表面技术2024，Vol.53Issue(16)：169-181,13.DOI:10.16490/j.cnki.issn.1001-3660.2024.16.014
基于综合满意度函数的D2钢激光抛光工艺参数优化方法
Optimization Method of Laser Polishing Process Parameters for D2 Steel Based on Comprehensive Satisfaction Function
摘要
Abstract
Due to its excellent wear resistance and good workability,D2 steel is widely used in various cold work moulds.However,after long time working,the roughness of D2 die steel on the surface of the die will be greatly increased,which will seriously affect the precision of the products.Laser polishing technology,as a non-contact,stable and easily automated surface treatment technology,can effectively reduce the roughness of the material surface.However,in the laser polishing process,due to the laser beam working back and forth,the roughness or hardness of the polished overlap or corners,etc.will be unstable compared with the unpolished part.How to control this instability,so that the whole laser polished part of the various values tend to stabilize,there are still few research reports.Therefore,in order to simultaneously improve the laser polishing effect and the stability of the polishing quality of D2 die steel,a method for optimizing the laser polishing process parameters of D2 steel based on the comprehensive satisfaction function was proposed.Firstly,a continuous laser was used for laser polishing based on the Box-Behnken method for a 3-factor,3-level experimental design with surface roughness,microhardness and polishing depth as the optimization objectives,and laser power,scanning speed and overlap rate as the experimental factors.Secondly,the dual-response surface model of the mean value and standard deviation of each objective was constructed according to the experimental data,and the objective entropy weight of the mean value and standard deviation of each objective was obtained according to the entropy weight theory,and the comprehensive entropy weight was constructed in combination with the subjective entropy weight.Finally,the dual-response surface model and comprehensive entropy right were introduced into the satisfaction function to construct an improved comprehensive satisfaction model,and the optimal combination of process parameters was obtained according to the stochastic gradient descent method,which was compared with the optimized dual-response surface method to prove the superiority of the method.The experimental results showed that with the laser power of 551 W,the scanning speed of 9 mm/s,and the overlap rate of 0.55,the roughness decreased from Ra=5.188 μm to Ra=1.056 μm with a reduction of 79.65% and the standard deviation of the roughness was 0.0128 μm.The microhardness decreased from 541.3HV0.5 to 509.3HV0.5 with a reduction of 5.91% and the standard deviation of microhardness was 12.9811HV0.5.At the same time,the depth of polishing was 0.331 mm and the standard deviation of the depth of polishing was 0.0024 mm.Comparison of the comprehensive results of the improved comprehensive satisfaction model method with the dual-response surface method,combining with the results of the friction wear experiments after the optimal combination of process parameters and traditional mechanical polishing,shows that the method proposed in this paper can maximize the microhardness while effectively reducing the surface roughness,keeping the polishing depth as small as possible.This method can provide a reference for the optimization of laser polishing process parameters for D2 die steel.关键词
D2钢/激光抛光/工艺参数优化/综合满意度函数/双响应面模型/综合熵权Key words
D2 steel/laser polishing/process parameter optimization/comprehensive satisfaction function/dual-response surface model/comprehensive entropy weight分类
航空航天引用本文复制引用
梁强,徐永航,杜彦斌,王敬,徐彬源..基于综合满意度函数的D2钢激光抛光工艺参数优化方法[J].表面技术,2024,53(16):169-181,13.基金项目
重庆市教育科学规划重点项目(2019-GX-015) （2019-GX-015）
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0473) （CSTB2022NSCQ-MSX0473）
重点实验室平台开放课题(KFJJ2019078) （KFJJ2019078）
重庆工商大学研究生创新型科研资助项目(yjscxx2023-211-54)Chongqing Education Science Planning Program(2019-GX-015) （yjscxx2023-211-54）
the Chongqing Natural Science Foundation General Project,China(CSTB2022NSCQ-MSX0473) （CSTB2022NSCQ-MSX0473）
the Key Laboratory Platform Open Project,China(KFJJ2019078) （KFJJ2019078）
the Innovation Science Research Foundation for Graduates of CTBU,China(yjscxx2023-211-54) （yjscxx2023-211-54）
