| SONG Zhuang,ZHAO Yu-gang,LIU Guang-xin,CAO Chen,LIU Qian,ZHANG Xia-jun-yu,DAI Di,ZHENG Zhi-long.Surface Roughness Prediction and Process Parameter Optimization of Magnetic Abrasive Finishing Based on WOA-LSSVM[J],52(1):242-252, 297 |
| Surface Roughness Prediction and Process Parameter Optimization of Magnetic Abrasive Finishing Based on WOA-LSSVM |
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| DOI：10.16490/j.cnki.issn.1001-3660.2023.01.025 |
| KeyWord:magnetic abrasive finishing orthogonal experiment whale optimization algorithm least squares support vector machine surface roughness |
| Author | Institution |
| SONG Zhuang |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| ZHAO Yu-gang |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| LIU Guang-xin |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| CAO Chen |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| LIU Qian |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| ZHANG Xia-jun-yu |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| DAI Di |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
| ZHENG Zhi-long |
School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo , China |
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| Abstract: |
| The work aims to achieve the accurate prediction of surface roughness during magnetic abrasive finishing and obtain the optimal process parameters of improving the material surface quality. CBN/Fe-based magnetic abrasive powder was prepared by the gas-solid two-phase double-stage atomization and rapid solidification method. The magnetic abrasive powder had ideal spherical structure and high grinding efficiency and performance, so it could overcome the shortcomings of poor performance of magnetic abrasive powder prepared by traditional preparation process and could be used for magnetic abrasive finishing experiment. L25(54) orthogonal experiment was designed with 316L stainless steel as experimental material. The rotational speed of the magnetic pole n, the working gap δ, the feed velocity of workpiece v and the magnetic abrasive powder size d were taken as the input values, and the surface roughness Ra obtained under different combinations of process parameters was taken as the output value. At the same time, the whale optimization algorithm (WOA) and least squares support vector machine (LSSVM) were introduced by Matlab. According to the orthogonal experimental results, the prediction model of surface roughness of magnetic abrasive finishing was constructed based on WOA-LSSVM. Then, the constructed nonlinear prediction model was used as the fitness function, and WOA was again employed to globally optimize the process parameters. Finally, the optimal combination of process parameters for magnetic abrasive finishing was obtained. Three groups of verification experiments were carried out with the optimized process parameters, and the results were compared with the prediction results of WOA-LSSVM model. The root mean square error RMSE was 0.003 373, and the average absolute error MAPE was 2.814% based on the WOA-LSSVM surface roughness prediction model constructed by orthogonal experiment. The results showed that the WOA-LSSVM surface roughness prediction model constructed for magnetic abrasive finishing had high prediction accuracy. With the surface roughness Ra as the evaluation standard, the optimal combination of process parameters was obtained:the rotational speed of the magnetic pole n was 1 526.690 7 r/min, the working gap δ was 1.527 414 mm, the feed velocity of workpiece v was 1.076 732 7 mm/min and the magnetic abrasive particle size d was 114.260 52 μm. The optimal surface roughness Ra under the optimal process parameters was 0.063 512 μm. The existing experimental equipment had some limitations, so the process parameters were fine-tuned to the maximum extent. The fine-tuning process parameters were used to conduct the experiment again, and the surface roughness Ra of the material was 0.062 μm, with a relative error of 2.44% compared with the predicted value. The results of this study were verified by experiments, which provided a theoretical basis for the predictable machining of magnetic abrasive finishing technology. The surface roughness prediction model of magnetic abrasive finishing based on WOA-LSSVM has excellent fitting performance, which can realize the controllable machining of magnetic abrasive finishing. The optimal combination of magnetic abrasive finishing technology and WOA algorithm can obtain better material surface quality. |
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