Simulation design method of micro-texture cutter for cutting CFRP composite layer

Abstract

The invention belongs to the field of simulation design, and discloses a simulation design method of a micro-texture tool for cutting a CFRP composite layer, which can screen out a plurality of optimized tool parameters which have obvious influence on the cutting performance of the tool from a plurality of mutually-shadowed parameters. The method comprises the following steps: firstly, establishing a three-phase cutting finite element simulation model based on an initial microstructure tool, a predetermined fiber phase, a predetermined matrix phase and a corresponding under-plane damage distance; then, a plurality of cutter shape parameters are selected; thirdly, taking a preset number of feature data points for each cutter shape parameter to obtain a corresponding under-plane damage distance, and performing variance analysis to screen out a plurality of screened cutter parameters from the plurality of cutter shape parameters; thirdly, establishing a response fitting device model, and screening out a plurality of optimized cutter parameters from the screened cutter parameters through the response fitting device model; and finally, the micro-texture tool is designed according to the multiple optimized tool parameters.

Description

Technical Field

[0001] The invention belongs to the field of simulation design, and in particular relates to a simulation design method for a micro-texture tool for cutting a CFRP composite layer. Background Art

[0002] CFRP (Carbon Fiber Reinforced Polymer, the same below) is a two-phase composite material with high specific strength and high specific modulus carbon fiber as the reinforcement phase and anti-fatigue and corrosion-resistant resin as the matrix phase. It is widely used in various fields such as aerospace, military, and medical.

[0003] Carbon fiber reinforced composite materials are typical difficult-to-process materials due to their high hardness, high strength and poor thermal conductivity. The surface quality of the workpiece after processing is poor and the tool wear is severe. In order to better cut, a micro-texture is designed on the tool head to improve the cutting performance of the tool head on the CFRP composite layer, and better results have been obtained in the specific implementation.

[0004] However, in this way, when the tool parameters (tool head rake angle, tool head back angle and tool head radius) remain unchanged, the parameters of the microtexture (the distance of the texture from the tool tip, the spacing between adjacent microstructures of the texture, the width of the microstructure of the texture and the depth of the microstructure of the texture) have an additional impact on the cutting performance. Therefore, under the condition of many related and mutually influential parameters, how to select an optimized parameter combination of a tool to achieve the optimal design of the tool is an urgent problem to be solved. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a simulation design method for a micro-texture tool for cutting CFRP composite layers, which can screen out multiple optimized tool parameters that have a significant impact on the tool cutting performance from a number of mutually shadowing tool head and micro-texture parameters, thereby making the micro-texture tool based on the optimized tool parameters have excellent cutting performance for the CFRP composite layer.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A micro-texture tool simulation design method for cutting a CFRP composite layer, wherein the CFRP composite layer has a predetermined fiber orientation angle, the carbon fiber of the CFRP composite layer is used as a predetermined fiber phase, and the matrix of the CFRP composite layer is used as a predetermined matrix phase, characterized in that it comprises the following steps:

[0008] Step S1: establishing a three-phase cutting finite element simulation model based on an initial micro-textured tool, a predetermined fiber phase, a predetermined matrix phase, and a corresponding subsurface damage distance;

[0009] Step S2: selecting multiple tool shape parameters of all initial micro-textured tools that vary in correlation with the subsurface damage distance;

[0010] Step S3: taking a predetermined number of characteristic data points for each tool shape parameter, simulating all the characteristic data points through the three-phase cutting finite element simulation model to obtain the corresponding subsurface damage distance, and performing variance analysis to select multiple selected tool parameters from the multiple tool shape parameters;

[0011] Step S4: establishing a response fitter model based on a plurality of screened tool parameters and corresponding subsurface damage distances, and screening out a plurality of optimized tool parameters from the screened tool parameters through the response fitter model;

[0012] Step S5: designing a micro-texture tool according to a plurality of optimized tool parameters.

[0013] Preferably, in step S1, after establishing the three-phase cutting finite element simulation model, the actual process parameters recorded in the actual cutting process of the CFRP composite layer by the initial micro-texture tool and the simulation process parameters obtained during the simulation process of the three-phase cutting finite element simulation model are compared and verified until the actual process parameters are the same as the corresponding simulation process parameters.

[0014] Preferably, in step S2, the tool shape parameters include the tool head rake angle, tool head clearance angle, tool head radius, the shortest distance between the micro texture and the tool tip, the spacing between adjacent micro texture structures, the width of a single micro texture structure and the depth of a single micro texture structure.

[0015] Preferably, step S3 includes the following sub-steps:

[0016] Step S3-1: performing multiple clustering of all characteristic parameter points with each characteristic data point, wherein the multiple clusterings include different combinations of all characteristic parameter points, thereby forming multiple preliminary screening simulation parameter data groups, and obtaining corresponding subsurface damage distances based on the preliminary screening simulation parameter data groups;

[0017] Step S3-2: Perform variance analysis on multiple subsurface damage distances corresponding to the same tool shape feature parameter, and select tool shape feature parameters with significant subsurface damage distance as screened tool parameters.

[0018] Furthermore, two characteristic data points are taken for each tool shape parameter, and the two characteristic data points correspond to the end points of the value range of the tool shape parameter respectively.

[0019] Preferably, in step S4, the response fitter model is verified by the fit verification R2, and finally a plurality of optimized tool parameters are obtained.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. Because the micro-texture tool simulation design method for cutting CFRP composite layers of the present invention firstly establishes a three-phase cutting finite element simulation model based on the initial micro-texture tool, the predetermined fiber phase, the predetermined matrix phase and the corresponding subsurface damage distance; then, selects multiple tool shape parameters; then, takes a predetermined number of characteristic data points for each tool shape parameter to obtain the corresponding subsurface damage distance, and performs variance analysis to screen out multiple screened tool parameters from the multiple tool shape parameters; then, establishes a response fitter model, and screens out multiple optimized tool parameters from the screened tool parameters through the response fitter model; finally, designs a micro-texture tool based on the multiple optimized tool parameters. Therefore, the present invention can screen out multiple optimized tool parameters that have a significant impact on the tool cutting performance from many mutually shadowed tool heads and micro-texture parameters through the post-aggregation variance analysis and the two parameter screening of the response surface method, so that the micro-texture tool based on the optimized tool parameters has excellent cutting performance for the CFRP composite layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the cutting posture of a micro-texture tool according to an embodiment of the present invention;

[0023] Figure 2 Schematic diagram of the cutting process of the micro-textured tool according to an embodiment of the present invention

[0024] Figure 3 Schematic diagram of the steps of a method for simulating and designing a micro-texture tool for cutting a CFRP composite layer according to an embodiment of the present invention.

[0025] In the figure: K, tool head, α, tool head back angle, β, tool head rake angle, R, tool head radius, T, texture margin, W, texture width, S, texture adjacent distance, D, texture depth, Y, subsurface damage distance, E, cutting chips. DETAILED DESCRIPTION

[0026] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following examples and drawings specifically illustrate the micro-texture tool simulation design method for cutting CFRP composite layers of the present invention. It should be noted that the description of these implementation methods is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0027] The CFRP composite layer in this embodiment has a predetermined fiber orientation angle, the carbon fiber of the CFRP composite layer is used as a predetermined fiber phase, and the matrix of the CFRP composite layer is used as a predetermined matrix phase. Specifically, the fiber orientation angle is the orientation angle of the carbon fiber in the CFRP composite layer.

[0028] The spatial position of the micro-textured tool relative to the CFRP composite layer in this embodiment is as follows: Figure 1As shown, the tip of the tool head K cuts the CFRP composite layer through a feed motion, and the tip of the tool head K is a cutting edge formed by the intersection of the front tool face (not shown in the figure) and the back tool face (not shown in the figure), the acute angle between the front tool face and the vertical plane is taken as the tool head front angle β, and the acute angle between the back tool face and the surface of the CFRP composite layer (equivalent to the horizontal Mina) is taken as the tool head back angle α, and the tool tip is rounded at a microscopic level, that is, a tool head radius R is formed, and the microtexture is located on the front tool face, presenting a plurality of parallel straight groove microstructures, and all are parallel to the cutting edge, for each straight groove microstructure, its width is taken as the texture width W, its depth is taken as the texture depth D, the distance between two adjacent straight groove microtextures is taken as the texture proximity S, and the closest distance between the microtexture and the tool tip is taken as the texture margin T.

[0029] When the micro-texture tool in this embodiment cuts the CFRP composite layer, Figure 2 As shown, the cutter head K forms a cutting action by horizontally moving on the surface of the CFRP composite layer. While cutting off the cutting chips E on the CFRP composite layer, a structural fracture phenomenon occurs in the area near the surface of the CFRP composite layer. The vertical distance below the surface involved in the fracture structure is called the subsurface damage distance.

[0030] like Figure 3 As shown, the micro-texture tool simulation design method S100 for cutting a CFRP composite layer includes the following steps:

[0031] Step S1: establishing a three-phase cutting finite element simulation model based on an initial micro-textured tool, a predetermined fiber phase, a predetermined matrix phase, and a corresponding subsurface damage distance.

[0032] After the three-phase cutting finite element simulation model is established, the actual process parameters recorded in the actual cutting process of the CFRP composite layer by the initial micro-texture tool and the simulation process parameters obtained in the simulation process of the three-phase cutting finite element simulation model are compared and verified until the actual process parameters are the same as the corresponding simulation process parameters. Specifically, the initial micro-texture tool is an actual micro-texture tool, and the CFRP composite layer composed of a predetermined fiber phase and a predetermined matrix is ​​also an actual CFRP composite layer. Based on the micro-texture tool and the CFRP composite layer, a three-phase cutting finite element simulation model is established in relevant simulation software, and various simulation parameter data of the CFRP composite layer obtained after simulation and various simulation parameter data of the micro-texture tool itself are compared and verified with various actual parameter data measured by relevant measuring devices after the CFRP composite layer is actually cut by the micro-texture tool, until the simulation parameter data is basically equal to the actual parameter data, and the three-phase cutting finite element simulation model is a valid simulation model.

[0033] Step S2: selecting a plurality of tool shape parameters of the initial micro-textured tool that vary in correlation with the subsurface damage distance.

[0034] The tool shape parameters include the tool head rake angle, tool head clearance angle, tool head radius, the shortest distance between the micro texture and the tool tip, the spacing between adjacent micro texture structures, the width of a single micro texture structure, and the depth of a single micro texture structure.

[0035] Step S3: Take a predetermined number of characteristic data points for each tool shape parameter, simulate all the characteristic data points through the three-phase cutting finite element simulation model to obtain the corresponding subsurface damage distance, and perform variance analysis to select multiple filtered tool parameters from multiple tool shape parameters.

[0036] Step S3 includes the following sub-steps:

[0037] Step S3-1: perform multiple clustering of all characteristic parameter points with each characteristic data point, wherein the multiple clusterings contain different combinations of all characteristic parameter points, thereby forming multiple preliminary screening simulation parameter data groups, and obtaining corresponding subsurface damage distances based on the preliminary screening simulation parameter data groups.

[0038] Specifically, two feature data points are taken for each tool shape parameter, and the two feature data points correspond to the end points of the value range of the tool shape parameter respectively.

[0039] As shown in the following table, each row represents a cluster combination formed by clustering based on tool head radius.

[0040]

[0041] Among them, R1 and R2 are two characteristic data points of the tool head radius; α1 and α2 are two characteristic data points of the tool head back angle; β1 and β2 are two characteristic data points of the tool head front angle; T1 and T2 are two characteristic data points of the texture margin distance; W1 and W2 are two characteristic data points of the texture width distance; S1 and S2 are two characteristic data points of the texture adjacent distance; D1 and D2 are two characteristic data points of the texture depth distance; Y1, Y2...Y12 are the subsurface damage distances corresponding to the obtained multiple clustering combinations.

[0042] Step S3-2: Perform variance analysis on multiple subsurface damage distances corresponding to the same tool shape feature parameter, and select tool shape feature parameters with significant subsurface damage distance as screened tool parameters.

[0043] Specifically, the process of variance analysis is: divide the characteristic data points of the tool head radius Y1, Y2...Y12 into two groups respectively, and calculate the sum of squares between groups (SSA) and the sum of squares within groups (SSE) to obtain F value and P value, and judge based on the P value (set a predetermined value, compare the obtained P value with the predetermined value, and judge according to the conclusion) whether the tool shape parameter has significant effect on the under-surface damage distance. If significant, the tool shape feature is selected as the tool parameter after screening. In this embodiment, there are four tool parameters after screening: tool head rake angle, tool head radius, texture width and texture depth.

[0044] Step S4: establishing a response fitter model based on the plurality of screened tool parameters and the corresponding subsurface damage distances, and screening out a plurality of optimized tool parameters from the screened tool parameters through the response fitter model.

[0045] The response fitter model is verified by the fit verification R2, and finally a plurality of optimized tool parameters are obtained. Specifically, the corresponding fitter model is the subsurface damage distance represented by a second-order four-factor polynomial function. The four factors are the tool head rake angle, tool head radius, texture width and texture depth. The fitter model is a prediction model for the subsurface damage distance. Specifically, the applicability and significance of the multiple prediction results are evaluated by the above-mentioned variance analysis.

[0046] Specifically, after the evaluation of the fitter model, the genetic algorithm and the white whale algorithm simulate the phenomena of replication, crossover and mutation in natural selection and inheritance. Starting from any initial population, a group of individuals that are more suitable for the environment are generated through random selection, crossover and mutation operations, so that the group evolves to better and better areas in the search space. In this way, it continues to multiply and evolve from generation to generation, and finally converges to a group of individuals that are most adapted to the environment, thereby obtaining a high-quality solution to the problem, namely the genetic algorithm. In the process of finding the optimal solution, it is easy to fall into the situation of local solution. In order to get rid of this dilemma, the swimming, predation, and falling behaviors of white whales are introduced in the mutation process, so as to change the position to update the individual position, recalculate the fitness, and determine the optimal individual solution. The algorithm is used to find the optimal solution.

[0047] Step S5: designing a micro-texture tool according to a plurality of optimized tool parameters.

[0048] In this embodiment, based on the above-mentioned fitter model results and subsequent optimization, the final optimized tool parameters are: tool head rake angle: 20°, tool head radius 2μm, texture width: 50μm, texture depth 15μm, that is, at this time, for the cutting of CFRP composite layers with the same fiber orientation angle, the subsurface damage distance formed by the micro-texture tool with the above-mentioned parameter combination is close to the minimum value.

[0049] The above-mentioned implementation modes are preferred cases of the present invention and are not used to limit the protection scope of the present invention. Various deformations or modifications that can be made by ordinary technicians in this field without creative work within the scope of the attached claims are still within the protection scope of this patent.

Claims

1. A micro-texture tool simulation design method for cutting a CFRP composite layer, wherein the CFRP composite layer has a predetermined fiber orientation angle, the carbon fiber of the CFRP composite layer is used as a predetermined fiber phase, and the matrix of the CFRP composite layer is used as a predetermined matrix phase, characterized in that: The following steps are involved: Step S1: establishing a three-phase cutting finite element simulation model based on the initial micro-textured tool, the predetermined fiber phase, the predetermined matrix phase and the corresponding subsurface damage distance; Step S2: selecting a plurality of tool shape parameters of the initial micro-textured tool that vary in correlation with the subsurface damage distance; Step S3: taking a predetermined number of characteristic data points for each tool shape parameter, simulating all the characteristic data points through the three-phase cutting finite element simulation model to obtain corresponding subsurface damage distances, and performing variance analysis to select multiple selected tool parameters from the multiple tool shape parameters; Step S4: establishing a response fitter model based on the plurality of screened tool parameters and the corresponding subsurface damage distances, and screening out a plurality of optimized tool parameters from the screened tool parameters through the response fitter model; Step S5: designing a micro-texture tool according to the plurality of optimized tool parameters.

2. The micro-texture tool simulation design method for cutting CFRP composite layer according to claim 1, characterized in that: in, In step S1, after establishing the three-phase cutting finite element simulation model, the actual process parameters recorded during the actual cutting process of the CFRP composite layer by the initial micro-texture tool and the simulation process parameters obtained during the simulation process of the three-phase cutting finite element simulation model are compared and verified until the actual process parameters are the same as the corresponding simulation process parameters.

3. The micro-textured tool for cutting CFRP composite layer according to claim 1, characterized in that: in, In step S2, the tool shape parameters include the tool head rake angle, tool head clearance angle, tool head radius, the shortest distance between the micro texture and the tool tip, the spacing between adjacent micro texture structures, the width of a single micro texture structure, and the depth of a single micro texture structure.

4. The micro-textured tool for cutting a CFRP composite layer according to claim 1, Features: in, Step S3 includes the following sub-steps: Step S3-1: performing multiple clustering of all the characteristic parameter points with each characteristic data point, wherein the multiple clusterings include different combinations of all the characteristic parameter points, thereby forming multiple preliminary screening simulation parameter data groups, and obtaining corresponding subsurface damage distances based on the preliminary screening simulation parameter data groups; Step S3-2: performing variance analysis on a plurality of subsurface damage distances corresponding to the same tool shape feature parameter, and selecting the tool shape feature parameter having a significant effect on the subsurface damage distance as the screened tool parameter.

5. The micro-textured tool for cutting CFRP composite layer according to claim 4, characterized in that: in, Two characteristic data points are taken for each tool shape parameter, and the two characteristic data points respectively correspond to the end points of the value range of the tool shape parameter.

6. The micro-textured tool for cutting CFRP composite layer according to claim 1, characterized in that: in, In step S4, the response fitter model is verified through the fit verification R2, and finally a plurality of optimized tool parameters are obtained.

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