Method for optimizing chip breaking performance of chip breaker groove of indexable cutter

Abstract

The invention discloses a method for optimizing the chip breaking performance of a chip breaker groove of an indexable cutter. The method comprises the steps that S1, groove profile parameters and machining process parameters of the indexable cutter are obtained; s2, determining the influence of each groove profile parameter on the chip breaking performance, and preferably selecting a key groove profile parameter with obvious influence; s3, taking the key groove profile parameters as optimization targets, and constructing a specific function relationship between the key groove profile parameters and related parameters; s4, determining a dynamic weight distribution value of the optimization target; s5, establishing a fitness function of the optimization target based on the dynamic weight distribution value, constructing a hybrid genetic optimization algorithm, performing algorithm optimization according to the specific function relationship and the fitness function, and obtaining an optimal solution of the optimization target through the hybrid genetic optimization algorithm; and S6, verifying an optimization result through finite element simulation analysis. According to the technical scheme, the groove type structure of the chip breaker groove of the cutter can be optimized, the chip breaking performance of the chip breaker groove is enhanced, and the machining efficiency of metal cutting is improved.

Description

Technical Field

[0001] This application relates to the technical field of metal cutting machining, and more specifically, to an optimization method for the chip breaking performance of the chip breaker groove of an indexable tool. Background Art

[0002] In metal machining, the geometric structure of the tool chip breaker groove is complex and variable, and usually relies on the experience of workers for design. To overcome the limitations of empirical design, researchers have started to use computational methods, such as simulation and optimization algorithms, to guide the design of chip breaker groove parameters. These optimization processes usually need to consider multiple conflicting objectives simultaneously. For example, it is necessary to maximize the chip breaking effect (usually related to the chip curl radius and chip thickness) while minimizing the cutting force (related to energy consumption, tool life, and machining stability), which is a typical multi-objective optimization problem (MOP). Traditional optimization methods often transform the multi-objective problem into a single-objective problem for solution by assigning fixed weights to each objective (such as the linear weighted method). However, this static and preset weight assignment method represents a fixed compromise solution determined before the optimization search begins. This fixed preference may limit the exploration ability of the optimization algorithm for the entire feasible solution space (especially the Pareto optimal front). Especially when the objective function relationship is complex, non-linear, or the Pareto front presents a non-convex shape, it may cause the algorithm to converge prematurely to a sub-optimal solution and fail to discover a compromise design solution with better comprehensive performance that can better meet the actual requirements. Obviously, an optimization method that can explore the trade-off between different objectives more comprehensively and adaptively is crucial for improving the design level of the chip breaker groove.

[0003] Therefore, how to provide an optimization method for the chip breaking performance of the chip breaker groove of an indexable tool, which can optimize the groove structure of the tool chip breaker groove, enhance the chip breaking performance of the chip breaker groove, and improve the machining efficiency of metal cutting, has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] Aiming at the problems in the prior art that relying on experience to design the chip breaker groove of an indexable tool leads to blindness in design and the chip breaking performance cannot be guaranteed, and the limitation that using static weights to handle multi-objective optimization in the existing optimization methods may lead to the inability to fully explore the optimal compromise solution set, the present invention aims to provide an optimization method for the chip breaking performance of the chip breaker groove of an indexable tool based on dynamic weights. This method can systematically optimize the groove structure parameters of the tool chip breaker groove, and by dynamically adjusting the weights of different optimization objectives during the optimization process, it can more effectively balance the conflict between the chip breaking performance and the cutting force, thereby enhancing the comprehensive chip breaking performance of the tool and improving the machining efficiency and quality of metal cutting.

[0005] The technical solution provided by this application is as follows: This application provides an optimization method for the chip-breaking performance of the chip-breaking groove of an indexable tool, including the following steps: S1. Obtain the groove type parameters and machining process parameters of the indexable tool; S2. Determine the influence of each groove type parameter on the chip-breaking performance of the chip-breaking groove of the indexable tool through an orthogonal experiment, and preferably select the key groove type parameters that have a significant influence on the chip-breaking performance; S3. Take the key groove type parameters as the optimization target, and construct specific functional relationships between the key groove type parameters and the ratio of the cutting thickness to the chip curl radius, and between the key groove type parameters and the cutting force based on the response surface method; S4. Construct a hybrid genetic optimization algorithm, and determine the dynamic weight allocation value of the optimization target during the iterative calculation process of the hybrid genetic optimization algorithm; S5. Based on the dynamic weight allocation value, establish a fitness function of the optimization target, optimize the hybrid genetic optimization algorithm according to the specific functional relationship and the fitness function, and then obtain the optimal solution of the optimization target of the tool chip-breaking groove based on the hybrid genetic optimization algorithm; S6. Verify the optimization result according to the finite element simulation analysis.

[0006] Further, in a preferred embodiment of the present invention, in step S2, the orthogonal experiment is a cutting simulation experiment; the steps of determining the influence of each groove type parameter on the chip-breaking performance of the chip-breaking groove of the indexable tool through the orthogonal experiment include: Obtain each groove type parameter of the tool chip-breaking groove, including the rake angle , land width , arc radius , chip breaker angle , edge height , chip-breaking groove depth and main cutting edge groove width ; Set up a simulation experiment, take the various groove type parameters as experimental factors, and set several levels for each factor and input them into the simulation experiment; According to the cutting simulation object, preset the cutting processing parameters and then perform the cutting simulation experiment; Perform variance analysis on the results of the cutting simulation experiment to obtain the variance analysis results of the chip-breaking situation of the chip-breaking groove and the variance analysis results of the cutting force, and determine the influence of each groove type parameter on the chip-breaking performance of the tool chip-breaking groove according to the analysis results.

[0007] Further, in a preferred embodiment of the present invention, in step S2, the steps of preferably selecting the key groove type parameters that have a significant influence on the chip-breaking performance include: Preset the confidence level of the variance analysis and determine the critical F value corresponding to the confidence level; Obtain the confidence level of the groove type parameters of the tool chip-breaking groove; Compare the confidence level of the groove parameters with the confidence level of the analysis of variance. If the confidence level of the groove parameters is less than the confidence level of the analysis of variance, it is determined that the groove parameters have a significant effect on the chip breaking performance; otherwise, there is no effect. Obtain the F value of each groove parameter of the tool chip breaking groove. Determine the type of influence of the groove parameters on the chip breaking performance, and compare the F value of the groove parameters with the critical F value in different types of influence. If the F value of the groove parameter is greater than the critical F value, it is determined that the groove parameter has a significant effect on the chip breaking performance; otherwise, there is no effect.

[0008] Further, in a preferred embodiment of the present invention, in step S3, the response surface method includes experimental design and response surface fitting; wherein the experimental design uses a central composite design, and the response surface fitting uses a quadratic polynomial fitting.

[0009] Further, in a preferred embodiment of the present invention, in step S3, the steps of constructing a specific functional relationship include: Keep other groove parameters unchanged and adjust the key groove parameter. Determine the cutting parameters, input the key groove parameter as an independent variable into the finite element cutting simulation experiment, and obtain the simulation experiment results. Conduct a significance level analysis on the simulation experiment results, remove insignificant terms according to the experimental requirements, and then fit and derive the regression function between the key groove parameter and the ratio of the cutting thickness to the chip curl radius. Then, based on the same method, fit and derive the regression function between the key groove parameter and the cutting force.

[0010] Further, in a preferred embodiment of the present invention, in step S4, the steps of determining the dynamic weight allocation value of the optimization objective include: Determine the algorithm parameters: obtain the current iteration number of the optimization algorithm as the total number of iterations, and preset the frequency parameter ; Define the weight function: define a set of dynamically changing weight coefficients for the optimization objective 、 , as the dynamic weight of optimization objective 1, as the dynamic weight of optimization objective 2, and determine the periodic functional relationship between the dynamic weight and the iteration number ; Calculate the dynamic weight: in each iteration of the optimization algorithm, calculate the dynamic weight allocation value of the optimization objective through the periodic functional relationship, and the formula is as follows: ; Among them, is the current iteration number of the optimization algorithm.

[0011] Furthermore, in a preferred embodiment of the present invention, the hybrid genetic optimization algorithm is specifically the HGA-PSO algorithm.

[0012] Furthermore, in a preferred embodiment of the present invention, in step S5, the steps of establishing the fitness function of the optimization objective include: Obtain the specific functional relationship between the key groove parameters and the ratio of cutting thickness to chip curling radius , and the specific functional relationship between the key groove parameters and the cutting force , where is the rake angle, is the edge height, is the chip-breaking angle; According to the specific functional relationships and , respectively determine the normalized boundary values and of the corresponding optimization objective within the feasible region; Determine the ratio of cutting thickness to chip curling radius and the cutting force , and the dynamic weight distribution values and and at the current iteration number ; : ; Among them, is the solution vector containing the key groove parameters .

[0013] Furthermore, in a preferred embodiment of the present invention, in step S5, the steps of optimizing the hybrid genetic optimization algorithm include: Parameter setting: Set the algorithm parameters, including the population size pop_size, the total number of iterations gen, the internal iteration number j of PSO max , the PSO factors c1 and c2, the inertia weight w, the crossover probability P c and the mutation probability P m ; Population initialization: Randomly generate the positions and velocities of the initial population, and the main loop of iterative optimization ; Calculate fitness: In each iteration , use the corresponding dynamic weight assignment value and , for each individual in the population, calculate its fitness in the current iteration ; ; Sorting and selection: Adopt the selection operator of the genetic algorithm to sort and select all individuals in the population based on the fitness value; PSO update: Perform j max PSO iterations on the individuals in the population after sorting and selection. In each PSO iteration j, refer to the individual best position pbest and the global best position gbest to update the velocity and position of each particle, where gbest is selected based on the fitness value of the current iteration ; ; Crossover and mutation: For the population after PSO update, randomly combine them in pairs and perform crossover operations with probability P c , and then perform mutation operations on each individual in the entire population with probability P m ; Optimized output: Determine whether the termination condition is reached. If so, output the optimal solution; otherwise, continue the next iteration. When the number of iterations i reaches gen, output the individual with the best fitness in the current population as the optimal solution .

[0014] Furthermore, in a preferred embodiment of the present invention, in step S6, the steps of verifying the reliability of the algorithm optimization result through finite element analysis include: Establish a three-dimensional model of the optimized grooved tool and a three-dimensional cutting simulation model; Input the optimal solution of the chip-breaking groove optimization target into the three-dimensional cutting simulation model to obtain the cutting simulation result; Compare and analyze the cutting simulation result with the chip-breaking performance of the grooved tool before optimization to verify the optimization result.

[0015] An optimization method for the chip breaking performance of the chip breaking groove of an indexable tool includes the following steps: S1. Obtain the groove type parameters and machining process parameters of the indexable tool; S2. Determine the influence of each groove type parameter on the chip breaking performance of the chip breaking groove of the indexable tool through an orthogonal experiment, and preferably select the key groove type parameters that have a significant influence on the chip breaking performance; S3. Take the key groove type parameters as the optimization target, and construct specific functional relationships between the key groove type parameters and the ratio of the cutting thickness to the chip curl radius, and between the key groove type parameters and the cutting force based on the response surface method; S4. Construct a hybrid genetic optimization algorithm, and determine the dynamic weight distribution value of the optimization target during the iterative calculation process of the hybrid genetic optimization algorithm; S5. Based on the dynamic weight distribution value, establish a fitness function of the optimization target, and optimize the hybrid genetic optimization algorithm according to the specific functional relationship and the fitness function, and then obtain the optimal solution of the optimization target of the tool chip breaking groove based on the hybrid genetic optimization algorithm; S6. Verify the optimization result according to the finite element simulation analysis. The optimization method for the chip breaking performance of the chip breaking groove of the indexable tool uses the experimental analysis method and the numerical simulation analysis method to analyze the groove structure parameters, explores the structure parameters that have an obvious influence on the chip breaking performance, and carries out optimization on them to achieve the improvement of the chip breaking performance of the chip breaking groove; in the steps of the optimization method, first, it is necessary to obtain each groove type parameter of the tool, that is, the structure parameter; then through a simulation experiment, taking the groove type parameter as the characteristic input for cutting simulation, obtain the influence of each groove type parameter on the chip breaking performance of the chip breaking groove of the tool, and preferably select the structure parameter that has a key influence on the chip breaking performance from them, that is, obtain the key groove type parameter; then, based on the key groove type parameter preferably selected that has a significant influence on the chip breaking performance, construct specific functional relationships between the key groove type parameter and the chip thickness / chip curl radius and between the key groove type parameter and the cutting force respectively; then construct a hybrid genetic optimization algorithm, after determining the dynamic weight distribution value of the optimization target, establish a fitness function of the optimization target based on the dynamic weight distribution value, and then optimize the hybrid genetic optimization algorithm in combination with the specific functional relationship and the fitness function, and then obtain the optimal value of the optimization target of the chip breaking groove according to the hybrid genetic optimization algorithm; finally, use the finite element method, by substituting the optimal solution into the simulation experiment, compare the chip breaking performance of the chip breaking groove before and after optimization to verify the reliability of the algorithm optimization result, and complete the optimization operation of the chip breaking groove structure parameter after verification analysis.

[0016] Therefore, the technical solution involved in the present invention has the following beneficial effects: It can optimize the groove structure of the tool chip breaking groove, enhance the chip breaking performance of the chip breaking groove, and improve the machining efficiency of metal cutting; and the present invention starts from the actual engineering technical problems, and solves the engineering technical problems by determining the key parameters and optimizing the parameters. This design idea also has certain reference significance for the same or similar types of engineering technical problems. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the steps of the method for optimizing the chip breaking performance of the chip breaker of the indexable tool according to the embodiments of the present invention; Figure 2 It is a working principle diagram of the method for optimizing the chip breaking performance of the chip breaker of the indexable tool according to the embodiments of the present invention; Figure 3 It is a variance analysis result diagram of the chip breaking situation of the chip breaker according to the embodiments of the present invention; Figure 4 It is a working principle diagram of the HGA-PSO optimization algorithm according to the embodiments of the present invention; Figure 5 It is a flowchart of the steps of the HGA-PSO optimization algorithm according to the embodiments of the present invention; Figure 6 It is a schematic cross-sectional structure diagram of the optimized chip breaker of the tool according to the embodiments of the present invention, where Figure A-A is a schematic cross-sectional structure diagram of the A-A section of the chip breaker, Figure B-B is a schematic cross-sectional structure diagram of the B-B section of the chip breaker, Figure C-C is a schematic cross-sectional structure diagram of the C-C section of the chip breaker, and Figure D-D is a schematic cross-sectional structure diagram of the D-D section of the chip breaker; Figure 7 It is a comparison diagram of the chip breaking situations of the chip breakers of the tool before and after optimization according to the embodiments of the present invention; Figure 8 It is a comparison diagram of the cutting forces of the chip breakers of the tool before and after optimization according to the embodiments of the present invention; Figure 9 It is a chip morphology diagram of the optimized groove-shaped tool according to the embodiments of the present invention. Among them, Figure a is a chip morphology diagram of the optimized groove-shaped tool at a feed rate of 0.15 mm / r, Figure b is a chip morphology diagram of the optimized groove-shaped tool at a feed rate of 0.2 mm / r, Figure c is a chip morphology diagram of the optimized groove-shaped tool at a feed rate of 0.25 mm / r, Figure d is a chip morphology diagram of the optimized groove-shaped tool at a feed rate of 0.3 mm / r, Figure e is a chip morphology diagram of the optimized groove-shaped tool at a feed rate of 0.35 mm / r, and Figure f is a chip morphology diagram of the optimized groove-shaped tool at a feed rate of 0.4 mm / r. Detailed Embodiments

[0019] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of this application.

[0020] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly disposed on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0021] It should be understood that the orientation or positional relationships indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "first", "second", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to this application.

[0022] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, the meanings of "a plurality" and "several" are two or more, unless otherwise specifically defined.

[0023] It should be noted that the structures, proportions, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they do not have technical substantive meanings. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that this application can produce and the purposes that can be achieved, should still fall within the scope that the technical content disclosed in this application can cover.

[0024] Please as Figures 1 to 9As shown in the figure, the present application provides an optimization method for the chip breaking performance of the chip breaker groove of an indexable tool, including the following steps: S1. Obtain the groove type parameters and machining process parameters of the indexable tool; S2. Determine the influence of each groove type parameter on the chip breaking performance of the chip breaker groove of the indexable tool through an orthogonal experiment, and preferably select the key groove type parameters that have a significant influence on the chip breaking performance; S3. Take the key groove type parameters as the optimization target, and construct specific functional relationships between the key groove type parameters and the ratio of the cutting thickness to the chip curling radius, and between the key groove type parameters and the cutting force based on the response surface method; S4. Construct a hybrid genetic optimization algorithm, and determine the dynamic weight allocation value of the optimization target during the iterative calculation process of the hybrid genetic optimization algorithm; S5. Based on the dynamic weight allocation value, establish a fitness function of the optimization target, optimize the hybrid genetic optimization algorithm according to the specific functional relationship and the fitness function, and then obtain the optimal solution of the optimization target of the tool chip breaker groove based on the hybrid genetic optimization algorithm; S6. Verify the optimization result according to the finite element simulation analysis. The technical solution involved in the present invention can optimize the groove structure of the tool chip breaker groove, enhance the chip breaking performance of the chip breaker groove, and improve the machining efficiency of metal cutting.

[0025] The following specifically elaborates on the optimization method for the chip breaking performance of the chip breaker groove of the indexable tool disclosed in the present invention in combination with specific embodiments. The optimization method for the chip breaking performance of the chip breaker groove of the indexable tool specifically includes the following steps: S1. Obtain the groove type parameters and machining process parameters of the indexable tool.

[0026] In the embodiment of the present invention, step S1 is used to collect and define the geometric parameters of the turning tool chip breaker groove that need to be analyzed and optimized, and these parameters constitute the basic variables for subsequent experimental design and optimization. Among them, the groove type parameters include the rake angle , land width , arc radius , chip breaker angle , edge height , chip breaker groove depth , and main cutting edge groove width .

[0027] S2. Determine the influence of each groove type parameter on the chip breaking performance of the chip breaker groove of the indexable tool through an orthogonal experiment, and preferably select the key groove type parameters that have a significant influence on the chip breaking performance.

[0028] Specifically, in the specific embodiment of the present invention, the orthogonal experiment is a cutting simulation experiment.

[0029] Among them, in the orthogonal experiment, the chip breaker groove structure of the tool for which the groove type parameters are to be analyzed should have good chip evacuation performance, lower cutting resistance, and have a chip breaker groove.

[0030] Specifically, in the specific embodiments of the present invention, the steps of determining the influence of each chip groove parameter on the chip breaking performance of the tool chip groove through orthogonal experiments include: obtaining each chip groove parameter of the tool chip groove, such as the parameters defined in S1: rake angle , land width , arc radius , chip breaker angle , edge height , chip groove depth , and main cutting edge groove width ; setting up a simulation experiment, taking the above chip groove parameters as experimental factors, and setting several levels (for example, three levels) for each factor; according to the cutting simulation object, presetting cutting parameters and then performing the cutting simulation experiment; performing variance analysis on the results of the cutting simulation experiment to obtain the variance analysis results of the chip breaking situation of the chip groove and the variance analysis results of the cutting force, and determining the influence of each chip groove parameter on the chip breaking performance of the tool chip groove according to the analysis results.

[0031] Among them, in the embodiments of the present invention, step S2 is used to determine the influence of each chip groove parameter on the chip breaking performance of the tool chip groove through a simulation experiment; according to the chip groove parameters, 7 parameter factors are designed for the simulation experiment: rake angle , land width , arc radius , chip breaker angle , edge height , chip groove depth , and main cutting edge groove width , and determining the simulation conditions; selecting a cutting simulation object (for example, 304 stainless steel outer circle) and turning processing parameters (for example: cutting speed (linear speed) v c = 200 m / min, feed rate f = 0.2 mm / r, cutting depth a p = 2 mm) according to the actual application, and then performing a simulation experiment to obtain the experimental results; by performing variance analysis on the experimental results, obtaining the variance analysis (ANOVA) results of the chip breaking situation and the variance analysis results of the cutting force, and thus judging the influence of each parameter factor on the chip breaking performance of the chip groove. In the embodiments of the present invention, the main response indexes for analysis are the chip breaking ability (which can be measured by the ratio of the chip thickness to the chip curl radius Q , and a smaller Q value usually indicates better curling and breaking) and the cutting force F , obtaining a variance analysis result table about Q and F , as shown in Figure 3 .

[0032] Specifically, in a specific embodiment of the present invention, in step S2, the steps of selecting key groove profile parameters that have a significant impact on the chip breaking performance include: presetting the confidence level of analysis of variance and determining the critical F value corresponding to the confidence level; obtaining the confidence level of the tool chip breaking groove profile parameters; comparing the confidence level of the groove profile parameters with the confidence level of analysis of variance. If the confidence level of the groove profile parameters is less than the confidence level of analysis of variance, it is determined that the groove profile parameters have a significant impact on the chip breaking performance, otherwise there is no impact; obtaining the F value of each groove profile parameter of the tool chip breaking groove; determining the type of influence of the groove profile parameters on the chip breaking performance, and comparing the F value of the groove profile parameters with the critical F value in different types of influence; if the F value of the groove profile parameters is greater than the critical F value, it is determined that the groove profile parameters have a significant impact on the chip breaking performance, otherwise there is no impact.

[0033] Among them, after determining the groove profile parameters that have an impact on the chip breaking performance, the next step is to select the groove profile parameters that have a significant impact on the chip breaking performance from these groove profile parameters, that is, to determine the key groove profile parameters according to the analysis of variance results. The steps are as follows: first, preset the confidence level of analysis of variance (for example, the significance level α = 0.05) and determine the corresponding critical F value; compare the P value (confidence level) of each groove profile parameter with the preset significance level α: if the P value is less than α, it is determined that the parameter has a significant impact on the response index ( Q or F ). At the same time, compare the F value of the parameter with the critical F value. If the F value is greater than the critical F value, it also indicates a significant impact. Considering comprehensively the influence degree of each parameter on Q and F (for example, through the magnitude of the F value or the contribution rate), select those parameters that have a significant impact on one or both targets as the key groove profile parameters. For example, according to Figure 3 the example analysis (assuming that P value < 0.05 and F value > 4.38 are significant), the rake angle A and the chip breaker angle G have a significant impact on Q and F ; the depth of the chip breaking groove has a significant impact on Q but has no significant impact on F; while the edge height may be more critical than the depth of the groove in terms of the overall chip breaking performance. Therefore, in this embodiment, the rake angle A, the edge height h, and the chip breaker angle G are selected as the key groove profile parameters for subsequent optimization.

[0034] S3. Taking the key groove profile parameters as the optimization objectives, based on the response surface method, construct specific functional relationships between the key groove profile parameters and the ratio of the cutting thickness to the chip curl radius, and between the key groove profile parameters and the cutting force.

[0035] Specifically, in a specific embodiment of the present invention, in step S3, the response surface method includes experimental design and response surface fitting; among them, the experimental design uses a central composite design, and the response surface fitting uses a quadratic polynomial fitting.

[0036] Specifically, in a specific embodiment of the present invention, in step S3, the steps of constructing a specific functional relationship include: keeping other groove parameters unchanged, adjusting the key groove parameter; determining cutting parameters, inputting the key groove parameter as an independent variable into a finite element cutting simulation experiment, and obtaining the simulation experiment results; performing a significance level analysis on the simulation experiment results, removing insignificant terms according to experimental requirements, and then fitting and deriving a regression function between the key groove parameter and the ratio of cutting thickness to chip curl radius; then, based on the same method, fitting and deriving a regression function between the key groove parameter and cutting force.

[0037] In an embodiment of the present invention, after selecting key groove parameters (rake angle A, edge height h, chip breaker angle G), it is then necessary to establish a quantitative relationship model between these parameters and the optimization objectives ( Q and F ). In step S3, the response surface method (Response Surface Methodology, RSM) is used for modeling, and the specific steps are as follows: Experimental design: Using central composite design (Central Composite Design, CCD), taking the 3 selected key parameters as factors, setting 5 levels for each factor (including the center point and axial points), and keeping other non-key parameters at fixed values (for example: limiting the contact length b n =0.2 mm, arc radius r = 0.8 mm, chip breaker groove depth H = 0.28 mm, main cutting edge groove width W = 1.65 mm); Simulation experiment: According to the CCD design table, adjust the key parameter combinations, and conduct a series of finite element turning simulation experiments, with the cutting parameters being the same as those in S2 (v c =200 m / min, f = 0.2 mm / r, a p =2 mm), and recording the Q and F values obtained from each simulation; Response surface fitting: Import the simulation results into statistical analysis software (such as Design-Expert), use a quadratic polynomial model to fit the data, perform a significance analysis (ANOVA) on the fitting model, and remove insignificant terms in the model (for example, based on P value > 0.1 or adjusted according to the goodness of fit of the model) to obtain the final regression model. Example of the regression function relationship between the key groove parameter and the ratio of chip thickness to chip curl radius Q: ; Similarly, example of the regression function relationship between the key groove parameter and cutting force F: .

[0038] S4. Construct a hybrid genetic optimization algorithm, and determine the dynamic weight allocation value of the optimization objective during the iterative calculation process of the hybrid genetic optimization algorithm.

[0039] In step S4, the specific dynamic weight allocation method is Dynamic Weight Aggregation (DWA), which periodically adjusts the weights according to the current iteration number t of the optimization algorithm.

[0040] Specifically, in a specific embodiment of the present invention, in step S4, the steps of determining the dynamic weight allocation value of the optimization objective include: determining algorithm parameters: obtaining the current iteration number of the optimization algorithm as the total number of iterations, and presetting a frequency parameter ; defining a weight function: defining a set of dynamically changing weight coefficients for the optimization objective 、 , as the dynamic weight of optimization objective 1, as the dynamic weight of optimization objective 2, and determining the periodic functional relationship between the dynamic weight and the iteration number ; calculating the dynamic weight: in each iteration of the optimization algorithm, calculate the dynamic weight allocation value of the optimization objective through the periodic functional relationship, and the formula is as follows: ; where is the current iteration number of the optimization algorithm.

[0041] Specifically, in a specific embodiment of the present invention, the hybrid genetic optimization algorithm is specifically the HGA-PSO algorithm.

[0042] Among them, in an embodiment of the present invention, dynamic weight aggregation DWA is used for dynamic weight allocation, and the specific implementation steps are as follows: Defining a weight function: Assigning weights and to two optimization objectives Q (assuming minimization is required) and F (minimization is required), which are functions of the iteration number t. Adopting a periodic change pattern based on the sine function: , where: t is the current iteration number of the optimization algorithm (HGA-PSO), is the total number of iterations; is a preset frequency parameter that controls the number of cycles of weight change in a complete optimization run. For example, if the total number of iterations gen = 100, setting = 50 means that the weights and It will experience two complete change cycles from 0 to 1 and back to 0 (or vice versa) throughout the optimization process. The selection of Fperiod should ensure that the algorithm has sufficient opportunities to search under different weight preferences, but not too frequently to disrupt convergence.

[0043] Calculate the dynamic weights: In each iteration t of the optimization algorithm, calculate the current weight values according to the above formula and . In this step, by periodically changing the weights, the search focus of the optimization algorithm will switch back and forth between minimizing Q and minimizing F. When is close to 1, the algorithm mainly focuses on improving the chip breaking ability; when is close to 1, the algorithm mainly focuses on reducing the cutting force. This systematic focus shift helps the algorithm explore different regions of the Pareto front, especially for non-convex or complex-shaped fronts, and can discover more diverse and potentially better trade-off solutions than the fixed-weight method.

[0044] S5. Based on the dynamic weight assignment values, establish the fitness function of the optimization objective, and optimize the hybrid genetic optimization algorithm according to the specific functional relationship and the fitness function. Subsequently, obtain the optimal solution of the tool chip breaker optimization objective based on the hybrid genetic optimization algorithm.

[0045] Specifically, in a specific embodiment of the present invention, in step S5, the steps of establishing the fitness function of the optimization objective include: obtaining the specific functional relationship between the key groove parameters and the ratio of cutting thickness / chip curl radius , and the specific functional relationship between the key groove parameters and the cutting force , where is the rake angle, is the edge height, is the chip-back angle; according to the specific functional relationships and , respectively determine the normalized boundary values of the corresponding optimization objectives within the feasible region and ; determine the ratio of cutting thickness / chip curl radius and the cutting force at the current iteration number and the dynamic weight assignment values and ; perform a weighted sum of the normalized boundary values of the optimization objective and their corresponding dynamic weight assignment values to obtain the fitness function of the optimization objective : ; where, is a function containing the key groove parameters The solution vector.

[0046] In the embodiments of the present invention, the fitness function is the weighted sum of the normalized values of each optimization objective and their corresponding dynamic weight allocation values, and a Hybrid Genetic Algorithm - Particle Swarm Optimization (HGA - PSO) is used to solve this multi - objective optimization problem with dynamic weights.

[0047] First, establish the fitness function: The goal is to find a set of key groove parameters x = (A, h, G) such that both Q(x) and F(x) are as small as possible. Use the dynamic weights obtained in S4 and to transform the multi - objective problem into a single - objective fitness function that varies with the iteration number t as shown in the formula above. The smaller the fitness value, the better the solution. Among them, and are the regression functions obtained in S3. , is Q and F the estimated maximum and minimum values within the feasible domain, which are used for normalization to ensure that the two objectives are in a similar scale range in fitness calculation. For example, determined according to the RSM experimental results in S3, we get , and , . In this step, the normalization process is particularly important for the dynamic weight strategy, avoiding the problem that the effect of weight adjustment is masked due to the too large difference in the value ranges of the objective functions.

[0048] Specifically, in the specific embodiments of the present invention, in step S5, optimizing the hybrid genetic optimization algorithm includes: the selection, crossover, and mutation operations of the genetic algorithm and the position and velocity update mechanisms of the particle swarm algorithm. The specific steps include: Parameter setting: Set algorithm parameters, such as the population size pop_size = 50, the total number of iterations gen = 100, the number of internal iterations j of the PSO max = 20, the PSO factors c1 = 0.5, c2 = 0.5, the inertia weight w = 0.8, w is the inertia weight inside the PSO, the crossover probability P c = 0.8, the mutation probability P m= 0.05. Boundary constraints: Set reasonable value ranges for the key groove parameters A, h, and G. Encoding: Encode real - valued parameters as needed (such as binary encoding or directly using real - valued encoding). Initialization: Randomly generate the positions x and velocities v of the initial population (individuals / particles). Iterative optimization main loop i = 1 to gen: a. Calculate fitness: For each individual in the population, calculate its fitness value at the current iteration t = i , and use the corresponding dynamic weight and ; b. Sorting and selection (GA part): Sort all individuals in ascending order of fitness value, remove the 2 / 5 individuals with the worst fitness, randomly copy 2 / 3 of the remaining individuals and add them to the population to keep the population size; c. PSO update (PSO part): Perform j max times of PSO iterations on the individuals (regarded as particles) in the current population. In each PSO iteration j, update the velocity and position of each particle, referring to its personal best position pbest and the global best position gbest (gbest can be selected based on the fitness value Fit(x, t) at the current iteration t = i); d. Crossover and mutation (GA part): For the population after PSO update, randomly pair them up and perform crossover operations with probability P c , and then perform mutation operations on each individual in the entire population with probability P m ; Finally, output: When the iteration number i reaches gen, output the individual with the best fitness in the current population as the found optimal solution .

[0049] Result example: Suppose the optimal solution obtained through the above optimization process is: rake angle A opt = 15°, edge height h opt = 0.1266 mm, chip - breaker angle G opt = 31.87°. Substitute these values into the regression function in S3 to calculate the corresponding predicted performance indicators: , .

[0050] In step S5, the combination of the hybrid characteristics of the HGA - PSO algorithm and the dynamic weight enables the algorithm to not only utilize the global search ability of GA to explore different optimization regions caused by weight changes but also utilize the local search ability of PSO to quickly converge within the region preferred by the current weight, thus hopefully finding a high - quality compromise solution more effectively.

[0051] S6. Verify the optimization results according to the finite - element simulation analysis.

[0052] In the embodiment of the present invention, step S6 verifies the improvement of the chip breaking performance of the tool chip breaking groove before and after the optimization of the key groove type parameters; the optimization of the groove type parameters of the tool chip breaking groove in the present invention is mainly aimed at the tool groove type cross-section parameters perpendicular to the main cutting edge and at a distance of 2 mm from the tool tip. From the above optimization results, the final tool groove type parameter values are: chip breaking groove depth H = 0.28 mm, main cutting edge groove width W = 1.65 mm, arc radius r = 0.8 mm, restricted contact length bn = 0.2 mm, rake angle A = 15°, edge height h = 0.1266 mm, and chip breaker angle G = 31.87°. As Figure 6 shown, from Figure 6 it can be clearly seen that the structure after optimization and the structure before optimization have the same combination of chip breaking groove units, and only the cross-section groove type parameters of the tool chip breaking groove are adjusted, thereby improving the chip breaking performance of the tool chip breaking groove.

[0053] Specifically, in a specific embodiment of the present invention, in step S6, the steps of verifying the reliability of the algorithm optimization results through finite element analysis include: establishing a three-dimensional model of the grooved tool after optimization and a three-dimensional cutting simulation model; inputting the optimal solution of the chip breaking groove optimization target into the three-dimensional cutting simulation model to obtain the cutting simulation results; and comparing and analyzing the cutting simulation results with the chip breaking performance of the grooved tool before optimization to verify the optimization results.

[0054] Among them, in the embodiment of the present invention, the reliability of the algorithm optimization results is verified through simulation experiments, and is specifically determined by comparing the chip breaking performance of the tool chip breaking groove before and after optimization; as Figure 7 shown, Figure 7 shown is a comparison diagram of the chip breaking conditions (chip thickness / chip curl radius) of the grooved tool before and after optimization at a cutting depth a p = 2 mm. It can be seen from the figure that the chip breaking conditions (chip thickness / chip curl radius) of the grooved tool after optimization have been significantly improved compared with those before optimization. Among them, the maximum improvement is 170.04% at a feed rate of 0.15 mm / r; the minimum improvement is 30.60% at a feed rate of 0.4 mm / r. Therefore, from the perspective of chip breaking conditions, the optimization results of the present invention are better and can significantly improve the chip breaking performance of the tool chip breaking groove.

[0055] Specifically, in a specific embodiment of the present invention, step S6 also verifies the cutting force of the grooved tool before and after the optimization of the groove type parameters. As Figure 8 shown, Figure 8 shown is the cutting force of the grooved tool before and after optimization at a cutting depth a pComparison diagram when = 2mm. It can be seen from the figure that the cutting force of the optimized grooved tool has decreased significantly compared with that before optimization. Among them, when the feed rate is 0.15mm / r, it is reduced by 12.59%; when the feed rate is 0.3mm / r, it is reduced by 2.57%, but when the feed is 0.4mm / r, it is increased by 4.73%. This is the result of the calculation error of the finite element analysis software itself. Due to the complexity of the cutting process, there is currently no clear mathematical model to quantitatively describe the cutting process, and approximate fitting methods are usually used for simulation. Therefore, calculation errors will occur in the cutting process, resulting in a certain deviation between the results and the actual situation. Generally speaking, from the aspect of cutting force, the optimization result of the present invention is good, which can significantly improve the chip breaking performance of the tool chip breaker. And, as Figure 9 shown, Figure 9 is the chip morphology diagram of the optimized grooved tool. It can be seen from the figure that for the chip morphology formed by the simulation cutting of the optimized grooved tool, as the feed rate increases, the curling radius of the chip during cutting will decrease, but at the same time the thickness will also increase. This phenomenon conforms to the change law of the actual cutting morphology, so the optimization method of the present invention is reasonable.

[0056] In summary, an optimization method for the chip breaking performance of the chip breaker groove of an indexable tool according to an embodiment of the present invention solves the problem that in the current structural design of the chip breaker groove of an indexable tool, it still relies on the experience of workers for design, which has a certain blindness, and the chip breaking performance of the designed chip breaker groove cannot be guaranteed, and it cannot meet the requirements of metal cutting processing. The optimization method for the chip breaking performance of the chip breaker groove of an indexable tool uses experimental analysis methods and numerical simulation analysis methods to analyze the groove structure parameters, explores the structure parameters that have an obvious impact on the chip breaking performance, and conducts optimization on them to achieve the improvement of the chip breaking performance of the chip breaker groove; in the steps of the optimization method, first, it is necessary to obtain various groove parameters of the tool, that is, structural parameters; then through a simulation experiment, taking the groove parameters as feature inputs for cutting simulation, obtain the influence of each groove parameter on the chip breaking performance of the tool chip breaker groove, and preferably select the structural parameters that have a key impact on the chip breaking performance, that is, obtain the key groove parameters; then, according to the key groove parameters preferably selected that have a significant impact on the chip breaking performance, respectively establish specific functional relationships between the key groove parameters and the chip thickness / chip curling radius and between the key groove parameters and the cutting force; then construct a hybrid genetic optimization algorithm, after determining the dynamic weight allocation value of the optimization target, establish a fitness function of the optimization target based on the dynamic weight allocation value, and then optimize the hybrid genetic optimization algorithm in combination with the specific functional relationship and the fitness function, and then obtain the optimal value of the chip breaker groove optimization target according to the hybrid genetic optimization algorithm; finally, use the finite element method, by substituting the optimal solution into the simulation experiment, compare the chip breaking performance of the chip breaker groove before and after optimization to verify the reliability of the algorithm optimization result, and complete the optimization operation of the chip breaker groove structure parameters after verification and analysis. Therefore, the technical solution of the present invention, compared with the prior art, can optimize the groove structure of the tool chip breaker groove, enhance the chip breaking performance of the chip breaker groove, and improve the processing efficiency of metal cutting. And the present invention starts from the actual engineering technical problems, and solves the engineering technical problems by determining the key parameters and optimizing the parameters. This design idea also has certain reference significance for the same or similar types of engineering technical problems.

[0057] The foregoing description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the invention is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimization method for the chip breaking performance of the chip breaking groove of an indexable tool, characterized in that, It includes the following steps: S1. Obtain the insert tool groove type parameters and machining process parameters; S2. Determine the influence of each groove type parameter on the chip breaking performance of the insert tool chip breaker groove through orthogonal experiments, and optimize and select the key groove type parameters that have a significant influence on the chip breaking performance; S3. Taking the key groove type parameters as the optimization target, construct specific functional relationships between the key groove type parameters and the ratio of cutting thickness to chip curl radius, and between the key groove type parameters and cutting force based on the response surface method; S4. Construct a hybrid genetic optimization algorithm, and determine the dynamic weight distribution value of the optimization target during the iterative calculation process of the hybrid genetic optimization algorithm; S5. Based on the dynamic weight distribution value, establish the fitness function of the optimization target, optimize the hybrid genetic optimization algorithm according to the specific functional relationship and the fitness function, and then obtain the optimal solution of the tool chip breaker groove optimization target based on the hybrid genetic optimization algorithm; S6. Verify the optimization result through finite element simulation analysis.

2. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 1, characterized in that In step S2, the orthogonal experiment is a cutting simulation experiment; the steps of determining the influence of each groove type parameter on the chip breaking performance of the insert tool chip breaker groove through orthogonal experiments include: Obtain each groove type parameter of the tool chip breaker groove; Set up a simulation experiment, take the respective groove type parameters as experimental factors, and set several levels for each factor and input them into the simulation experiment; According to the cutting simulation object, preset the cutting parameters and then perform the cutting simulation experiment; Conduct variance analysis on the results of the cutting simulation experiment to obtain the variance analysis results of the chip breaking situation of the chip breaker groove and the variance analysis results of the cutting force, and determine the influence of each groove type parameter on the chip breaking performance of the tool chip breaker groove according to the analysis results.

3. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 1, characterized in that, In step S2, the steps of optimizing and selecting the key groove type parameters that have a significant influence on the chip breaking performance include: Preset the confidence level of variance analysis and determine the critical F value corresponding to the confidence level; Obtain the confidence level of the tool chip breaker groove type parameters; Compare the groove type parameter confidence level with the confidence level of variance analysis. If the groove type parameter confidence level is less than the confidence level of variance analysis, it is determined that the groove type parameter has a significant influence on the chip breaking performance, otherwise it has no influence; Obtain the F value of each groove type parameter of the tool chip breaker groove; Determine the type of influence of the groove type parameter on the chip breaking performance, and compare the F value of the groove type parameter with the critical F value in different types of influence; If the F value of the groove type parameter is greater than the critical F value, it is determined that the groove type parameter has a significant influence on the chip breaking performance, otherwise it has no influence.

4. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 1, wherein In step S3, the response surface method includes experimental design and response surface fitting; among them, the experimental design adopts central composite design, and the response surface fitting adopts quadratic polynomial fitting.

5. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 4, characterized in that, In step S3, the steps of constructing a specific functional relationship include: Keep other groove type parameters unchanged and adjust the key groove type parameters; Determine the cutting parameters, input the key groove type parameters as independent variables into the finite element cutting simulation experiment, and obtain the simulation experiment results; Conduct significance level analysis on the simulation experiment results, remove the insignificant terms according to the experimental requirements, and then fit and derive the regression function between the key groove type parameters and the ratio of cutting thickness to chip curl radius. Then, based on the same method, a regression function between the key groove parameters and the cutting force is derived by fitting.

6. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 1, characterized in that, In step S4, the steps of determining the dynamic weight allocation value of the optimization objective include: Determine algorithm parameters: Obtain the current iteration number of the optimization algorithm as the total number of iterations, and preset the frequency parameter ; Define a weight function: Define a set of dynamically changing weight coefficients for the optimization objective , , be the dynamic weight for optimization objective 1, be the dynamic weight for optimization objective 2, and determine the periodic functional relationship between the dynamic weights and the number of iterations ; Calculate the dynamic weight: In each iteration of the optimization algorithm calculate the dynamic weight assignment value of the optimization objective according to the periodic function relationship, and the formula is as follows: ； Among them, is the current iteration number of the optimization algorithm.

7. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 6, characterized in that, The hybrid genetic optimization algorithm is specifically the HGA-PSO algorithm.

8. The optimization method for the chip breaking performance of the indexable tool chip breaker according to claim 7, characterized in that In step S5, the steps of establishing the fitness function of the optimization objective include: Obtain the specific functional relationship between the key groove parameters and the ratio of the cutting thickness to the chip curling radius , and the specific functional relationship between the key groove parameters and the cutting force , where is the rake angle, is the edge height, is the chip breaker angle; According to the specific functional relationship and , respectively determine the normalized boundary values of the corresponding optimization objectives within the feasible region and ; Determine the cutting thickness / chip curl radius ratio and the cutting force , at the current iteration number for the dynamic weight assignment value and ; Perform a weighted sum of the normalized boundary value of the optimization objective and its corresponding dynamic weight assignment value to obtain the fitness function of the optimization objective : ； Among them, is the solution vector containing key groove parameters .

9. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 8, characterized in that, In step S5, the steps of optimizing the hybrid genetic optimization algorithm include: Parameter settings: Set algorithm parameters, including population size pop_size, total number of iterations gen, number of internal iterations j of PSO max , PSO factors c1 and c2, inertia weight w, crossover probability P c , and mutation probability P m ; Population initialization: Randomly generate the positions of the initial population and velocities , iterative optimization main loop ; Calculate fitness: In each iteration among them, use the corresponding dynamic weight assignment value and , for each individual in the population, calculate its fitness in the current iteration ; Sorting and selection: Using the selection operator of the genetic algorithm, all individuals in the population are sorted and selected based on the fitness value; PSO Update: Perform j max PSO iterations on the sorted and selected population individuals. In each PSO iteration j, refer to the individual best position pbest and the global best position gbest to update the velocity and position of each particle, where gbest is based on the fitness value of the current iteration Select; Crossover and Mutation: For the population updated by PSO, randomly pair them up in twos and perform crossover operation with probability P c Then, for each individual in the whole population, perform mutation operation with probability P m ; Optimized output: Determine whether the termination condition is reached. If so, output the optimal solution; otherwise, continue with the next iteration. When the iteration count i reaches gen, output the individual with the best fitness in the current population as the optimal solution .

10. The optimization method for the chip breaking performance of the indexable tool chip breaker groove according to claim 9, characterized in that, In step S6, the steps of verifying the reliability of the algorithm optimization result through finite element analysis include: Establish a three-dimensional model of the optimized groove tool and a three-dimensional cutting simulation model; Input the optimal solution of the chip-breaking groove optimization objective into the three-dimensional cutting simulation model to obtain the cutting simulation result; Compare and analyze the cutting simulation result with the chip-breaking performance of the groove tool before optimization to verify the optimization result.

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