CN116108753A - Parameter optimization method for milling titanium alloy by micro-texture ball end mill - Google Patents
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Abstract
The invention provides a parameter optimization method for milling a titanium alloy by a micro-texture ball end mill. The method uses a milling performance prediction model established by stepwise regression analysis as a model reference to be optimized, namely milling force F and cutter abrasion V B Roughness R of the surface of the workpiece a And (3) a model equation, introducing a genetic algorithm, and establishing a target optimization model of the micro-texture parameters and the cutting parameters. The method not only can realize targeted optimization of milling force, surface roughness and tool front tool face abrasion single targets, but also can realize related parameter optimization by weighting references of the milling force, the surface roughness and the tool front tool face abrasion single targets, has comprehensive functions, is convenient to use, has high accuracy, and has a relative error of a result and an experimental result within 10%.
Description
Technical Field
The invention belongs to the technical field of titanium alloy mechanical manufacturing, and particularly relates to a parameter optimization method for milling titanium alloy by a micro-texture ball end mill.
Background
Along with the continuous application of titanium alloy in the field of mechanical manufacturing, a high-quality processing method for titanium alloy is also continuously developed and improved. Ball nose milling cutter BNM-200 is used as an important tool for milling titanium alloy, and is severely worn and damaged in the milling process. At present, research shows that the etching of the micro-concave circular pit-shaped micro-texture on the front cutter surface of the milling cutter is a good method for improving the milling wear resistance of the milling cutter, and the micro-texture parameters, cutting speed, cutting depth, feeding amount and the like can influence the quality of the processed surface of the titanium alloy and the wear of the cutter, so that the optimization of the parameters related to the milling process is very important for the advanced enhancement of the milling performance of the cutter. Aiming at relevant parameters of milling, a method for automatically carrying out parameter optimization processing is designed, the optimization is convenient to use, important optimization references can be provided for the actual test, the production efficiency is improved, and the production cost is reduced.
Due to the variability of actual milling environments, the milling parameter optimization method has a few limitations at present: the optimization method can only realize accurate parameter optimization for the technical process of milling the titanium alloy by the BNM-200 type micro-concave round pit micro-texture ball end mill under the dry cutting basic condition.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a parameter optimization method for milling a titanium alloy by a micro-texture ball end mill.
The invention is realized by the following technical scheme that the invention provides a parameter optimization method for milling titanium alloy by a micro-texture ball end mill, wherein a milling performance prediction model established by stepwise regression analysis is used as a model reference to be optimized, namely milling force F and cutter abrasion V B Roughness R of the surface of the workpiece a The model equation is introduced into a genetic algorithm, and a target optimization model of the micro-texture parameters and the cutting parameters is established;
step 1: obtaining a model to be optimized; establishing a stepwise regression model through research and analysis of test results, and solving a model equation to be optimized by using MATLAB;
step 2: preparing an operation environment and calling a corresponding algorithm library;
step 3: optimizing the single-target milling force F, setting weight values, and substituting the single-target milling force F into a template function to set related parameters; optimizing the setting of an objective function by using milling force F; obtaining parameter values and constraint conditions of a part of genetic algorithm, wherein 4 items of values are population scale, crossover probability, mutation probability and termination algebra, and establishing independent variable definition domains lb and ub;
step 4: tool wear V B And surface roughness R a Optimizing and setting weight values, substituting template functions to set related parameters;
step 5: multi-objective optimization and weight setting, substituting a template function and setting related parameters; the multi-objective optimization is to comprehensively consider milling force F and tool wear V B Surface roughness R a The weighted values of the three are used as references for optimization treatment;
step 6: constraint condition setting and data output optimization; setting extremum ranges for each parameter;
step 7: and drawing an adaptability iteration graph, and calling matplotlib to generate a genetic iteration graph.
Further, the model equation to be optimized includes:
milling force F:
tool wear V B ：
V B ＝137.25-0.2966v-14.293a p +742.49f-1.5372D (2)-1.1783L-0.0596L 1 +1.2232va p -0.362a p L 1 -4.651fD+0.0035DL 1 +0.0091D 2 +0.004L 2
Surface roughness R a ：
Further, the method optimizes the built model by invoking Genetic Algorithm functions from the heuristic code library scikit-opt in Python.
Further, an extremum range is set for each parameter, specifically: cutting speedv[110,180]m/min, depth of cut a p [0.2,0.55]mm, feed rate f 0.05,0.12]mm/r, distance from edge L90,160]Micro-texture pitch L 1 [120,260]Mu m, micro-texture diameter d [30,100 ]]μm and post-processing workpiece surface roughness R a ≤0.5。
Further, the population scale is set to 200, the maximum iteration number is between 150 and 200, the crossover probability is greater than 0.9, the variation probability is less than 0.1, and the sum of weights in multi-objective optimization is 1.
The beneficial effects of the invention are as follows:
the micro-texture parameter and cutting parameter optimization model provided by the invention not only can realize targeted optimization of single targets of milling force, surface roughness and tool front tool face abrasion, but also can realize relevant parameter optimization by weighting references of the three, has comprehensive functions, is convenient to use, has high accuracy, and has a relative error of a result and an experimental result within 10%.
Drawings
FIG. 1 is a diagram of an optimization algorithm operating environment.
FIG. 2 is a diagram of milling force F to create an objective function and call genetic algorithm optimization.
FIG. 3 is tool wear V B And establishing an objective function and calling a genetic algorithm optimization graph.
FIG. 4 is a graph of surface roughness R a And establishing an objective function and calling a genetic algorithm optimization graph.
Fig. 5 is a schematic diagram of the set-up objective function.
FIG. 6 is a schematic diagram of the optimization of a calling genetic algorithm.
FIG. 7 is a schematic diagram of determining constraints and optimizing result output.
FIG. 8 is a genetic algorithm optimization iteration diagram.
FIG. 9 is a flow chart of an optimization operation.
FIG. 10 is an exemplary diagram of an optimization operation surface.
Fig. 11 is a genetic iteration diagram.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
With reference to fig. 1-11, the invention provides a parameter optimization method for milling titanium alloy by a micro-texture ball end mill, wherein a milling performance prediction model established by stepwise regression analysis is used as a model reference to be optimized, namely milling force F and tool wear V B Roughness R of the surface of the workpiece a The model equation is introduced into a genetic algorithm, and a target optimization model of the micro-texture parameters and the cutting parameters is established; the genetic algorithm adopted by the optimization method optimizes the established model by calling Genetic Algorithm functions through a heuristic algorithm code library scikit-opt in Python;
step 1: obtaining a model to be optimized; establishing a stepwise regression model through research and analysis of test results, and solving a model equation to be optimized by using MATLAB;
the model equation to be optimized comprises:
milling force F:
tool wear V B ：
V B ＝137.25-0.2966v-14.293a p +742.49f-1.5372D (2)-1.1783L-0.0596L 1 +1.2232va p -0.362a p L 1 -4.651fD+0.0035DL 1 +0.0091D 2 +0.004L 2
Surface roughness R a ：
Step 2: preparing an operation environment and calling a corresponding algorithm library; the Python specific technical operation is shown in FIG. 1.
Step 3: optimizing the single-target milling force F, setting weight values, and substituting the single-target milling force F into a template function to set related parameters; optimizing the setting of an objective function by using milling force F; obtaining parameter values and constraint conditions of a part of genetic algorithm, wherein 4 items of values are population scale, crossover probability, mutation probability and termination algebra, and establishing independent variable definition domains lb and ub; the Python specific technical operation is shown in FIG. 2.
Step 4: tool wear V B And surface roughness R a Optimizing and setting weight values, substituting template functions to set related parameters;
by the same method as shown in the step 3, the cutter wear V B And surface roughness R a The optimized Python specific technical operation is shown in fig. 3 and 4.
Step 5: multi-objective optimization and weight setting, substituting a template function and setting related parameters; the multi-objective optimization is to comprehensively consider milling force F and tool wear V B Surface roughness R a The weighted values of the three are used as references for optimization treatment; the Python specific technical operation of the objective function of the multi-objective optimization and the setting of the three weights is shown in fig. 5. And (3) obtaining parameter values and constraint conditions such as population scale, crossover probability, mutation probability, termination algebra and the like, establishing a custom variable definition domain, and the same is shown in the step (3). The Python specific technical operation is shown in FIG. 6.
Step 6: constraint condition setting and data output optimization; the actual machining of the micro-texture ball end mill milling titanium alloy is limited by a series of machining conditions, and in order to ensure the machining safety, each machining index and each workpiece precision are maintained in a normal range, each parameter is required to be provided with an extremum range; setting extremum ranges for various parameters, specifically: cutting speed v 110,180]m/min, depth of cut a p [0.2,0.55]mm, feed rate f 0.05,0.12]mm/r, distance from edge L90,160]Micro-texture pitch L 1 [120,260]Mu m, micro-texture diameter d [30,100 ]]μm and post-processing workpiece surface roughness R a Less than or equal to 0.5. The Python specific technical operation is shown in FIG. 7.
Step 7: and drawing an adaptability iteration graph, and calling matplotlib to generate a genetic iteration graph. The Python specific technical operation is shown in FIG. 8.
Under the algorithm of the technical scheme steps, the population scale is generally set to 200, the maximum iteration number is about 150-200, the crossover probability is generally greater than 0.9, the variation probability is generally less than 0.1, the sum of weights in multi-objective optimization is generally 1, and the weights are divided according to milling force 0.4, tool wear 0.3 and surface roughness 0.3.
The specific implementation flow of the invention is shown in fig. 9, and the operation interface of the micro-texture ball end mill for milling the titanium alloy related parameters is set according to the operation steps of the method of the invention, as illustrated in fig. 10.
In the specific implementation, 7 constraint conditions shown in fig. 9 are confirmed, and the GA parameters and the optimization mode are selected, so that the genetic iteration diagram and the optimization result can be output according to the well-programmed calculation program of Python. In order to facilitate understanding of the invention, practical examples will be described below taking multi-objective optimization as an example.
Examples: multi-objective optimization, setting F, V B 、R a The weights are divided according to milling force 0.4, tool abrasion 0.3 and surface roughness 0.3, the GA parameter setting population scale is 200, iteration times are 150, crossover probability is 0.9 and variation probability is 0.1, genetic algorithm multi-objective optimization is carried out, a genetic iteration chart automatically generated by the algorithm is shown in fig. 11, and at the moment, the optimization output result is that at the moment, the optimization result is that: v=120.02 (m/min), a p ＝0.20(mm)、f＝0.05(mm/r)、D＝64.36(μm)、L＝158.61(μm)、L 1 ＝122.02(μm)。
The above describes in detail the method for optimizing parameters in milling titanium alloy by using micro-texture ball end mill, and specific examples are applied to illustrate the principle and implementation of the invention, and the above examples are only used to help understand the method and core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (5)
1. A parameter optimization method for milling titanium alloy by a micro-texture ball end mill is characterized by comprising the following steps: the method uses a milling performance prediction model established by stepwise regression analysis as a model reference to be optimized, namely milling force F and cutter abrasion V B Roughness R of the surface of the workpiece a The model equation is introduced into a genetic algorithm, and a target optimization model of the micro-texture parameters and the cutting parameters is established;
step 1: obtaining a model to be optimized; establishing a stepwise regression model through research and analysis of test results, and solving a model equation to be optimized by using MATLAB;
step 2: preparing an operation environment and calling a corresponding algorithm library;
step 3: optimizing the single-target milling force F, setting weight values, and substituting the single-target milling force F into a template function to set related parameters; optimizing the setting of an objective function by using milling force F; obtaining parameter values and constraint conditions of a part of genetic algorithm, wherein 4 items of values are population scale, crossover probability, mutation probability and termination algebra, and establishing independent variable definition domains lb and ub;
step 4: tool wear V B And surface roughness R a Optimizing and setting weight values, substituting template functions to set related parameters;
step 5: multi-objective optimization and weight setting, substituting a template function and setting related parameters; the multi-objective optimization is to comprehensively consider milling force F and tool wear V B Surface roughness R a The weighted values of the three are used as references for optimization treatment;
step 6: constraint condition setting and data output optimization; setting extremum ranges for each parameter;
step 7: and drawing an adaptability iteration graph, and calling matplotlib to generate a genetic iteration graph.
2. The method of claim 1, wherein the model equation to be optimized comprises:
milling force F:
tool wear V B ：
V B ＝137.25-0.2966v-14.293a p +742.49f-1.5372D (2)-1.1783L-0.0596L 1 +1.2232va p -0.362a p L 1 -4.651fD+0.0035DL 1 +0.0091D 2 +0.004L 2
Surface roughness R a ：
3. The method of claim 1, wherein the method optimizes the built model by calling Genetic Algorithm functions from a heuristic code library scikit-opt in Python.
4. The method according to claim 1, characterized in that extremum ranges are set for the parameters, in particular: cutting speed v 110,180]m/min, depth of cut a p [0.2,0.55]mm, feed rate f 0.05,0.12]mm/r, distance from edge L90,160]Micro-texture pitch L 1 [120,260]Mu m, micro-texture diameter d [30,100 ]]μm and post-processing workpiece surface roughness R a ≤0.5。
5. The method of claim 1, wherein the population size is set to 200, the maximum number of iterations is between 150 and 200, the crossover probability is greater than 0.9, the variation probability is less than 0.1, and the sum of weights in the multi-objective optimization is 1.
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| CN106363168A (en) * | 2016-12-02 | 2017-02-01 | 哈尔滨理工大学 | Micro-texture hard alloy ball-end mill preparation method based on 3D printing technology |
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| CN115495923A (en) * | 2022-10-09 | 2022-12-20 | 北部湾大学 | Milling parameter multi-objective optimization and decision method based on chaotic genetic algorithm |
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| CN106363168A (en) * | 2016-12-02 | 2017-02-01 | 哈尔滨理工大学 | Micro-texture hard alloy ball-end mill preparation method based on 3D printing technology |
| CN108920844A (en) * | 2018-07-06 | 2018-11-30 | 哈尔滨理工大学 | A kind of rose cutter geometric Parameters Optimization method based on associative simulation |
| CN111563301A (en) * | 2020-05-14 | 2020-08-21 | 北京工业大学 | Thin-wall part milling parameter optimization method |
| CN115495923A (en) * | 2022-10-09 | 2022-12-20 | 北部湾大学 | Milling parameter multi-objective optimization and decision method based on chaotic genetic algorithm |
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