| 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],53(10):173-182 |
| Multi-objective Optimization of Laser Polishing Process Parameters for the Surface of 45# Steel Based on MOGWO |
| Received:July 26, 2023 Revised:September 23, 2023 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.10.014 |
| KeyWord:laser polishing second-order response surface model MOGWO algorithm TOPSIS-CRITIC multi-objective optimization |
| Author | Institution |
| LIANG Qiang |
School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing , China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
| XU Yonghang |
School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing , China |
| LI Yongliang |
School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing , China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
| WANG Jing |
School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing , China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
| DU Yanbin |
School of Mechanic Engineering, Chongqing Technology and Business University, Chongqing , China;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
|
| Hits: |
| Download times: |
| Abstract: |
| 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. |
| Close |
|
|
|