Feed speed optimization method and system for complex trajectory constant force high efficiency cutting
By establishing a direct functional relationship model between spindle torque and feed rate, and combining the hybrid intelligent algorithms of NSGA-Ⅱ and PSO, the accuracy and stability issues of feed rate optimization in CNC milling of complex curved surface parts were solved, achieving efficient and stable machining results.
Patent Information
- Application Number
- CN202610184663.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-09
- Publication Date
- 2026-05-26
Smart Images
Figure CN122085885A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of CNC machining and intelligent manufacturing technology, and specifically relates to a method and system for optimizing feed rate for high-efficiency cutting with constant force on complex trajectories. Background Technology
[0002] In CNC milling of complex curved surfaces, cutting parameters change during the machining process, leading to corresponding changes in cutting forces. Traditional machining methods typically employ conservative cutting parameters (such as reducing feed rate) to avoid tool breakage, machine tool damage, or decreased machining quality due to sudden load changes. However, this conservative strategy often results in low machining efficiency. Drastic changes in cutting conditions (such as cutting width and depth) make it difficult for a constant feed rate to adapt to load fluctuations, thus affecting machining stability and tool life.
[0003] In recent years, to improve machining efficiency and process stability, scholars have proposed various feed rate optimization methods, mainly including online adaptive control and offline optimization. Online adaptive control methods dynamically adjust the feed rate by monitoring signals such as cutting force, torque, or current in real time; however, these systems are complex, costly, and have strict real-time requirements. Offline optimization methods, on the other hand, are based on machining process simulation and perform global optimization of the feed rate before machining, offering advantages such as high flexibility and ease of integration.
[0004] Existing offline optimization methods mostly establish an indirect relationship between feed rate and machining load based on material removal rate (MRR) or empirical formulas, failing to fully utilize direct mechanical models that reflect machining load (such as torque models), resulting in limited optimization accuracy. In addition, most methods only focus on a single objective (such as minimizing machining time), failing to fully consider the impact of load fluctuations on tool life and machining quality, and lacking systematic consideration of actual machining constraints such as feed rate smoothness. Summary of the Invention
[0005] The purpose of this invention is to provide a method and system for optimizing feed rate in high-efficiency constant-force cutting with complex trajectories, so as to solve the above-mentioned problems.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a feed rate optimization method for efficient constant-force cutting with complex trajectories, comprising: Establish a functional relationship model between spindle torque and feed rate; Based on the functional relationship model, a multi-objective optimization function is constructed with the objectives of minimizing machining time and minimizing spindle torque fluctuation. The multi-objective optimization function is solved under preset constraints to obtain the optimal solution set; The optimal solution is selected from the set of optimal solutions, an optimized feed rate sequence is generated, and the CNC machining code is updated accordingly.
[0007] Furthermore, establishing the functional relationship model between spindle torque and feed rate includes: Based on the milling force mechanism model, the relationship between the instantaneous undeformed chip thickness and the feed rate is derived, and the spindle torque is then established. With feed rate The linear function relationship between them: ,in As constants for tool geometry parameters and workpiece material cutting coefficients, this model establishes a direct mapping between the feed rate and the corresponding torque at each tool position point.
[0008] Furthermore, the multi-objective optimization function constructed based on the functional relationship model, with the objectives of minimizing machining time and minimizing spindle torque fluctuation, includes: Minimize total machining time: expressed as the sum of the reciprocals of the feed rates at each tool position point, i.e. ; Minimize spindle torque ripple: expressed as the standard deviation of the torque series, i.e. ; The two objectives are combined into a total objective function through a weighted summation: ,in and These are adjustable weighting coefficients.
[0009] Furthermore, the preset constraints include: Constant torque constraint: ,in To allow for torque variability; at the same time, It must be greater than the average torque of the original machining, but not exceed 90% of its maximum value; Feed rate constraint: The optimized feed rate must not exceed the maximum value allowed by the machine tool or process. ,Right now ; Feed rate smoothness constraint: This is achieved by requiring acceleration / deceleration distances to be less than tool travel distances, specifically expressed as follows: ,in This is the maximum allowable acceleration of the machine tool. For the first The distance the cutting tool travels.
[0010] Furthermore, the step of solving the multi-objective optimization function under preset constraints to obtain the optimal solution set includes: A hybrid intelligent optimization algorithm combining NSGA-II and Particle Swarm Optimization (PSO) is used to solve the multi-objective optimization function to obtain the Pareto optimal solution set.
[0011] Further, specific steps include: An initial population, including the position and velocity of each individual, is randomly generated within the feasible region of the feed rate. Algorithm parameters are set, including population size, maximum number of iterations, and inertia weight. Learning factors ; Non-dominated sorting and crowding calculation: Perform fast non-dominated sorting on the initial population and calculate the crowding of individuals within each non-dominated layer; Iterative optimization: a. Generating offspring: The offspring population is generated through selection, crossover, and mutation operations in NSGA-II; b. Merging and Elite Selection: Merge the parent and offspring populations, perform non-dominated ranking and crowding calculations, and select a new parent population based on the elite strategy; c. PSO Update: In the new parent population, the PSO algorithm is applied to update the velocity and position of each individual; the velocity update formula is: ,in For inertial weights, , As a learning factor, , It is a random number. For the individual's historical best position, The globally optimal position is found; the position is updated as follows: If the solution at the new position dominates the original solution, then replace the original solution. d. Update the optimal solution: Based on the updated population, recalculate and update the historical optimal solution for each individual. and global optimal ; Termination and Output: Repeat the iteration steps until the maximum number of iterations is reached, and output the final non-dominated solution set.
[0012] Furthermore, the step of selecting the optimal solution from the optimal solution set, generating an optimized feed rate sequence, and updating the CNC machining code accordingly includes: From the Pareto solution set, based on the weighted sum of the objective function... The minimum principle is used to select the final optimal feed rate sequence; the obtained optimal feed rate sequence is then updated to the corresponding tool position instructions in the original CNC program to generate optimized G-code for actual machining.
[0013] Secondly, the present invention provides a feed rate optimization system for high-efficiency constant-force cutting with complex trajectories, comprising: The model building module is used to establish a functional relationship model between spindle torque and feed rate. The objective building module is used to construct a multi-objective optimization function based on a functional relationship model, with the objectives of minimizing machining time and minimizing spindle torque fluctuation. The solution module is used to solve the multi-objective optimization function under preset constraints to obtain the optimal solution set; The output module is used to select the optimal solution from the optimal solution set, generate an optimized feed rate sequence, and update the CNC machining code accordingly.
[0014] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the feed rate optimization method for constant force high-efficiency cutting of complex trajectories.
[0015] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the feed rate optimization method for constant force high-efficiency cutting of complex trajectories.
[0016] Compared with the prior art, the present invention has the following technical effects: This invention establishes a direct function model between feed rate and spindle torque, constructs a multi-objective optimization problem with minimizing machining time and minimizing torque fluctuation as its core, and introduces constant torque constraints, feed rate limits and smoothness constraints. It employs a hybrid intelligent algorithm combining NSGA-II and particle swarm optimization (PSO) for efficient solution, thereby significantly improving machining efficiency while ensuring stable machining load.
[0017] This invention directly establishes an explicit functional relationship between feed rate and spindle torque (a direct reflection of machining load), making the optimization basis closer to physical reality and improving the accuracy and reliability of optimization.
[0018] Simultaneously considering two key objectives—machining efficiency (time) and process stability (torque fluctuation)—it meets the actual needs of roughing and high-performance machining. It comprehensively considers constraints such as constant load, machine tool performance limits, and motion smoothness to ensure that the optimization results can be directly and safely applied to actual machine tools.
[0019] The proposed NSGA-II and PSO hybrid algorithm combines the advantages of the former in Pareto solution set search and the latter in global exploration and fast convergence. It effectively overcomes the shortcomings of single algorithms that are prone to getting trapped in local optima or having low computational efficiency when solving high-dimensional complex problems, and can stably and efficiently obtain high-quality optimization solutions. Attached Figure Description
[0020] Figure 1 This is a schematic diagram of the cutting width change during the complex trajectory machining process in this invention.
[0021] Figure 2 This is the overall flowchart of the feed rate optimization method for constant force high-efficiency cutting proposed in this invention.
[0022] Figure 3 This is the overall flowchart of the NSGA-Ⅱ and PSO hybrid optimization algorithm proposed in this invention.
[0023] Figure 4 This is a schematic diagram of the S-shaped curve processing path used for verification in an embodiment of the present invention.
[0024] Figure 5 This is a curve comparing the spindle motor torque before and after optimization in an embodiment of the present invention (with the Y-axis as a reference).
[0025] Figure 6 This is a curve comparing the spindle motor torque before and after optimization in an embodiment of the present invention (with time as a reference). Detailed Implementation
[0026] The present invention will be further described below with reference to the accompanying drawings: Example 1, please refer to Figure 2 This invention provides a feed rate optimization method for efficient constant-force cutting with complex trajectories, comprising: Establish a functional relationship model between spindle torque and feed rate; Based on the functional relationship model, a multi-objective optimization function is constructed with the objectives of minimizing machining time and minimizing spindle torque fluctuation. The multi-objective optimization function is solved under preset constraints to obtain the optimal solution set; The optimal solution is selected from the set of optimal solutions, an optimized feed rate sequence is generated, and the CNC machining code is updated accordingly.
[0027] This invention establishes a direct function model between feed rate and spindle torque, constructs a multi-objective optimization problem with minimizing machining time and minimizing torque fluctuation as its core, and introduces constant torque constraints, feed rate limits and smoothness constraints. It employs a hybrid intelligent algorithm combining NSGA-II and particle swarm optimization (PSO) for efficient solution, thereby significantly improving machining efficiency while ensuring stable machining load.
[0028] Example 2: This invention provides a feed rate optimization method for high-efficiency constant-force cutting with complex trajectories, comprising: S1: Establish a functional relationship model between spindle torque and feed rate.
[0029] Specifically, based on the milling force mechanism model, the relationship between the instantaneous undeformed chip thickness and the feed rate is derived, thereby establishing the spindle torque. With feed rate The linear function relationship between them: ,in These are constants related to tool geometry, workpiece material cutting coefficients, etc. This model establishes a direct mapping between the feed rate and the corresponding torque at each tool position point.
[0030] S2: Construct a multi-objective optimization function.
[0031] 1) Optimization objectives include: (1) Minimize total machining time: expressed as the sum of the reciprocals of the feed rates at each tool position point, i.e. .
[0032] (2) Minimize spindle torque fluctuation: expressed as the standard deviation of the torque sequence, i.e. .
[0033] The two objectives are combined into a total objective function through a weighted summation: ,in and These are adjustable weighting coefficients.
[0034] S3: Set processing constraints.
[0035] The constraints include: (1) Constant torque constraint: To ensure stable machining load, the optimized torque value should be maintained at the target torque. Within a certain range of fluctuations nearby, i.e. ,in This represents the allowable torque fluctuation rate (typically 20%). Meanwhile, It needs to be greater than the average torque of the original machining, but not exceed 90% of its maximum value, in order to balance efficiency and safety.
[0036] (2) Feed rate limit constraint: The optimized feed rate shall not exceed the maximum value allowed by the machine tool or process. ,Right now .
[0037] (3) Feed rate smoothness constraint: To prevent drastic changes in feed rate from impacting the machine tool and cutting tool, and to ensure machining quality, it is necessary to limit the rate of change of feed rate between adjacent tool positions. This constraint is achieved by requiring that the acceleration / deceleration distance be less than the tool travel distance, and the specific expression is as follows: ,in This is the maximum allowable acceleration of the machine tool. For the first The distance the cutting tool travels.
[0038] S4: Solved using a hybrid intelligent optimization algorithm.
[0039] To address the aforementioned multi-objective, multi-constraint, and high-dimensional optimization problems, a hybrid optimization algorithm combining NSGA-II and PSO is proposed. The core steps of this algorithm include: Initialization: Randomly generate an initial population within the feasible region of feed rates, including the position and velocity of each individual (a set of feed rate sequences). Set algorithm parameters, such as population size, maximum number of iterations, and inertia weight. Learning factors .
[0040] Non-dominated sorting and crowding calculation: Perform fast non-dominated sorting on the initial population and calculate the crowding of individuals within each non-dominated layer.
[0041] Iterative optimization: a. Generating offspring: The offspring population is generated through selection, crossover, and mutation operations of NSGA-II.
[0042] b. Merging and Elite Selection: Merge the parent and offspring populations, perform non-dominated sorting and crowding calculations, and select a new parent population based on an elite strategy (binary tournament selection).
[0043] c. PSO Update: In the new parent population, the PSO algorithm is applied to update the velocity and position of each individual. The velocity update formula is: ,in For the individual's historical best position, The globally optimal position is taken from the current best non-dominated sorting layer. The position is updated as follows: If the solution at the new position dominates the original solution, then replace the original solution.
[0044] d. Update the optimal solution: Based on the updated population, recalculate and update the historical optimal solution for each individual. and global optimal .
[0045] Termination and Output: Repeat the iteration steps until the maximum number of iterations is reached, and output the final non-dominated solution set (Pareto front).
[0046] Optimal solution selection: From the Pareto solution set, based on the weighted sum of the objective function... The principle of minimum is used to select the final optimal feed rate sequence.
[0047] S5: Generate optimized CNC code.
[0048] The obtained optimal feed rate sequence is updated to the corresponding tool position instructions in the original numerical control (NC) program to generate optimized G-code for actual machining.
[0049] Example 3: This invention provides a feed rate optimization method for high-efficiency constant-force cutting with complex trajectories, comprising: This embodiment takes the milling of an S-shaped groove on a titanium alloy (Ti-6Al-4V) workpiece as an example, and applies the method described in this invention to optimize the feed rate. The machine tool is a three-axis vertical machining center, and the cutting tool is a 10mm diameter four-flute flat-bottom end mill.
[0050] S1: Establish the torque model Based on the tool geometry parameters and the milling force coefficient of the workpiece material, the spindle torque at each discrete tool position point is calculated using an instantaneous milling force model. With command feed rate proportionality coefficient Thus establishing The relationship. This step is completed using an offline simulation program.
[0051] S2: Set optimization goals and parameters The optimization objective is set as minimizing processing time and torque fluctuation. The weighting coefficients are set to... , The focus is on machining efficiency. The original G-code feed rate is 90 mm / min.
[0052] S3: Set constraints Constant torque constraint: 110% of the average value of the original machining simulation torque is taken as the target torque. Volatility .
[0053] Feed rate limit: Set upper limit The lower limit is the original feed rate of 90 mm / min.
[0054] Smoothness constraint: Set the maximum acceleration according to the machine tool performance. .
[0055] S4: Run the hybrid optimization algorithm Algorithm parameter settings: Population size Maximum number of iterations Inertial weight Learning factor .
[0056] Run the hybrid optimization algorithm. In contrast, running the NSGA-II algorithm alone (with parameters identical to the NSGA-II part of the hybrid algorithm) five times failed to obtain a Pareto solution set satisfying the constraints, while the hybrid algorithm converged stably in all five runs and output a Pareto solution set. A weighted objective function was then selected from this set. The solution with the smallest value is taken as the optimal feed rate sequence.
[0057] S5: Generate and validate optimized G-code Write the optimal feed rate sequence into the original G code to generate the optimized NC program.
[0058] Table 1. G-code before and after S-curve optimization
[0059] Analysis of experimental results: Actual milling operations were performed using the G-code before and after optimization, and the spindle motor torque signal was collected.
[0060] Processing time: 69.73 seconds before optimization, 51.60 seconds after optimization, a reduction of 26%.
[0061] Torque stability: The torque variance was 0.002 (Nm)² before optimization and decreased to 0.0014 (Nm)² after optimization, with a 30% reduction in fluctuation. The average torque increased slightly from 0.0789 Nm to 0.0872 Nm, indicating an improvement in load level, but it remained within a safe range.
[0062] Torque tracking effect: such as Figure 5 and Figure 6 As shown, the optimized torque curve (Topt) is more stable than the unoptimized curve (Tori), and it is closer to the set target torque band, verifying the effectiveness of the constant torque constraint.
[0063] In summary, this embodiment fully verifies the effectiveness and superiority of the proposed method in complex trajectory processing in simultaneously improving processing efficiency and maintaining load stability.
[0064] In another embodiment of the present invention, a feed rate optimization system for high-efficiency constant-force cutting with complex trajectories is provided. This system can be used to implement the above-mentioned feed rate optimization method for high-efficiency constant-force cutting with complex trajectories. Specifically, the system includes: The model building module is used to establish a functional relationship model between spindle torque and feed rate. The objective building module is used to construct a multi-objective optimization function based on a functional relationship model, with the objectives of minimizing machining time and minimizing spindle torque fluctuation. The solution module is used to solve the multi-objective optimization function under preset constraints to obtain the optimal solution set; The output module is used to select the optimal solution from the optimal solution set, generate an optimized feed rate sequence, and update the CNC machining code accordingly.
[0065] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.
[0066] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a feed rate optimization method for complex trajectory constant force high-efficiency cutting.
[0067] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the feed rate optimization method for efficient constant-force cutting of complex trajectories in the above embodiments.
[0068] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0069] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.
Claims
1. A method for optimizing feed rate in high-efficiency constant-force cutting with complex trajectories, characterized in that, include: Establish a functional relationship model between spindle torque and feed rate; Based on the functional relationship model, a multi-objective optimization function is constructed with the objectives of minimizing machining time and minimizing spindle torque fluctuation. The multi-objective optimization function is solved under preset constraints to obtain the optimal solution set; The optimal solution is selected from the set of optimal solutions, an optimized feed rate sequence is generated, and the CNC machining code is updated accordingly.
2. The feed rate optimization method for high-efficiency constant-force cutting with complex trajectories according to claim 1, characterized in that, The establishment of the functional relationship model between spindle torque and feed rate includes: Based on the milling force mechanism model, the relationship between the instantaneous undeformed chip thickness and the feed rate is derived, and the spindle torque is then established. With feed rate The linear function relationship between them: ,in As constants for tool geometry parameters and workpiece material cutting coefficients, this model establishes a direct mapping between the feed rate and the corresponding torque at each tool position point.
3. The feed rate optimization method for high-efficiency cutting with constant force on complex trajectories according to claim 1, characterized in that, The multi-objective optimization function, based on the functional relationship model, aims to minimize machining time and spindle torque fluctuation, including: Minimize total machining time: expressed as the sum of the reciprocals of the feed rates at each tool position point, i.e. ; Minimize spindle torque ripple: expressed as the standard deviation of the torque series, i.e. ; The two objectives are combined into a total objective function through a weighted summation: ,in and These are adjustable weighting coefficients.
4. The feed rate optimization method for high-efficiency constant-force cutting with complex trajectories according to claim 1, characterized in that, The preset constraints include: Constant torque constraint: ,in To allow for torque variability; at the same time, It must be greater than the average torque of the original machining, but not exceed 90% of its maximum value; Feed rate constraint: The optimized feed rate must not exceed the maximum value allowed by the machine tool or process. ,Right now ; Feed rate smoothness constraint: This is achieved by requiring acceleration / deceleration distances to be less than tool travel distances, specifically expressed as follows: ,in This is the maximum allowable acceleration of the machine tool. For the first The distance the cutting tool travels.
5. The feed rate optimization method for high-efficiency constant-force cutting with complex trajectories according to claim 4, characterized in that, The step of solving the multi-objective optimization function under preset constraints to obtain the optimal solution set includes: A hybrid intelligent optimization algorithm combining NSGA-II and Particle Swarm Optimization (PSO) is used to solve the multi-objective optimization function to obtain the Pareto optimal solution set.
6. The feed rate optimization method for high-efficiency constant-force cutting with complex trajectories according to claim 5, characterized in that, The specific steps include: An initial population, including the position and velocity of each individual, is randomly generated within the feasible region of the feed rate. Algorithm parameters are set, including population size, maximum number of iterations, and inertia weight. Learning factors ; Non-dominated sorting and crowding calculation: Perform fast non-dominated sorting on the initial population and calculate the crowding of individuals within each non-dominated layer; Iterative optimization: a. Generating offspring: The offspring population is generated through selection, crossover, and mutation operations in NSGA-II; b. Merging and Elite Selection: Merge the parent and offspring populations, perform non-dominated ranking and crowding calculations, and select a new parent population based on the elite strategy; c. PSO Update: In the new parent population, the PSO algorithm is applied to update the velocity and position of each individual; the velocity update formula is: ,in For inertial weights, , As a learning factor, , It is a random number. For the individual's historical best position, The globally optimal position is found; the position is updated as follows: If the solution at the new position dominates the original solution, then replace the original solution. d. Update the optimal solution: Based on the updated population, recalculate and update the historical optimal solution for each individual. and global optimal ; Termination and Output: Repeat the iteration steps until the maximum number of iterations is reached, and output the final non-dominated solution set.
7. The feed rate optimization method for high-efficiency constant-force cutting with complex trajectories according to claim 1, characterized in that, The step of selecting the optimal solution from the optimal solution set, generating an optimized feed rate sequence, and updating the CNC machining code accordingly includes: From the Pareto solution set, based on the weighted sum of the objective function... The minimum principle is used to select the final optimal feed rate sequence; the obtained optimal feed rate sequence is then updated to the corresponding tool position instructions in the original CNC program to generate optimized G-code for actual machining.
8. A feed rate optimization system for high-efficiency cutting with constant force on complex trajectories, characterized in that, include: The model building module is used to establish a functional relationship model between spindle torque and feed rate. The objective building module is used to construct a multi-objective optimization function based on a functional relationship model, with the objectives of minimizing machining time and minimizing spindle torque fluctuation. The solution module is used to solve the multi-objective optimization function under preset constraints to obtain the optimal solution set; The output module is used to select the optimal solution from the optimal solution set, generate an optimized feed rate sequence, and update the CNC machining code accordingly.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the feed rate optimization method for constant force high-efficiency cutting of complex trajectories as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the feed rate optimization method for constant force high-efficiency cutting of complex trajectories as described in any one of claims 1 to 7.