梁强,徐永航,李永亮,王敬,杜彦斌.基于MOGWO的45#钢表面激光抛光工艺参数多目标优化[J].表面技术,2024,53(10):173-182.
LIANG Qiang,XU Yonghang,LI Yongliang,WANG Jing,DU Yanbin.Multi-objective Optimization of Laser Polishing Process Parameters for the Surface of 45# Steel Based on MOGWO[J].Surface Technology,2024,53(10):173-182
基于MOGWO的45#钢表面激光抛光工艺参数多目标优化
Multi-objective Optimization of Laser Polishing Process Parameters for the Surface of 45# Steel Based on MOGWO
投稿时间:2023-07-26  修订日期:2023-09-23
DOI:10.16490/j.cnki.issn.1001-3660.2024.10.014
中文关键词:  激光抛光  二阶响应面模型  MOGWO算法  TOPSIS-CRITIC  多目标优化
英文关键词:laser polishing  second-order response surface model  MOGWO algorithm  TOPSIS-CRITIC  multi-objective optimization
基金项目:重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0473);重庆市高校创新研究群体资助项目(CXQT21024);制造装备机构设计与控制重庆市重点实验室开放课题(KFJJ2019078);重庆工商大学研究生创新型科研资助项目(YJSCXX2023-211-54)
作者单位
梁强 重庆工商大学 机械工程学院,重庆 400067;重庆工商大学 智能装备绿色设计与制造重庆市重点实验室,重庆 400067 
徐永航 重庆工商大学 机械工程学院,重庆 400067 
李永亮 重庆工商大学 机械工程学院,重庆 400067;重庆工商大学 智能装备绿色设计与制造重庆市重点实验室,重庆 400067 
王敬 重庆工商大学 机械工程学院,重庆 400067;重庆工商大学 智能装备绿色设计与制造重庆市重点实验室,重庆 400067 
杜彦斌 重庆工商大学 机械工程学院,重庆 400067;重庆工商大学 智能装备绿色设计与制造重庆市重点实验室,重庆 400067 
AuthorInstitution
LIANG Qiang School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing 400067, China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing 400067, China 
XU Yonghang School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing 400067, China 
LI Yongliang School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing 400067, China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing 400067, China 
WANG Jing School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing 400067, China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing 400067, China 
DU Yanbin School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing 400067, China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing 400067, China 
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中文摘要:
      目的 提高45#钢表面激光抛光后的成形质量,提出一种激光抛光工艺参数多目标优化方法。方法 构建基于功率、扫描速度、搭接距离的三因素三水平激光抛光试验,并分别应用粗糙度测量仪、显微硬度计和超景深三维显微镜测试抛光层的粗糙度、显微硬度和抛光层深度。基于试验数据,分别应用指数模型和二阶响应面模型构建抛光工艺参数与表面粗糙度、显微硬度、抛光深度的回归预测模型,并对2种模型的预测精度进行对比分析。采用多目标灰狼优化算法(MOGWO)结合优劣解距离法(TOPSIS)-CRITIC综合评价决策体系对抛光工艺参数进行寻优和多属性决策。结果 二阶响应面模型具有更高的预测精度,能够更好地反映激光抛光工艺参数与各响应目标之间的映射关系。当功率为113 W、扫描速度为3 m/min、搭接距离为0.13 mm时,粗糙度值Ra从11.563 μm降至5.713 μm,降幅为50.59%,显微硬度从185.9HV0.5升至364.7HV0.5,升幅为96.18%,此时的抛光深度为0.051 mm,最大相对误差为7.84%。结论 此方法可以为其他金属材料表面激光抛光质量预测模型的构建及工艺参数寻优提供借鉴。
英文摘要:
      To improve the forming quality of 45 steel surfaces after laser polishing, the work aims to propose a multi-objective optimization method of laser polishing process parameters. Laser power, scanning speed, and overlap distance were taken as process parameters, and surface roughness, microhardness, and polishing depth were taken as evaluation indexes to construct a 3-factor and 3-level laser polishing experiment. Before the experiment, the plate was ground flat and processed with a ball milling cutter to produce a texture and then ultrasonically cleaned with anhydrous ethanol and dried, and the zigzag scanning trajectory was used to carry out the laser polishing experiment. A roughness meter was used to measure the surface roughness of the polished surface before and after laser polishing, a microhardness tester was used to measure the microhardness of the polished layer of the material before and after laser polishing, and a super depth-of-field 3D microscope was used to measure the polishing depth after laser polishing. Based on the experimental data, the exponential model and the second-order response surface model were used to construct the regression prediction models of the laser polishing process parameters and the surface roughness, microhardness, and polishing depth regarding the construction method of the prediction model of the geometrical characteristics of the laser cladding layer. By comparing the correlation coefficient R, determination coefficient R2, and determination adjustment coefficient with the significance test of the two models, as well as comparing the correlation between the experimental values and the predicted values of the two models, it was obtained that the second-order response surface model had a higher prediction accuracy, and it could better reflect the mapping relationship between the laser polishing process parameters and the response targets. The main effect analysis was used to study the effect law of each process parameter of laser polishing on the surface roughness, microhardness, and polishing depth of laser polishing. The multi-objective gray wolf optimization algorithm (MOGWO) was used to optimize the laser polishing process parameters so that the microhardness was as large as possible and the surface roughness and polishing depth were as small as possible. The 50 Pareto solution sets obtained were substituted into the comprehensive evaluation decision system constructed by the technique for order preference by similarity to an ideal solution (TOPSIS) method and CRITIC for decision making, and the best combination of laser polishing process parameters was obtained:laser power 114 W, scanning speed 3 m/min and lap distance 0.13 mm. The process test was carried out under the optimal combination of process parameters. The experimental results showed that the surface roughness of the material decreased from Ra 11.563 μm to Ra 5.713 μm under the combination of process parameters, with a decrease of 50.59%. The microhardness increased from 185.9HV0.5 to 364.7HV0.5, with an increase of 96.18%. At this time, the polishing depth was 0.051 mm, and the maximum relative error was 7.84%. It is proved that this method can provide a reference for the construction of a laser polishing quality prediction model and process parameter optimization for other metal materials.
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