宋壮,赵玉刚,刘广新,曹辰,刘谦,张夏骏雨,代迪,郑志龙.基于WOA–LSSVM的磁粒研磨表面粗糙度预测及工艺参数优化[J].表面技术,2023,52(1):242-252, 297.
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].Surface Technology,2023,52(1):242-252, 297 |
| 基于WOA–LSSVM的磁粒研磨表面粗糙度预测及工艺参数优化 |
| 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 |
| 中文关键词: 磁粒研磨 正交试验 鲸鱼优化算法 最小二乘支持向量机 表面粗糙度 |
| 英文关键词:magnetic abrasive finishing orthogonal experiment whale optimization algorithm least squares support vector machine surface roughness |
| 基金项目:国家自然科学基金(51875328);山东省自然科学基金面上项目(ZR2019MEE013) |
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| Author | Institution |
| SONG Zhuang | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| ZHAO Yu-gang | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| LIU Guang-xin | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| CAO Chen | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| LIU Qian | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| ZHANG Xia-jun-yu | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| DAI Di | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
| ZHENG Zhi-long | School of Mechanical Engineering, Shandong University of Technology, Shandong Zibo 255000, China |
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| 中文摘要: |
| 目的 实现磁粒研磨过程中表面粗糙度值的准确预测，同时获得提高材料表面质量的最优工艺参数组合。方法 通过自由降落气固两相流双级雾化快凝法制备CBN/Fe基磁性磨料，用于磁粒研磨试验。将316L不锈钢作为实验材料，以磁极转速n、加工间隙δ、进给速度v和磁性磨料粒径d为输入值，以表面粗糙度Ra为输出值，设计L25(54)正交试验。同时借助Matlab软件引入鲸鱼优化算法(WOA)与最小二乘支持向量机(LSSVM)，基于正交试验结果构建WOA–LSSVM的磁粒研磨表面粗糙度预测模型，并将输出值表面粗糙度 Ra 作为适应度，再次调用WOA对工艺参数进行全局寻优，获得最优工艺参数组合。使用优化得到的工艺参数组合进行试验，并与模型预测结果进行对比。结果 根据正交试验构建的WOA–LSSVM表面粗糙度预测模型的均方根误差(RMSE)为0.003 373，平均绝对百分比误差(MAPE)为2.814%。通过WOA寻优得到了最佳工艺参数组合，n、δ、v、d分别为1 526.690 7 r/min、1.527 414 mm、1.076 732 7 mm/min、114.260 52 μm，此时获得的最佳表面粗糙度为0.063 512 μm。对寻优所得的工艺参数组合微调后进行试验，得到的表面粗糙度Ra为0.062 μm，与模型预测值的相对误差约为2.44%。结论 基于WOA–LSSVM的表面粗糙度预测模型拟合性能优良，可实现磁粒研磨的可控加工。使用磁粒研磨技术结合WOA的寻优结果可获得更优的表面质量。 |
| 英文摘要: |
| 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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