multi-link optimization. By performing curvature analysis on the safe motion path and associating it with the tool prediction usage data, the machining load distribution is made clearer and controllable. The threshold layering method manages the tool load in different zones, and cooperates with the adaptive adjustment of the feed speed to improve the machining efficiency while ensuring the machining quality. Axial motion parameter analysis and jitter correction ensure the stability of the machining process and effectively reduce the influence of mechanical vibration on machining accuracy. Through tool posture compensation processing, the machining error caused by tool deformation is overcome and the machining accuracy of the workpiece is improved. The post-optimization link comprehensively considers multiple process parameters, which not only optimizes the machining efficiency but also extends the tool life and reduces the production cost. The overall solution significantly improves the controllability of the machining process and the stability of the machining quality through a systematic optimization process.

[0085] In one embodiment, a machining instruction sequence is generated according to the posture compensation trajectory to obtain a corresponding optimized NC code, including: The multiple trajectory points of the posture compensation trajectory are grouped and processed, and the distance between adjacent trajectory points is used as the segmentation basis. When the distance between adjacent trajectory points is greater than the preset threshold, segmentation is performed at this position. The segmentation data includes the spatial coordinate information of the trajectory points and the tool posture angle information, and this information is stored by establishing a trajectory segmentation database.

[0086] Based on the segmented trajectory data, the trapezoidal speed planning algorithm is used to calculate the feed speed curve. The algorithm divides the machining process into acceleration, uniform speed and deceleration. In the acceleration section, the feed speed increases from zero to the set value according to the preset acceleration; in the uniform speed section, the set feed speed is kept constant; in the deceleration section, the feed speed is reduced to zero according to the preset deceleration. The displacement of each section is obtained by integral operation to ensure the continuity of the speed curve.

[0087] According to the calculated feed speed curve, the linear interpolation algorithm is used to interpolate the machining trajectory. The interpolation algorithm is based on the principle of equal time intervals. According to the set interpolation cycle, the motion increment of each axis is calculated to generate an interpolation point sequence. The interpolation point sequence contains the position coordinates of each interpolation point and the corresponding time information.

[0088] The tool radius compensation algorithm is applied to the interpolation point sequence to calculate the tool center point position. The algorithm uses the tool radius as the offset, offsets the trajectory along the normal vector direction of the tool axis, and obtains the compensated tool position coordinates. The tool wear factor is also considered during the compensation calculation process, and the compensation amount is corrected by the tool wear compensation coefficient.

[0089] Based on the machine tool kinematic model, the inverse kinematics of the tool position coordinates is solved. By establishing the transformation relationship between the workpiece coordinate system and the machine tool coordinate system, the displacement of each motion axis is calculated. This process involves complex spatial geometric operations, and the calculation results meet the machine tool kinematic constraints.

[0090] The calculated displacement of each axis is converted into a code format that can be recognized by the machine tool. The conversion process follows specific coding rules, including quantization of displacement, coding conversion of feed speed, and code generation of auxiliary functions. The generated machine tool code sequence conforms to the grammatical specifications of the machine tool control system.

[0091] Optimize and integrate the machining parameters in the machine tool code sequence. The integration process adjusts parameters such as cutting amount and feed speed based on the preset process rules. The optimized NC code has higher machining efficiency and better machining quality, while ensuring the safety and stability of the machining process.

[0092] The optimized NC code integrates the processing results of multiple links such as posture compensation, speed planning, and trajectory interpolation, and realizes the conversion from posture compensation trajectory to actual processing instructions. Through strict mathematical model and algorithm support, the unity of processing accuracy and efficiency is guaranteed.

[0093] The inverse solution formulas include: ; ; ; ; ; A and B are the rotation axis angles, X, Y, and Z are the axial positions of the machine tool, i, j, and k represent the tool direction vectors, L is the tool length, x is the target position of the tool tip in the X-axis direction of the workpiece coordinate system, y is the target position of the tool tip in the Y-axis direction of the workpiece coordinate system, z is the target position of the tool tip in the Z-axis direction of the workpiece coordinate system, and atan2 is the inverse tangent function.

[0094] This embodiment ensures the continuity and smoothness of the trajectory during complex surface processing by adopting a trajectory segmentation method based on a distance threshold. The feed speed calculation is performed using a trapezoidal speed planning algorithm, which makes the acceleration and deceleration process smoother and effectively reduces the impact vibration of the machine tool. The combined application of the linear interpolation algorithm and the tool radius compensation algorithm improves the accuracy of the processing trajectory, while taking into account the tool wear factor and extending the tool life. The inverse solution operation based on the machine tool kinematic model ensures the accuracy of the spatial trajectory conversion and avoids abnormal situations such as machine tool overtravel. The NC code that has been integrated through parameter optimization improves the processing efficiency while ensuring the processing quality, and realizes the unity of efficient and high-quality processing.

[0095] Reference Figure 2 As shown, the present invention also provides a milling cutter processing system based on CAM simulation technology, which is applied to any of the above-mentioned milling cutter processing methods based on CAM simulation technology, including: The acquisition module is used to obtain the three-dimensional data of the workpiece mold and the milling cutter parameters, and to carry out process planning to obtain the initial workpiece processing strategy; An analysis module, which is used to perform tool trajectory planning and contact point calculation for the initial workpiece machining strategy to obtain the corresponding tool motion trajectory; The association module is used to perform CAM simulation analysis on the initial workpiece processing strategy to obtain corresponding real-time simulation data, and perform collision interference detection on the tool motion trajectory based on the real-time simulation data to obtain the corresponding safe motion path; A processing module, which is used to analyze the cutting force and cutting temperature of the safe motion path to obtain the corresponding process verification results; A control module, which is used to perform workpiece surface analysis on real-time simulation data to obtain corresponding workpiece surface quality information, and to perform tool prediction on the workpiece surface quality information based on process verification results to obtain corresponding tool prediction usage data; The execution module is used to perform processing analysis on the safe motion path and the tool prediction usage data, and optimize the NC processing code of the initial workpiece processing strategy to obtain the corresponding optimized workpiece processing plan.

[0096] The present invention provides a milling cutter processing system based on CAM simulation technology. By acquiring workpiece mold three-dimensional data and milling cutter parameters for process planning, combined with tool trajectory planning and contact point calculation, various parameters in the processing process can be more accurately evaluated, thereby improving the reliability of the processing plan and providing a more scientific basis for actual processing. Real-time simulation data is obtained through CAM simulation analysis, and collision interference detection is performed, so that accurate control and optimization of the tool motion trajectory are achieved, collision problems in the processing process are effectively avoided, and processing safety is guaranteed. Process verification based on cutting force and cutting temperature analysis can ensure stable operation under different processing conditions, reduce processing quality fluctuations, and improve processing accuracy. By analyzing the workpiece surface quality information and combining the tool prediction use data, a more reasonable tool use strategy is formulated, and the optimized operation of the entire processing process is achieved through NC processing code optimization, thereby effectively improving processing efficiency and extending the tool life. By comprehensively considering the coupling relationship between multiple process parameters, the processing strategy can be flexibly adjusted according to the characteristics and processing requirements of different workpieces, and it can better adapt to diversified processing needs.

[0097] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0098] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A milling cutter processing method based on CAM simulation technology, characterized in that: include: Obtain the workpiece mold 3D data and milling cutter parameters, and perform process planning to obtain the initial workpiece processing strategy; Performing tool trajectory planning and contact point calculation on the initial workpiece machining strategy to obtain a corresponding tool motion trajectory; Performing CAM simulation analysis on the initial workpiece machining strategy to obtain corresponding real-time simulation data, and performing collision interference detection on the tool motion trajectory based on the real-time simulation data to obtain a corresponding safe motion path; Perform cutting force and cutting temperature analysis on the safe motion path to obtain corresponding process verification results; Performing workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, and performing tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool prediction usage data; The safe motion path and the tool prediction usage data are processed and analyzed, and the initial workpiece processing strategy is optimized by NC processing code to obtain a corresponding optimized workpiece processing plan.

2. The milling cutter processing method based on CAM simulation technology according to claim 1 is characterized in that: The step of obtaining the three-dimensional data of the workpiece mold and the parameters of the milling cutter, and performing process planning to obtain the initial workpiece processing strategy includes: Performing surface analysis on the three-dimensional data of the workpiece mold to obtain corresponding workpiece surface data; Performing model analysis on the workpiece mold three-dimensional data based on the workpiece surface data to obtain corresponding mold three-dimensional data; Performing preliminary construction based on the three-dimensional mold data and the workpiece surface data to obtain a corresponding initial workpiece mold model; Performing a processing difficulty analysis on the initial workpiece mold model to obtain regional processing complexity data; Matching and calculating the regional processing complexity data according to the milling cutter parameters to obtain a processing specification parameter set; Performing a partition processing plan on the initial workpiece mold model according to the processing specification parameter set to obtain a regional processing order; Calculating the tool entry and exit paths for the processing sequence of the area to obtain the tool's spatial conversion trajectory; Calculating the machining feed rate and the spindle speed according to the spatial conversion trajectory to obtain corresponding process parameters; The process parameters and the processing sequence are combined and optimized to obtain an initial workpiece processing strategy.

3. The milling cutter processing method based on CAM simulation technology according to claim 1 is characterized in that: The performing tool trajectory planning and contact point calculation on the initial workpiece machining strategy to obtain a corresponding tool motion trajectory includes: Performing machining feature analysis on the initial workpiece machining strategy to obtain contour data of the area to be machined; Performing parameter curve discretization processing on the contour data to obtain a surface sampling point set; Performing spline curve fitting on the surface sampling point set to obtain a tool path curve; Calculate the curvature radius of the sampling point according to the tool path curve to obtain corresponding turning angle parameter data; Performing speed calculation on the rotation angle parameter data to obtain the feed speed of each track segment; Sampling the tool path curve at time intervals according to the feed speed to obtain a tool position point sequence; Performing tool axis vector calculation on the tool position point sequence to obtain spatial posture data; Performing tool contact area interference check based on the spatial posture data to obtain an effective cutting boundary; Performing local trajectory optimization on the effective cutting boundary to obtain a contact point sequence; The contact point sequence and the feed speed are combined and mapped to obtain the tool motion trajectory.

4. The milling cutter processing method based on CAM simulation technology according to claim 1, characterized in that: The CAM simulation analysis is performed on the initial workpiece processing strategy to obtain corresponding real-time simulation data, and collision interference detection is performed on the tool motion trajectory according to the real-time simulation data to obtain a corresponding safe motion path, including: Performing dynamic simulation processing on the initial workpiece processing strategy to obtain the real-time simulation data; Performing path analysis on the tool motion trajectory according to the real-time simulation data to obtain corresponding tool position sequence data; Performing envelope surface calculation on the tool position sequence data to obtain corresponding tool swept volume data; Performing Boolean operation on the real-time simulation data according to the tool sweep volume data to obtain corresponding material removal volume data; Performing spatial collision detection on the material removal volume data to obtain corresponding collision detection data; Performing path optimization calculation on the tool position sequence data according to the collision detection data to obtain corresponding obstacle avoidance path data; Performing smoothing processing on the obstacle avoidance path data to obtain a corresponding smooth transition path; The speed of the tool motion trajectory is planned according to the smooth transition path to obtain a corresponding safe motion path.

5. The milling cutter processing method based on CAM simulation technology according to claim 1 is characterized in that: The cutting force and cutting temperature analysis of the safe motion path to obtain corresponding process verification results includes: Sampling the tool contact point trajectory on the safe motion path to obtain corresponding sampling data point coordinates; Calculate the instantaneous cutting angle of the tool according to the coordinates of the sampling data points to obtain corresponding cutting-in and cutting-out angle data; Performing geometric dimension cutting calculation on the cutting-in and cutting-out angle data to obtain corresponding cutting cross-sectional area data; Calculate the unit cutting amount according to the cutting cross-sectional area data to obtain the corresponding cutting force value; Performing moment decomposition operation on the cutting force value to obtain corresponding three-dimensional cutting force components; Calculate cutting power according to the three-dimensional cutting force components to obtain corresponding cutting heat data; Performing heat distribution calculation on the cutting heat data to obtain a corresponding cutting temperature value; A machining simulation verification analysis is performed based on the three-dimensional cutting force components and the cutting temperature values ​​to obtain the process verification result.

6. The milling cutter processing method based on CAM simulation technology according to claim 1 is characterized in that: The performing workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, and performing tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool prediction usage data, includes: Performing three-dimensional surface analysis on the real-time simulation data to obtain three-dimensional surface morphology data; Performing workpiece surface roughness analysis based on the three-dimensional surface topography data to obtain workpiece surface roughness distribution data; Performing characteristic statistics on the workpiece surface roughness distribution data to obtain workpiece surface quality characteristic parameters; Verifying and associating the workpiece surface quality characteristic parameters according to the process verification results to obtain tool surface quality associated parameters; Performing wear prediction on the tool surface quality associated parameters to obtain tool wear prediction parameters; Performing temperature correction on the tool wear prediction parameters according to the process verification results to obtain temperature compensation data; The tool wear prediction parameters and the temperature compensation data are comprehensively calculated and processed to obtain the tool prediction usage data.

7. The milling cutter processing method based on CAM simulation technology according to claim 1, characterized in that: The processing analysis of the safe motion path and the tool prediction usage data is performed, and the NC processing code of the initial workpiece processing strategy is optimized to obtain a corresponding optimized workpiece processing plan, including: Performing curvature analysis on the safe motion path to obtain corresponding path curvature data; Performing distribution correlation calculation on the tool prediction usage data according to the path curvature data to obtain a corresponding tool load distribution diagram; Performing threshold stratification on the tool load distribution diagram to obtain corresponding machining load areas; Optimizing the feed speed parameters of the safe motion path according to the machining load area to obtain a corresponding speed optimization trajectory; Performing motion characteristic analysis on the speed optimization trajectory to obtain corresponding axial motion parameters; Performing jitter correction calculation on the speed optimization trajectory according to the axial motion parameters to obtain a corresponding stable motion path; Performing tool posture compensation processing on the stable motion path to obtain a corresponding posture compensation trajectory; Generating a machining instruction sequence according to the posture compensation trajectory to obtain a corresponding optimized NC code; The initial workpiece machining strategy is post-optimized according to the optimized NC code to obtain the optimized workpiece machining plan.

8. The milling cutter processing method based on CAM simulation technology according to claim 7 is characterized in that: The step of generating a machining instruction sequence according to the posture compensation trajectory to obtain a corresponding optimized NC code includes: Processing the posture compensation trajectory in segments to obtain corresponding trajectory segment data; Performing speed planning calculation on the trajectory segment data to obtain a corresponding feed speed curve; Performing machining trajectory interpolation calculation according to the feed speed curve to obtain a corresponding interpolation point sequence; Performing tool compensation calculation on the interpolation point sequence to obtain corresponding tool position coordinates; Performing an inverse calculation on the axial motion of the machine tool according to the tool position coordinates to obtain the corresponding displacement of each axis; Performing encoding conversion processing on the motion displacement of each axis to obtain a corresponding machine tool code sequence; Integrate the machining parameters according to the machine tool code sequence to obtain the corresponding optimized NC code; The inverse solution formulas include: ； ； ； ； ； A and B are the rotation axis angles, X, Y, and Z are the axial positions of the machine tool, i, j, and k represent the tool direction vectors, L is the tool length, x is the target position of the tool tip in the X-axis direction of the workpiece coordinate system, y is the target position of the tool tip in the Y-axis direction of the workpiece coordinate system, z is the target position of the tool tip in the Z-axis direction of the workpiece coordinate system, and atan2 is the inverse tangent function.

9. A milling cutter processing system based on CAM simulation technology, characterized in that: The milling cutter processing method based on CAM simulation technology applied to any one of claims 1 to 8 above comprises: An acquisition module, which is used to obtain the three-dimensional data of the workpiece mold and the parameters of the milling cutter, and to perform process planning to obtain an initial workpiece processing strategy; An analysis module, the analysis module is used to perform tool trajectory planning and contact point calculation on the initial workpiece machining strategy to obtain a corresponding tool motion trajectory; An association module, the association module is used to perform CAM simulation analysis on the initial workpiece processing strategy to obtain corresponding real-time simulation data, and perform collision interference detection on the tool motion trajectory according to the real-time simulation data to obtain a corresponding safe motion path; A processing module, the processing module is used to analyze the cutting force and cutting temperature of the safe motion path to obtain a corresponding process verification result; A control module, the control module is used to perform workpiece surface analysis on the real-time simulation data to obtain corresponding workpiece surface quality information, perform tool prediction on the workpiece surface quality information based on the process verification result, and obtain corresponding tool prediction usage data; An execution module is used to perform processing analysis on the safe motion path and the tool prediction usage data, and to optimize the NC processing code of the initial workpiece processing strategy to obtain a corresponding optimized workpiece processing plan.

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