Milling cutter machining method and system based on CAM simulation technology

Abstract

The present invention relates to a milling cutter machining method and system based on CAM simulation technology. The method includes obtaining three-dimensional data of a workpiece mold and milling cutter parameters, and performing process planning to obtain an initial workpiece machining strategy; performing tool path 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 and interference detection on the tool motion trajectory based on the real-time simulation data to obtain a corresponding safe motion path; performing cutting force and cutting temperature analysis on the safe motion path to obtain corresponding process verification results; performing machining analysis on the safe motion path and the predicted tool usage data, and optimizing the NC machining code for the initial workpiece machining strategy to obtain a corresponding optimized workpiece machining plan. The present invention can flexibly adjust the machining strategy according to the characteristics and machining requirements of different workpieces.

Description

Technical Field

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

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

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

[0004] To achieve the above object, the present invention provides a milling cutter machining method based on CAM simulation technology, including:

[0005] Obtain the three-dimensional data of the workpiece mold and the milling cutter parameters, and perform process planning to obtain the initial workpiece machining strategy;

[0006] Perform tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool movement trajectory;

[0007] Perform CAM simulation analysis on the initial workpiece machining strategy to obtain the corresponding real-time simulation data, and perform collision interference detection on the tool movement trajectory based on the real-time simulation data to obtain the corresponding safe movement path;

[0008] Perform cutting force and cutting temperature analysis on the safe movement path to obtain the corresponding process verification result;

[0009] Perform workpiece surface analysis on the real-time simulation data to obtain the corresponding workpiece surface quality information, and perform tool prediction on the workpiece surface quality information based on the process verification result to obtain the corresponding tool prediction usage data;

[0010] Perform machining analysis on the safe movement path and the predicted tool usage data, and optimize the NC machining code for the initial workpiece machining strategy to obtain the corresponding optimized workpiece machining plan.

[0011] Furthermore, obtaining the three-dimensional data of the workpiece mold and the milling cutter parameters and performing process planning to obtain the initial workpiece machining strategy includes:

[0012] Perform surface analysis on the three-dimensional data of the workpiece mold to obtain the corresponding workpiece surface data;

[0013] Perform model analysis on the three-dimensional data of the workpiece mold based on the workpiece surface data to obtain the corresponding three-dimensional mold data;

[0014] Perform preliminary construction based on the three-dimensional mold data and the workpiece surface data to obtain the corresponding initial workpiece mold model;

[0015] Perform machining difficulty analysis on the initial workpiece mold model to obtain regional machining complexity data;

[0016] Perform matching calculations on the regional machining complexity data according to the milling cutter parameters to obtain a set of machining specification parameters;

[0017] Perform zoning machining planning on the initial workpiece mold model according to the set of machining specification parameters to obtain the regional machining sequence;

[0018] Perform calculation of the tool entry and exit paths for the regional machining sequence to obtain the spatial conversion trajectory of the tool;

[0019] Perform calculation of the machining feed rate and spindle speed according to the spatial conversion trajectory to obtain the corresponding process parameters;

[0020] Combine and optimize the process parameters with the machining sequence to obtain the initial workpiece machining strategy.

[0021] Furthermore, performing tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool movement trajectory includes:

[0022] Perform machining feature analysis on the initial workpiece machining strategy to obtain the contour data of the area to be machined;

[0023] Perform parametric curve discretization processing on the contour data to obtain a set of surface sampling points;

[0024] Perform spline curve fitting on the set of surface sampling points to obtain the tool path curve;

[0025] Perform calculation of the curvature radius of the sampling points according to the tool path curve to obtain the corresponding corner parameter data;

[0026] Perform speed calculation on the corner parameter data to obtain the feed speed of each segment of the trajectory;

[0027] Perform time interval sampling on the tool path curve according to the feed speed to obtain a sequence of tool location points;

[0028] Perform tool axis vector calculation on the sequence of tool location points to obtain spatial attitude data;

[0029] Perform interference inspection on the tool contact area according to the spatial attitude data to obtain an effective cutting boundary;

[0030] Perform local trajectory optimization on the effective cutting boundary to obtain a sequence of contact points;

[0031] Combine and map the sequence of contact points with the feed speed to obtain the tool motion trajectory.

[0032] Furthermore, 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, includes:

[0033] Perform dynamic simulation processing on the initial workpiece machining strategy to obtain the real-time simulation data;

[0034] Perform path analysis on the tool motion trajectory according to the real-time simulation data to obtain corresponding tool position sequence data;

[0035] Perform envelope surface calculation on the tool position sequence data to obtain corresponding tool swept volume data;

[0036] Perform Boolean operation on the real-time simulation data based on the tool swept volume data to obtain corresponding material removal volume data;

[0037] Perform spatial collision detection on the material removal volume data to obtain corresponding collision detection data;

[0038] Perform path optimization calculation on the tool position sequence data according to the collision detection data to obtain corresponding obstacle avoidance path data;

[0039] Perform fairing on the obstacle avoidance path data to obtain a corresponding smooth transition path;

[0040] Perform speed planning on the tool motion trajectory according to the smooth transition path to obtain a corresponding safe motion path.

[0041] Furthermore, performing cutting force and cutting temperature analysis on the safe motion path to obtain corresponding process verification results, includes:

[0042] Sample the tool contact point trajectory of the safe motion path to obtain the corresponding sampled data point coordinates;

[0043] Calculate the instantaneous cutting angle of the tool based on the sampled data point coordinates to obtain the corresponding cutting-in and cutting-out angle data;

[0044] Perform geometric dimension cutting calculation on the cutting-in and cutting-out angle data to obtain the corresponding cutting cross-sectional area data;

[0045] Calculate the unit cutting parameter based on the cutting cross-sectional area data to obtain the corresponding cutting force value;

[0046] Perform moment decomposition operation on the cutting force value to obtain the corresponding three-direction cutting force components;

[0047] Calculate the cutting power based on the three-direction cutting force components to obtain the corresponding cutting heat data;

[0048] Perform heat distribution calculation on the cutting heat data to obtain the corresponding cutting temperature value;

[0049] Perform machining simulation verification analysis based on the three-direction cutting force components and the cutting temperature value to obtain the process verification result.

[0050] Further, analyze the workpiece surface of the real-time simulation data to obtain the corresponding workpiece surface quality information, and perform tool prediction on the workpiece surface quality information based on the process verification result to obtain the corresponding tool prediction usage data, including:

[0051] Perform surface three-dimensional analysis on the real-time simulation data to obtain surface three-dimensional topography data;

[0052] Perform workpiece surface roughness analysis based on the surface three-dimensional topography data to obtain workpiece surface roughness distribution data;

[0053] Perform feature statistics on the workpiece surface roughness distribution data to obtain workpiece surface quality characteristic parameters;

[0054] Verify and correlate the workpiece surface quality characteristic parameters based on the process verification result to obtain tool surface quality correlation parameters;

[0055] Perform wear prediction on the tool surface quality correlation parameters to obtain tool wear prediction parameters;

[0056] Perform temperature correction on the tool wear prediction parameters based on the process verification result to obtain temperature compensation data;

[0057] Perform comprehensive calculation and processing on the tool wear prediction parameters and the temperature compensation data to obtain the predicted tool usage data.

[0058] Further, performing machining analysis on the safe motion path and the predicted tool usage data, and optimizing the NC machining code for the initial workpiece machining strategy to obtain the corresponding optimized workpiece machining plan, includes:

[0059] Perform curvature analysis on the safe motion path to obtain the corresponding path curvature data;

[0060] Perform distribution correlation calculation on the predicted tool usage data based on the path curvature data to obtain the corresponding tool load distribution map;

[0061] Perform threshold stratification on the tool load distribution map to obtain the corresponding machining load regions;

[0062] Optimize the feed speed parameters of the safe motion path based on the machining load regions to obtain the corresponding speed optimization trajectory;

[0063] Perform motion characteristic analysis on the speed optimization trajectory to obtain the corresponding axial motion parameters;

[0064] Perform jitter correction calculation on the speed optimization trajectory based on the axial motion parameters to obtain the corresponding stable motion path;

[0065] Perform tool attitude compensation processing on the stable motion path to obtain the corresponding attitude compensation trajectory;

[0066] Generate a machining instruction sequence based on the attitude compensation trajectory to obtain the corresponding optimized NC code;

[0067] Perform post-optimization on the initial workpiece machining strategy based on the optimized NC code to obtain the optimized workpiece machining plan.

[0068] Further, the generating a machining instruction sequence based on the attitude compensation trajectory to obtain the corresponding optimized NC code includes:

[0069] Perform segmented processing on the attitude compensation trajectory to obtain the corresponding trajectory segmentation data;

[0070] Perform speed planning calculation on the trajectory segmentation data to obtain the corresponding feed speed curve;

[0071] Perform machining trajectory interpolation operation based on the feed speed curve to obtain the corresponding interpolation point sequence;

[0072] Perform tool compensation calculation on the interpolation point sequence to obtain the corresponding tool position coordinates;

[0073] Inverse kinematic calculation is performed on the axial movement of the machine tool according to the tool position coordinates to obtain the movement displacement of each axis accordingly;

[0074] Encoding conversion processing is performed on the movement displacement of each axis to obtain the corresponding machine tool code sequence;

[0075] Integrating processing is performed on the machining parameters according to the machine tool code sequence to obtain the corresponding optimized NC code;

[0076] Among them, the formula for inverse kinematic calculation includes:

[0077] ; ; ;

[0078] ; ;

[0079] A and B are the angles of the rotating axes, 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 point in the X-axis direction of the workpiece coordinate system, y is the target position of the tool tip point in the Y-axis direction of the workpiece coordinate system, z is the target position of the tool tip point in the Z-axis direction of the workpiece coordinate system, and atan2 is the arctangent function.

[0080] The present invention also provides a milling cutter machining system based on CAM simulation technology, which is applied to the milling cutter machining method based on CAM simulation technology described in any one of the above, and includes:

[0081] An acquisition module, which is used to acquire the three-dimensional data of the workpiece mold and the milling cutter parameters, and perform process planning to obtain an initial workpiece machining strategy;

[0082] An analysis module, which is used to perform tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool movement trajectory;

[0083] An association module, which is used to perform CAM simulation analysis on the initial workpiece machining strategy to obtain the corresponding real-time simulation data, and perform collision interference detection on the tool movement trajectory according to the real-time simulation data to obtain the corresponding safe movement path;

[0084] A processing module, which is used to perform cutting force and cutting temperature analysis on the safe movement path to obtain the corresponding process verification result;

[0085] A control module, which is used to analyze the workpiece surface of the real-time simulation data to obtain corresponding workpiece surface quality information, and predict the tool based on the process verification result for the workpiece surface quality information to obtain corresponding predicted tool usage data;

[0086] An execution module, which is used to perform machining analysis on the safe motion path and the predicted tool usage data, and optimize the NC machining code for the initial workpiece machining strategy to obtain a corresponding optimized workpiece machining plan.

[0087] A milling cutter machining method and system based on CAM simulation technology provided by the present invention have the following beneficial effects:

[0088] By obtaining the three-dimensional data of the workpiece mold and the milling cutter parameters for process planning, combining tool path planning and contact point calculation, it is possible to more accurately evaluate various parameters during the machining process, thereby improving the reliability of the machining plan and providing a more scientific basis for actual machining. By obtaining real-time simulation data through CAM simulation analysis and performing collision interference detection, precise control and optimization of the tool motion trajectory are achieved, effectively avoiding collision problems during the machining process and ensuring machining safety. Based on the analysis of cutting force and cutting temperature for process verification, it can ensure stable operation under different machining conditions, reduce machining quality fluctuations, and improve machining accuracy. By analyzing the workpiece surface quality information and combining the predicted tool usage data, a more reasonable tool usage strategy is formulated, and the optimized operation of the entire machining process is realized through NC machining code optimization, thereby effectively improving the machining efficiency and extending the tool service life. By comprehensively considering the coupling relationship between multiple process parameters, the machining strategy can be flexibly adjusted according to the characteristics and machining requirements of different workpieces, and it can better adapt to diverse machining needs. Brief Description of the Drawings

[0089] Figure 1 is a flowchart of a milling cutter machining method based on CAM simulation technology provided by the present invention;

[0090] Figure 2 is a structural diagram of a milling cutter machining system based on CAM simulation technology provided by the present invention.

[0091] The realization, functional characteristics, and advantages of the purpose of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0092] In order to make the purpose, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0093] Next, in combination with the accompanying drawings and specific embodiments, the present invention will be further described.

[0094] Referring to Figure 1 As shown, the present invention provides a milling cutter machining method based on CAM simulation technology, including:

[0095] Step S1: Obtain the three-dimensional data of the workpiece mold and the milling cutter parameters, and perform process planning to obtain the initial workpiece machining strategy;

[0096] Step S2: Perform tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory;

[0097] Step S3: Perform CAM simulation analysis on the initial workpiece machining strategy to obtain the 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;

[0098] Step S4: Analyze the cutting force and cutting temperature of the safe motion path to obtain the corresponding process verification result;

[0099] Step S5: Analyze the workpiece surface of the real-time simulation data to obtain the corresponding workpiece surface quality information, and perform tool prediction on the workpiece surface quality information based on the process verification result to obtain the corresponding tool prediction usage data;

[0100] Step S6: Perform machining analysis on the safe motion path and the tool prediction usage data, and optimize the NC machining code of the initial workpiece machining strategy to obtain the corresponding optimized workpiece machining plan.

[0101] Based on the above steps, the detailed process is as follows:

[0102] Step S1: Obtain the 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 geometric dimensions, shape features, and material properties of the workpiece. At the same time, collect the key parameters of the milling cutter, such as tool type, tool material, tool geometric parameters (tool diameter, number of cutting edges, rake angle, clearance angle, etc.), and cutting parameter (spindle speed, feed rate, cutting depth, etc.). Based on the obtained workpiece and tool information, perform detailed process planning, including determining the machining reference, process arrangement, fixture design, rough machining and finish machining plans, etc. In the process of process planning, factors such as workpiece material characteristics, machining accuracy requirements, and surface quality requirements are involved, and cutting parameters and machining methods are reasonably selected. By analyzing the structural characteristics of the workpiece, different machining areas are divided, and corresponding machining 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 machining strategy plan containing complete machining information is formed.

[0103] Step S2: Based on the initial machining strategy, conduct detailed tool path planning. According to the geometric features and machining requirements of the workpiece, determine appropriate cutting methods, such as down milling or up milling, contour machining or equal-parameter machining, etc. Then use CAM software to generate tool paths, including determining parameters such as feed direction, stepover, and entry / exit methods. During the path planning process, focus on calculating the contact point positions 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. Through numerical calculation methods, determine the tool posture at each machining point position, including the position coordinates and direction vectors of the tool. At the same time, it is necessary to consider the connection transition between adjacent tool paths to ensure the continuity and stability of the cutting process. For the machining of complex surfaces, local curvature analysis is also required, and the cutting parameters and tool posture are adjusted according to the surface characteristics to ensure machining accuracy and efficiency.

[0104] Step S3: After completing the tool path planning, use CAM simulation software to conduct virtual simulation of the entire machining process. During the simulation process, calculate and display various parameters during the tool movement in real time, including tool position, posture, cutting state, etc. Through simulation, the relative position relationship between the tool, the workpiece, and the fixture can be visually observed, and potential collision and interference problems can be detected in a timely manner. Conduct collision detection and analysis for each tool location point, including interference checks in multiple aspects such as the tool and the workpiece, the tool and the fixture, and the tool shank and the workpiece. When potential collision risks are found, the original path needs to be optimized and adjusted. Possible adjustment methods include changing the tool feed direction, adjusting the tool tilt angle, adding transitional movements, etc. The optimized path needs to be simulated and verified again to ensure that all motion paths are safe and reliable. The finally generated safe motion path should not only ensure machining quality but also ensure the safety and reliability of the entire machining process.

[0105] Step S4: Based on the safe motion path, carry out the analysis of cutting force and cutting temperature. First, establish a mechanical model of the cutting process, considering factors such as workpiece material properties, tool geometric parameters, and cutting parameters, and calculate the main cutting force, feed force, and back force during the cutting process. Through finite element analysis software, simulate the stress distribution and deformation conditions during the cutting process to evaluate the stability of the cutting process. At the same time, establish a thermo-mechanical coupling model of cutting 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 influence of parameters such as cutting speed, feed rate, and depth of cut on the cutting temperature, and evaluate the influence of high temperature on tool wear and workpiece surface quality. Based on the analysis results, complete process verification data can be obtained, including the variation curve of cutting force with time, temperature field distribution map, stress-strain data, etc. These data will provide an important basis for subsequent process optimization.

[0106] Step S5: Analyze the surface of the machined workpiece, including the evaluation of quality indicators such as surface roughness, shape accuracy, and dimensional accuracy. By processing the simulation data, the microscopic topography characteristics of the workpiece surface can be obtained, such as cutting texture, surface defects, etc. Combining the process verification results, establish a tool wear prediction model 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, workpiece material properties, etc. Through data analysis and pattern recognition methods, establish the correlation between tool wear and machining quality, and predict the tool state at different machining stages. These prediction data can be used to estimate the remaining service life of the tool, determine when tool replacement or cutting parameter adjustment is needed, so as to ensure the stability of machining quality.

[0107] Step S6: Conduct a comprehensive evaluation of all the data and analysis results obtained previously. Combine the safe motion path with the predicted tool 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 processes, etc. The focus is on optimizing the NC machining code, which includes optimizing the feed rate distribution, adjusting cutting parameters, improving the tool approach and retract strategies, etc. The optimization process needs to consider multiple objectives, such as improving machining efficiency, extending tool life, ensuring machining accuracy, etc. Through a multi-objective optimization algorithm, generate the final optimized machining plan, which should be able to balance the requirements of multiple aspects such as machining efficiency, machining quality, and machining cost. Finally, output the optimized NC code and the complete process document to provide detailed operation guidance for actual machining.

[0108] A milling cutter machining method based on CAM simulation technology provided by the present invention can more accurately evaluate various parameters in the machining process by obtaining the three-dimensional data of the workpiece mold and the milling cutter parameters, and combining tool path planning and contact point calculation, thereby improving the reliability of the machining plan and providing a more scientific basis for actual machining. By obtaining real-time simulation data through CAM simulation analysis and conducting collision interference detection, precise control and optimization of the tool motion trajectory are achieved, effectively avoiding collision problems during the machining process and ensuring machining safety. Based on the analysis of cutting force and cutting temperature for process verification, it can ensure stable operation under different machining conditions, reduce machining quality fluctuations, and improve machining accuracy. By analyzing the workpiece surface quality information and combining the predicted tool usage data, a more reasonable tool usage strategy is formulated, and the optimization operation of the entire machining process is realized through the optimization of the NC machining code, thereby effectively improving machining efficiency and extending the tool service life. By comprehensively considering the coupling relationship between multiple process parameters, the machining strategy can be flexibly adjusted according to the characteristics and machining requirements of different workpieces, and it can better adapt to diverse machining needs.

[0109] In one embodiment, three-dimensional data of a workpiece mold and milling cutter parameters are obtained, and process planning is carried out to obtain an initial workpiece machining strategy, including:

[0110] The workpiece mold is scanned and modeled in all directions by a three-dimensional scanner to generate point cloud data, and the point cloud data is converted into a three-dimensional mesh model. The milling cutter parameters include basic parameters such as tool diameter, tool length, number of cutting edges, helix angle, etc., which are determined by the specifications provided by the tool manufacturer.

[0111] When analyzing the workpiece surface, a curvature analysis method is used to extract the surface features of the three-dimensional mesh model, calculate the Gaussian curvature and mean curvature of each mesh point, and identify different types of surface features such as flat surfaces, 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 types and geometric features of each region.

[0112] In the model analysis stage, the functional regions of the mold are divided according to the workpiece surface feature map, and geometric dimension measurement and tolerance analysis are carried out on different regions. By fitting the surface equation using the least squares method, a mathematical model of each region is established to generate a complete three-dimensional data model of the mold.

[0113] During the construction of the initial workpiece mold model, the three-dimensional data of the mold and the workpiece surface data are registered to establish a coordinate transformation relationship. The Boolean operation method is used to calculate the intersection volume of the workpiece and the mold to determine the material removal area and generate a blank model with machining allowance.

[0114] The acquisition of regional machining complexity data is based on the following rules: the greater the surface curvature, the higher the machining complexity; the machining complexity of deep cavity regions is higher than that of open regions; the smaller the detail feature size, the higher the machining complexity. By establishing a machining complexity evaluation index system, a quantitative score is given to each region.

[0115] The matching calculation of the machining specification parameter set is based on the geometric parameters and cutting ability of the milling cutter, and a tool-workpiece matching rule library is established. 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 cutting edges should be adapted to the feed rate. According to these rules, a suitable machining parameter combination is selected for each machining region.

[0116] The planning of the regional machining order follows the machining principle of from rough to fine and from simple to difficult. By calculating the topological relationship and machining constraints between regions, a machining sequence dependency graph is constructed, and a graph theory algorithm is used to optimize the machining order to ensure the continuity and efficiency of the machining process.

[0117] The calculation of the tool path in the tool space transformation adopts 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 idle cutting, and protecting the machining surface quality are considered to generate an optimized tool motion trajectory.

[0118] The determination of process parameters is 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 constraint conditions such as material removal rate, tool life, and machining accuracy are comprehensively considered to solve the optimal combination of process parameters.

[0119] By combining and optimizing process parameters and machining sequences, a multi-objective optimization model is established. The optimization objectives include maximizing machining efficiency, optimizing machining quality, and maximizing tool life. An intelligent optimization algorithm is used to solve the optimal machining strategy plan to generate a complete machining process file.

[0120] In this embodiment, workpiece data is obtained through full-range scanning and modeling, and surface features are accurately extracted by combining the curvature analysis method, realizing high-precision digital modeling of complex workpieces. A multi-dimensional machining complexity evaluation system is used to divide the workpiece into regions, and a tool-workpiece matching rule library is established for parameter optimization, effectively improving the rationality and feasibility of the machining plan. The inverse kinematics algorithm and cutting force model are introduced in the trajectory planning, and multiple constraint factors such as avoiding interference and reducing idle cutting are comprehensively considered, significantly optimizing the tool motion trajectory and ensuring the machining quality. By establishing a multi-objective optimization model to combine and optimize process parameters and machining sequences, while ensuring machining accuracy, machining efficiency and tool life are maximized, achieving the dual goals of improving machining quality and reducing production costs.

[0121] In one embodiment, tool path planning and contact point calculation are performed on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory, including:

[0122] Based on the machining strategy of the initial workpiece, feature analysis is performed on the area to be machined, and geometric features on the workpiece surface, including planes, curved surfaces, concave and convex features, etc., are extracted through the feature recognition algorithm, so as to obtain the contour data of the area to be machined. The contour data contains the key point coordinate information and topological relationship of the workpiece surface.

[0123] The obtained contour data is processed by the parametric curve discretization method. On the premise of ensuring the curve accuracy, the parametric 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 machining accuracy requirements. The higher the accuracy requirements, the denser the sampling points.

[0124] Use the B-spline curve fitting algorithm to fit the set of sampled points and construct a smooth and continuous tool path curve. During the fitting process, by adjusting the positions of the control points and the distribution of the knot vectors, the error between the fitting curve and the sampled points is made to meet the machining accuracy requirements.

[0125] Calculate the radius of curvature at each sampled point on the tool path curve. Combining the tool geometric parameters and the process parameters, solve the corresponding angular parameter data. The angular parameters reflect the law of the tool's attitude change during the machining process.

[0126] Based on the angular parameter data, comprehensively consider the tool performance parameters, the workpiece material characteristics, and the machining process requirements, and calculate the reasonable feed rate for each segment of the trajectory. The calculation of the feed rate needs to meet the process conditions such as the cutting force constraint and the surface roughness constraint.

[0127] According to the calculated feed rate, sample the tool path curve at equal time intervals to obtain the sequence of tool location points. The selection of the sampling time interval is matched with the interpolation cycle of the numerical control system.

[0128] Calculate the tool axis vector for the sequence of tool location points to determine the spatial attitude of the tool at each position point. The calculation of the tool axis vector is based on the tool geometric model and the workpiece surface normal vector information to ensure that the tool maintains the correct cutting contact relationship with the workpiece surface.

[0129] Combining the tool geometric model and the workpiece feature model, conduct interference checks on the cutting areas of the tool in each attitude to determine the effective cutting boundary. The interference checks include local interference checks and overall interference checks between the tool and the workpiece.

[0130] For the detected interference areas, optimize the local trajectory by adjusting the tool attitude and path to generate a sequence of contact points without interference. The optimization process takes ensuring machining accuracy as the premise and minimizes the amount of tool attitude adjustment.

[0131] Combine and map the optimized sequence of contact points with the feed rate data to generate the complete tool motion trajectory. The motion trajectory data includes tool position, attitude, and speed information, which is used for subsequent numerical control machining and simulation verification.

[0132] In this embodiment, by precisely analyzing the surface features of the workpiece and extracting contour data, accurate identification and positioning of the machining area are achieved. The discrete processing of parametric curves combined with the B-spline curve fitting algorithm ensures the continuity and smoothness of the tool path, effectively improving the machining surface quality. By calculating the curvature radius and optimizing the feed rate in combination with process parameters, both the machining 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 checking mechanism, intelligent adjustment of the tool attitude is realized, effectively avoiding interference problems during the machining process. Optimizing the local trajectory and generating the final motion trajectory significantly improves the machining efficiency while ensuring the machining accuracy, making the entire machining process more reliable and stable. The overall method not only ensures the machining quality but also improves the machining efficiency, providing strong support for practical production applications.

[0133] In one embodiment, a CAM simulation analysis is performed on the initial workpiece machining strategy to obtain corresponding real-time simulation data. Based on the real-time simulation data, a collision interference detection is carried out on the tool motion trajectory to obtain a corresponding safe motion path, including:

[0134] The dynamic simulation processing of the initial workpiece machining strategy is carried out in a discrete sampling point manner for real-time simulation. The dynamic simulation system samples the machining process at a sampling period of 0.001 seconds to obtain real-time simulation data including process parameters such as tool pose, feed rate, and spindle speed. The system forms a continuous motion trajectory curve by cubic spline interpolation of the sampled discrete data points.

[0135] The path analysis of the tool motion trajectory constructs a tool position sequence based on the real-time simulation data. The path analysis module discretizes the interpolated trajectory curve at an equal distance with a step size of 0.1 mm to generate a 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 pose sequence data structure.

[0136] The calculation of the tool swept volume processes the position sequence using the envelope surface method. The system moves the tool geometric model under the control of the pose sequence, and obtains the motion swept volume by calculating the envelope surface of the tool surface family. The solution of the envelope surface uses the characteristic line method. By solving the characteristic equation set, the characteristic curve on the envelope surface is obtained, and then a complete swept volume model is constructed.

[0137] The calculation of the material removal volume uses the Boolean operation method. The system performs a Boolean difference operation on the workpiece blank model and the tool swept volume to obtain the volume model of the material removal area. The Boolean operation uses the boundary representation method. By calculating the interface between the two entities, the workpiece is divided into a retained area and a removed area.

[0138] Spatial collision detection is based on the material removal volume model for interference analysis. The detection module uses the 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 the collision position and collision depth.

[0139] The optimization of the collision avoidance path uses the artificial potential field method. The optimization module constructs a repulsive force field based on the collision detection data and combines it with the attractive potential field on the workpiece surface to locally adjust the original tool position sequence. The optimal collision 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 5 mm.

[0140] Path smoothing processing uses the quintic polynomial fitting method. The smoothing module identifies the mutation points of the collision avoidance path and inserts a transition curve in the mutation area. The transition curve uses the quintic polynomial function to ensure the continuity of position, velocity, and acceleration and achieve a smooth transition of the trajectory.

[0141] The speed planning of the safe motion path adopts the "look-ahead - deceleration" strategy. The planning module calculates the maximum allowable feed speed for each segment according to the curvature distribution of the smooth path. The early deceleration method is used at the corners to ensure that the dynamic characteristics of the machining process meet the machine tool performance constraints. The speed planning result contains complete machining information such as the position, attitude, and feed speed of the path points.

[0142] In this embodiment, through CAM simulation analysis of the initial workpiece machining strategy, dynamic simulation is implemented by discrete sampling points, and continuous motion trajectories are formed by combining cubic spline interpolation, significantly improving the simulation accuracy. Based on the envelope surface method, the tool swept volume is calculated, and Boolean operations are performed using the boundary representation method to accurately obtain the material removal volume, improving the reliability of the machining process. The hierarchical bounding box algorithm is used for spatial collision detection, combined with the artificial potential field method to optimize the collision avoidance path, effectively avoiding interference collisions during the machining process. Path smoothing processing is achieved through quintic polynomial fitting, and the "look-ahead - deceleration" strategy is used for speed planning to ensure the continuity and smoothness of the machining trajectory. This method not only optimizes the tool motion trajectory but also realizes precise control of the machining process, reduces machining errors, improves machining efficiency and quality, and provides effective technical support for the improvement and optimization of the milling cutter machining process.

[0143] In one embodiment, the cutting force and cutting temperature of the safe motion path are analyzed to obtain the corresponding process verification results, including:

[0144] When analyzing the cutting force and cutting temperature of the safe motion path, three-dimensional spatial coordinate data is obtained by sampling the tool contact point trajectory. The sampling method adopts the equal arc length sampling principle, and the sampling point coordinates are extracted at a fixed interval on the tool motion trajectory. The sampling interval is set to 0.1 mm to ensure that the sampling accuracy meets the requirements of subsequent analysis.

[0145] Based on the acquired coordinate data of the sampling points, calculate the instantaneous cutting-in angle and cutting-out angle of the cutting tool during the machining process. During the calculation process, the angle between the axial vector of the cutting tool and the normal vector of the workpiece surface is used as the reference benchmark for the cutting-in angle, and the cutting-in and cutting-out angle values are obtained through vector cross product operations. The range of the cutting-in angle value is between 0° and 180°, and the range of the cutting-out angle value is between 180° and 360°.

[0146] According to the calculated cutting-in and cutting-out angle data, calculate the cross-sectional area in combination with the geometric parameters of the cutting tool. The calculation method is based on the tooth profile equation of the cutting tool. The cutting area is discretized into micro-element areas, and the total cutting area is obtained through numerical integration. The cutting cross-sectional area data directly affects the magnitude of the cutting force and is an important basis for optimizing the process parameters.

[0147] For the cutting cross-sectional area data, use the unit cutting force coefficient method to calculate the cutting force. The cutting force coefficient is obtained through an empirical formula, which takes into account factors such as the workpiece material properties, the type of cutting tool material, and the machining process parameters. The calculation result represents the magnitude of the cutting force in units of Newton (N).

[0148] Decompose the calculated cutting force into three components to obtain the values of the tangential force, radial force, and axial force respectively. The decomposition process is based on the establishment of the cutting tool coordinate system, and the cutting force is projected onto the corresponding directions through the coordinate transformation matrix. The three-component force data is used to evaluate the force state of the cutting tool and the machining stability.

[0149] Use the three-component cutting force data to calculate the cutting power and convert the cutting power into cutting heat. During the calculation process, 90% of the cutting power is converted into heat, and the remaining energy is lost in the forms of vibration, noise, etc. The cutting heat data is expressed in units of joule (J).

[0150] Conduct a distribution calculation of the cutting heat to determine the proportion of heat flowing into the cutting tool, workpiece, and chip. The heat distribution ratio is determined by the thermophysical properties of the material, and the temperature field distribution of the cutting area is obtained by solving the heat conduction equation. The calculation result represents the cutting temperature value in units of degree Celsius (℃).

[0151] Comprehensively verify the process based on the three-component cutting force and the cutting temperature value. The verification criteria include: the cutting force should be less than 80% of the maximum allowable cutting force of the cutting tool, the cutting temperature should not exceed 70% of the heat-resistant temperature of the cutting tool material, and the cutting power should be within the rated power range of the machine tool. The process verification result is an important basis for evaluating the feasibility of the machining plan.

[0152] In this embodiment, by analyzing the cutting force and cutting temperature of the safe motion path, a comprehensive process verification of the milling cutter machining process is achieved. Based on the equal arc length sampling principle, the trajectory coordinate data of the tool contact point is obtained. Combining the calculation of the cutting-in and cutting-out angles and the cross-sectional area analysis, the accuracy of the process parameter calculation is ensured. The unit cutting force coefficient method is used to calculate the cutting force and decompose it into three-direction components, accurately reflecting the actual force state of the tool. Through the calculation of the cutting power and heat distribution, the temperature field distribution during the machining process is accurately predicted.

[0153] In one embodiment, the workpiece surface is analyzed for the real-time simulation data to obtain the corresponding workpiece surface quality information. Based on the process verification results, the tool prediction is performed on the workpiece surface quality information to obtain the corresponding tool prediction usage data, including:

[0154] After obtaining the real-time simulation data of the milling cutter machining, the workpiece surface is scanned point by point using a three-dimensional laser scanning device with a scanning accuracy of 0.1μm and a scanning spacing of 0.5mm. The obtained point cloud data is processed through data filtering and reconstruction to generate the three-dimensional topography data of the workpiece surface. The three-dimensional topography data includes the height information, shape features, and microstructural features of the workpiece surface.

[0155] Based on the reconstructed three-dimensional topography data, parameters such as the arithmetic mean deviation Ra and the maximum profile height Rz in the ISO4287 standard are used to quantitatively evaluate the surface roughness of the workpiece. By sampling and analyzing different regions of the workpiece surface, a roughness distribution cloud map is drawn, recording the roughness values and distribution rules of each region to form the workpiece surface roughness distribution data. The roughness evaluation uses 16 sampling lengths, each sampling length being 4mm.

[0156] Statistical analysis is performed on the workpiece surface roughness distribution data to extract characteristic parameters such as the average value, standard deviation, skewness, and kurtosis of the surface roughness. The extraction of the characteristic parameters uses the sub-region cumulative statistical method. The workpiece surface is divided into 36 evaluation regions, each region having an area of 10mm×10mm. The overall machining quality level of the workpiece is quantitatively characterized by the characteristic parameters.

[0157] 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 parameters, and the workpiece surface quality characteristic parameters after machining with tools of different wear degrees are recorded. The variation law of the surface roughness with the tool wear amount is obtained by least squares fitting, and a tool surface quality correlation parameter model is established.

[0158] According to the correlation parameter model, the BP neural network algorithm is used to predict tool wear. The workpiece surface quality characteristic parameters are used as the input variables of the neural network, and the tool wear amount is used as the output variable. The network weights are optimized through training samples. The hidden layer of the prediction model uses 15 neurons, and the training error is less than 0.01 mm. The prediction results include the flank wear width VB of the tool and the nose radius r.

[0159] Based on the tool wear prediction, the cutting temperature is introduced to correct the prediction results. The temperature in the cutting area is measured by a thermocouple, and a correction model of temperature and tool wear is established. The temperature correction coefficient increases with the increase of the cutting temperature. When the cutting temperature exceeds 600 °C, the correction coefficient reaches the maximum value of 1.5. The temperature compensation data reflects the influence of high temperature on the aggravation of tool wear.

[0160] The tool wear prediction parameters and the temperature compensation data are weighted and superimposed to calculate the final predicted tool usage data. The weighting coefficients are optimized and determined by the genetic algorithm, where the weight of the wear prediction parameters is 0.7 and the weight of the temperature compensation data is 0.3. The predicted tool usage data includes the remaining service life of the tool and the recommended replacement time, providing a basis for formulating the tool replacement plan.

[0161] In this embodiment, the workpiece surface is scanned and measured with high precision by a three-dimensional laser scanning device. Combining with the data reconstruction technology, the precise characterization of the workpiece surface micro-topography is realized, providing a reliable data basis for subsequent analysis. The surface roughness characteristics are extracted by the sub-region cumulative statistical method, overcoming the limitations of the traditional single-point measurement method and comprehensively reflecting the overall machining quality level of the workpiece. Based on the tool wear prediction model of the BP neural network, the relationship between the workpiece surface quality and the tool state is established, improving the prediction accuracy. The cutting temperature correction mechanism is introduced, comprehensively considering the influence of temperature on the tool life, making the prediction results more accurate and reliable. By optimizing the weighting coefficients with the genetic algorithm, the reasonable fusion of wear prediction and temperature compensation is realized, providing a scientific basis for tool replacement decision-making, effectively avoiding the decline of workpiece quality caused by excessive tool wear, and improving the machining efficiency and product qualification rate.

[0162] In one embodiment, the safety motion path and the predicted tool usage data are subjected to machining analysis, and the NC machining code of the initial workpiece machining strategy is optimized to obtain the corresponding optimized workpiece machining plan, including:

[0163] The machining analysis process of the safety motion path and the predicted tool usage data realizes the optimization of the workpiece machining plan through multiple links. This process executes the CAM simulation technology to dynamically simulate and optimize the milling cutter machining method.

[0164] The curvature analysis of the safe motion path obtains the path curvature data through numerical calculation methods. This analysis uses the three-point method to calculate the curvature values of each point on the path, discretizes the path into a series of point sets, and calculates the curvature of the arc formed by adjacent three points. The curvature data reflects the changes in the geometric characteristics of the path and provides basic data support for subsequent analysis.

[0165] The distribution correlation calculation between the path curvature data and the tool prediction usage data is carried out based on the correlation analysis method. This calculation maps the curvature data onto the tool usage prediction data, establishes a mathematical model to describe the corresponding relationship between the two, and generates a load distribution map reflecting the tool force distribution. The load distribution map intuitively shows the force conditions of each part of the tool during the machining process.

[0166] The threshold stratification of the tool load distribution map uses the multi-level threshold segmentation method. This method sets multiple load threshold levels and divides the load distribution map into machining load areas of different degrees. The setting of the threshold is based on the tool material characteristics and machining process requirements to ensure that the stratification results meet the actual machining needs.

[0167] The feed speed parameter optimization is adaptively adjusted according to the machining load area. In this optimization process, different load areas are mapped to the corresponding feed speed parameters. A lower feed speed is used in the high-load area, and a higher feed speed is used in the low-load area to form a speed optimization trajectory.

[0168] The motion characteristic analysis of the speed optimization trajectory focuses on studying the motion parameters of each axis. This analysis obtains parameters such as axial acceleration and speed change rate through dynamic calculations, and evaluates the smoothness and continuity of the motion trajectory. The axial motion parameters reflect the motion states of each axis of the machine tool during the machining process.

[0169] The jitter correction calculation optimizes the speed optimization trajectory based on the axial motion parameters. This calculation uses a filtering algorithm to eliminate the high-frequency vibration components in the trajectory, smooth the motion trajectory, reduce the mechanical vibration during the machining process, and obtain a stable motion path.

[0170] The tool attitude compensation process is realized through spatial geometric transformation. This process calculates the tilt angle of the tool during the machining process, compensates for the machining errors caused by tool deformation, ensures that the machining accuracy meets the requirements, and forms an attitude compensation trajectory.

[0171] The generation of the machining instruction sequence converts the attitude compensation trajectory into NC machining code. This conversion process follows specific post-processing rules to generate a G-code instruction sequence that meets the requirements of the NC system and realizes the output of optimized NC code.

[0172] The post-optimization of the workpiece machining strategy comprehensively improves the initial machining plan. This optimization integrates the optimization results of the above-mentioned links, adjusts process parameters such as cutting parameters and feed rates, and formulates the final optimized workpiece machining plan. The optimization plan comprehensively considers multiple factors such as machining efficiency, surface quality, and tool life to achieve the overall optimization of the machining process.

[0173] In this embodiment, an accurate control of the machining process is achieved based on a machining analysis method with multi-link optimization. By analyzing the curvature of the safe motion path and correlating it with the predicted tool usage data, the machining load distribution becomes clearer and more controllable. The threshold stratification method manages the tool load in partitions, and together with the adaptive adjustment of the feed rate, it improves the machining efficiency while ensuring the machining quality. The analysis of the axial motion parameters and the jitter correction ensure the stability of the machining process and effectively reduce the influence of mechanical vibration on the machining accuracy. Through the tool attitude compensation process, 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, extends the tool life while optimizing the machining efficiency, 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.

[0174] In one embodiment, a machining instruction sequence is generated according to the attitude compensation trajectory to obtain the corresponding optimized NC code, including:

[0175] The multiple trajectory points of the attitude compensation trajectory are grouped. The distance between adjacent trajectory points is used as the basis for segmentation. 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 attitude angle information, and these information are stored by establishing a trajectory segmentation database.

[0176] Based on the trajectory segmentation data, the trapezoidal velocity planning algorithm is used to calculate the feed rate curve. This algorithm divides the machining process into an acceleration segment, a constant velocity segment, and a deceleration segment. In the acceleration segment, the feed rate increases from zero to the set value according to the preset acceleration; in the constant velocity segment, the set feed rate is kept constant; in the deceleration segment, the feed rate decreases to zero according to the preset deceleration. The displacement of each segment is obtained through integral operation to ensure the continuity of the velocity curve.

[0177] According to the calculated feed rate curve, the linear interpolation algorithm is used to perform interpolation operation on the machining trajectory. The interpolation algorithm is based on the equal time interval principle, calculates the motion increments of each axis according to the set interpolation period, and generates an interpolation point sequence. The interpolation point sequence includes the position coordinates of each interpolation point and the corresponding time information.

[0178] Apply the tool radius compensation algorithm to the interpolation point sequence to calculate the tool center point position. This algorithm offsets the trajectory along the normal vector direction of the tool axis with the tool radius as the offset amount to obtain the compensated tool position coordinates. During the compensation calculation process, the tool wear factor is also considered, and the compensation amount is corrected through the tool wear compensation coefficient.

[0179] Based on the machine tool kinematic model, perform inverse kinematic solution on the tool position coordinates. By establishing the conversion relationship between the workpiece coordinate system and the machine tool coordinate system, calculate the displacement of each motion axis. This process involves complex spatial geometric operations, and the operation results satisfy the machine tool kinematic constraint conditions.

[0180] Convert the calculated displacement of each axis into the code format recognizable by the machine tool. The conversion process follows specific coding rules, including quantization processing of the displacement, coding conversion of the feed speed, and code generation of the auxiliary functions. The generated machine tool code sequence conforms to the syntax specifications of the machine tool control system.

[0181] Optimize and integrate the processing parameters in the machine tool code sequence. The integration process adjusts parameters such as the cutting amount and feed speed based on the preset process rules. The optimized NC code has higher processing efficiency and better processing quality, while ensuring the safety and stability of the processing process.

[0182] This optimized NC code integrates the processing results of multiple links such as posture compensation, speed planning, and trajectory interpolation, and realizes the conversion from the posture compensation trajectory to the actual processing instructions. With the support of strict mathematical models and algorithms, the unity of processing accuracy and efficiency is guaranteed.

[0183] Among them, the formulas for the inverse solution operation include:

[0184] ; ; ;

[0185] ; ;

[0186] A and B are the angles of the rotating axes, X, Y, and Z are the positions of the machine tool axes, i, j, and k represent the tool direction vectors, L is the tool length, x is the target position of the tool tip point in the X-axis direction of the workpiece coordinate system, y is the target position of the tool tip point in the Y-axis direction of the workpiece coordinate system, z is the target position of the tool tip point in the Z-axis direction of the workpiece coordinate system, and atan2 is the arctangent function.

[0187] In this embodiment, by adopting a trajectory segmentation method based on a distance threshold, the continuity and smoothness of the trajectory during complex surface machining are ensured. The trapezoidal velocity planning algorithm is used to calculate the feed rate, making the acceleration and deceleration processes smoother and effectively reducing the impact vibration of the machine tool. By combining the linear interpolation algorithm and the tool radius compensation algorithm, the accuracy of the machining trajectory is improved. At the same time, considering the tool wear factor, the tool service life is extended. Based on the inverse solution operation of the machine tool kinematic model, the accuracy of the spatial trajectory conversion is guaranteed, and abnormal situations such as machine tool overtravel are avoided. The NC code optimized through parameter integration improves the machining efficiency while ensuring the machining quality, achieving the unity of high-efficiency and high-quality machining.

[0188] Referring to Figure 2 As shown, the present invention also provides a milling cutter machining system based on CAM simulation technology, which is applied to the milling cutter machining method based on CAM simulation technology in any one of the above, including:

[0189] An acquisition module, which is used to acquire the three-dimensional data of the workpiece mold and the milling cutter parameters, and perform process planning to obtain an initial workpiece machining strategy;

[0190] An analysis module, which is used to perform tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory;

[0191] An association module, which is used to perform CAM simulation analysis on the initial workpiece machining strategy to obtain the corresponding real-time simulation data, and perform collision interference detection on the tool motion trajectory according to the real-time simulation data to obtain the corresponding safe motion path;

[0192] 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 result;

[0193] A control module, which is used to analyze the workpiece surface based on the real-time simulation data to obtain the corresponding workpiece surface quality information, and perform tool prediction on the workpiece surface quality information based on the process verification result to obtain the corresponding tool predicted usage data;

[0194] An execution module, which is used to perform machining analysis on the safe motion path and the tool predicted usage data, and optimize the NC machining code of the initial workpiece machining strategy to obtain the corresponding optimized workpiece machining plan.

[0195] A milling cutter processing system based on CAM simulation technology provided by the present invention can conduct process planning by obtaining three-dimensional data of a workpiece mold and milling cutter parameters. By combining tool path planning and contact point calculation, it can more accurately evaluate various parameters during the processing, 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 carried out to achieve precise control and optimization of the tool movement trajectory, effectively avoiding collision problems during the processing and ensuring processing safety. Process verification is carried out based on cutting force and cutting temperature analysis, which 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 with the predicted tool usage data, a more reasonable tool usage strategy is formulated, and the optimization operation of the entire processing process is achieved through NC machining code optimization, thereby effectively improving the processing efficiency and extending the tool service 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 diverse processing needs.

[0196] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness 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.

[0197] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. 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 similarly included in the patent protection scope of the present invention.

Claims

1. A milling cutter machining method based on CAM simulation technology, characterized in that, Including: Obtain the three-dimensional data of the workpiece mold and the milling cutter parameters, and perform process planning to obtain the initial workpiece machining strategy; Perform tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory; Perform CAM simulation analysis on the initial workpiece machining strategy to obtain the 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; Perform cutting force and cutting temperature analysis on the safe motion path to obtain the corresponding process verification result; Perform workpiece surface analysis on the real-time simulation data to obtain the corresponding workpiece surface quality information, and perform tool prediction on the workpiece surface quality information based on the process verification result to obtain the corresponding tool prediction usage data; Perform machining analysis on the safe motion path and the tool prediction usage data, and optimize the NC machining code for the initial workpiece machining strategy to obtain the corresponding optimized workpiece machining plan; The performing workpiece surface analysis on the real-time simulation data to obtain the corresponding workpiece surface quality information, and performing tool prediction on the workpiece surface quality information based on the process verification result to obtain the corresponding tool prediction usage data includes: Perform surface three-dimensional analysis on the real-time simulation data to obtain surface three-dimensional topography data; Perform workpiece surface roughness analysis based on the surface three-dimensional topography data to obtain workpiece surface roughness distribution data; Perform feature statistics on the workpiece surface roughness distribution data to obtain workpiece surface quality characteristic parameters; Perform verification association on the workpiece surface quality characteristic parameters based on the process verification result to obtain tool surface quality association parameters; Perform wear prediction on the tool surface quality association parameters to obtain tool wear prediction parameters; Perform temperature correction on the tool wear prediction parameters based on the process verification result to obtain temperature compensation data; Perform comprehensive calculation and processing on the tool wear prediction parameters and the temperature compensation data to obtain the tool prediction usage data.

2. The milling cutter machining method based on CAM simulation technology according to claim 1, characterized in that The obtaining the three-dimensional data of the workpiece mold and the milling cutter parameters, and performing process planning to obtain the initial workpiece machining strategy includes: Perform surface analysis on the three-dimensional data of the workpiece mold to obtain the corresponding workpiece surface data; Perform model analysis on the three-dimensional data of the workpiece mold based on the workpiece surface data to obtain the corresponding mold three-dimensional data; Perform preliminary construction based on the mold three-dimensional data and the workpiece surface data to obtain the corresponding initial workpiece mold model; Perform machining difficulty analysis on the initial workpiece mold model to obtain regional machining complexity data; Perform matching calculation on the regional machining complexity data based on the milling cutter parameters to obtain a set of machining specification parameters; Perform zoning machining planning on the initial workpiece mold model according to the set of machining specification parameters to obtain the regional machining sequence; Perform calculation of the tool entry and exit paths for the regional machining sequence to obtain the spatial conversion trajectory of the tool; Perform calculation of the machining feed rate and spindle speed based on the spatial conversion trajectory to obtain the corresponding process parameters; Combining and optimizing the process parameters with the regional machining sequence to obtain an initial workpiece machining strategy.

3. The milling cutter machining method based on CAM simulation technology according to claim 1, characterized in that Performing tool path planning and contact point calculation 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 the 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; Calculating the curvature radius of the sampling points according to the tool path curve to obtain the corresponding corner parameter data; Performing speed calculation on the corner parameter data to obtain the feed speed of each segment of the trajectory; Performing time interval sampling on the tool path curve according to the feed speed to obtain a cutter location sequence; Performing cutter axis vector calculation on the cutter location sequence to obtain spatial attitude data; Performing interference inspection on the tool contact area according to the spatial attitude data to obtain an effective cutting boundary; Performing local trajectory optimization on the effective cutting boundary to obtain a contact point sequence; Combining and mapping the contact point sequence with the feed speed to obtain the tool motion trajectory.

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

5. The milling cutter machining method based on CAM simulation technology according to claim 1, characterized in that, Performing cutting force and cutting temperature analysis on the safe motion path to obtain the corresponding process verification results, including: Performing tool contact point trajectory sampling on the safe motion path to obtain the corresponding sampling data point coordinates; Performing instantaneous cutting angle calculation of the tool according to the sampling data point coordinates to obtain the corresponding cutting-in and cutting-out angle data; Performing geometric size cutting calculation on the cutting-in and cutting-out angle data to obtain the corresponding cutting cross-sectional area data; Performing unit cutting parameter calculation 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 the corresponding three-direction cutting force components; Performing cutting power calculation according to the three-direction cutting force components to obtain the corresponding cutting heat data; Perform heat distribution calculation on the cutting heat data to obtain the corresponding cutting temperature value; Perform machining simulation verification analysis based on the three-way cutting force components and the cutting temperature value to obtain the process verification result.

6. The milling cutter machining method based on CAM simulation technology according to claim 1, characterized in that, The step of performing machining analysis on the safe motion path and the predicted tool usage data, and optimizing the NC machining code for the initial workpiece machining strategy to obtain the corresponding optimized workpiece machining plan includes: Perform curvature analysis on the safe motion path to obtain the corresponding path curvature data; Perform distribution correlation calculation on the predicted tool usage data based on the path curvature data to obtain the corresponding tool load distribution map; Perform threshold stratification on the tool load distribution map to obtain the corresponding machining load area; Optimize the feed speed parameter of the safe motion path based on the machining load area to obtain the corresponding speed optimization trajectory; Perform motion characteristic analysis on the speed optimization trajectory to obtain the corresponding axial motion parameters; Perform jitter correction calculation on the speed optimization trajectory based on the axial motion parameters to obtain the corresponding stable motion path; Perform tool attitude compensation processing on the stable motion path to obtain the corresponding attitude compensation trajectory; Generate a machining instruction sequence based on the attitude compensation trajectory to obtain the corresponding optimized NC code; Perform post-optimization on the initial workpiece machining strategy based on the optimized NC code to obtain the optimized workpiece machining plan.

7. The milling cutter machining method based on CAM simulation technology according to claim 6, characterized in that The step of generating a machining instruction sequence based on the attitude compensation trajectory to obtain the corresponding optimized NC code includes: Perform segmented processing on the attitude compensation trajectory to obtain the corresponding trajectory segment data; Perform speed planning calculation on the trajectory segment data to obtain the corresponding feed speed curve; Perform machining trajectory interpolation operation based on the feed speed curve to obtain the corresponding interpolation point sequence; Perform tool compensation calculation on the interpolation point sequence to obtain the corresponding tool position coordinates; Perform inverse kinematic calculation on the machine tool axial motion based on the tool position coordinates to obtain the corresponding motion displacement of each axis; Perform coding conversion processing on the motion displacement of each axis to obtain the corresponding machine tool code sequence; Integrate the machining parameters based on the machine tool code sequence to obtain the corresponding optimized NC code; Among them, the formula for inverse kinematic calculation includes: ； ； ； ； ； A and B are the angles of the rotating axes, X, Y, and Z are the positions of the machine tool axes, i, j, and k represent the tool direction vector, L is the tool length, x is the target position of the tool tip point in the X-axis direction of the workpiece coordinate system, y is the target position of the tool tip point in the Y-axis direction of the workpiece coordinate system, z is the target position of the tool tip point in the Z-axis direction of the workpiece coordinate system, and atan2 is the arctangent function.

8. A milling cutter processing system based on CAM simulation technology, characterized in that, Applied to the milling cutter machining method based on CAM simulation technology according to any one of claims 1-7 above, including: An acquisition module, which is used to acquire the three-dimensional data of the workpiece mold and the milling cutter parameters, and perform process planning to obtain the initial workpiece machining strategy; An analysis module, which is used to perform tool path planning and contact point calculation on the initial workpiece machining strategy to obtain the corresponding tool motion trajectory; An associated module, which 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 a 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 a corresponding process verification result; A control module, which is used to analyze the workpiece surface of the real-time simulation data to obtain corresponding workpiece surface quality information, and perform tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool predicted usage data; An execution module, which is used to perform processing analysis on the safe motion path and the tool predicted usage data, and optimize the NC machining code of the initial workpiece processing strategy to obtain a corresponding optimized workpiece processing plan; The analysis of the workpiece surface of the real-time simulation data to obtain corresponding workpiece surface quality information, and the tool prediction on the workpiece surface quality information based on the process verification result to obtain corresponding tool predicted usage data includes: Performing surface three-dimensional analysis on the real-time simulation data to obtain surface three-dimensional topography data; Performing workpiece surface roughness analysis based on the surface three-dimensional topography data to obtain workpiece surface roughness distribution data; Performing feature statistics on the workpiece surface roughness distribution data to obtain workpiece surface quality characteristic parameters; Performing verification association on the workpiece surface quality characteristic parameters based on the process verification result to obtain tool surface quality association parameters; Performing wear prediction on the tool surface quality association parameters to obtain tool wear prediction parameters; Performing temperature correction on the tool wear prediction parameters based on the process verification result to obtain temperature compensation data; Performing comprehensive calculation processing on the tool wear prediction parameters and the temperature compensation data to obtain the tool predicted usage data.

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