赵传营,赵玉刚,刘宁,宋盼盼,高跃武,张勇,刘广新.基于神经网络遗传算法的磁粒研磨TC4材料工艺参数优化[J].表面技术,2020,49(2):316-321.
ZHAO Chuan-ying,ZHAO Yu-gang,LIU Ning,SONG Pan-pan,GAO Yue-wu,ZHANG Yong,LIU Guang-xin.Optimization of Process Parameters of Magnetic Abrasive Finishing TC4 Material Based on Neural Network and Genetic Algorithm[J].Surface Technology,2020,49(2):316-321 |
| 基于神经网络遗传算法的磁粒研磨TC4材料工艺参数优化 |
| Optimization of Process Parameters of Magnetic Abrasive Finishing TC4 Material Based on Neural Network and Genetic Algorithm |
| 投稿时间：2019-06-05 修订日期：2020-02-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2020.02.040 |
| 中文关键词: 磁粒研磨 TC4 正交实验 神经网络 遗传算法 表面粗糙度 |
| 英文关键词:magnetic abrasive finishing TC4 orthogonal experiment neural network genetic algorithm roughness |
| 基金项目:国家自然科学基金（51875328）；山东省自然科学基金（ZR201807060394） |
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| Author | Institution |
| ZHAO Chuan-ying | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
| ZHAO Yu-gang | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
| LIU Ning | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
| SONG Pan-pan | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
| GAO Yue-wu | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
| ZHANG Yong | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
| LIU Guang-xin | School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China |
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| 中文摘要: |
| 目的 利用磁粒研磨光整加工技术提高TC4材料的表面质量，使用BP神经网络建立加工工艺参数和表面粗糙度之间的关系，使用遗传算法寻找最优工艺参数组合。方法 使用双级雾化快凝法制备的金刚石磁性磨料对TC4材料工件进行L9(34)正交试验，借助Matlab软件建立结构为4-12-1的BP神经网络，根据正交试验结果训练BP神经网络，探究工艺参数主轴转速n、加工间隙δ、进给速率v、磨料粒径D和表面粗糙度Ra之间的关系。使用决定系数R2评判BP神经网络训练结果，基于训练好的BP神经网络使用遗传算法对工艺参数进行全局寻优。使用计算得到的优化工艺参数进行试验，并测量工件表面粗糙度，与计算得到的表面粗糙度做对比。结果 BP神经网络的预测误差在1.5%以下，通过决定系数R2优化的模型可在训练样本较少的情况下进行有效可靠的预测。遗传算法优化的结果，在主轴转速为1021.26 r/min、加工间隙为1.52 mm、进给速率为1.04 mm/min、磨料粒径为197.91 μm下，获得最佳表面粗糙度，为0.0951 μm。使用调整后的工艺参数，在主轴转速为1020 r/min、加工间隙为1.50 mm、进给速率为1.0 mm/min、磨料粒径为196 μm下，试验得到的表面粗糙度为0.093 μm，与计算得到的最佳表面粗糙度误差为2.21%。结论 采用磁粒研磨光整加工技术与寻优参数结合，可以有效提高TC4材料加工后的表面质量。 |
| 英文摘要: |
| The work aims to improve the surface quality of TC4 materials by magnetic abrasive finishing, establish the relationship between processing parameters and roughness by BP neural network, and find the optimal combination of process parameters by genetic algorithm. The diamond magnetic abrasive prepared by gas-solid two-phase double-stage atomization and rapid solidification was used to perform L9(34) orthogonal test on TC4 material workpiece. BP neural network with the structure of 4-12-1 was established by Matlab software. BP was trained according to orthogonal test results to explore the relationship between the spindle speed n, working gap δ, feed rate v, abrasive size D and roughness Ra. The BP neural network training results were evaluated by coefficient of determination R2. Based on the trained BP neutral networks, genetic algorithms were used to globally optimize process parameters. The calculated optimized process parameters were used to conduct experiment and measure surface roughness and then compare such roughness with the calculated roughness Ra. The prediction error of BP neural network was less than 1.5%, the model optimized by coefficient of determination R2 could make effective and reliable prediction under the condition of fewer samples. The results of genetic algorithm optimization: the optimum roughness was 0.0951 μm at spindle speed of 1021.26 r/min, machining gap of 1.52 mm, feed rate of 1.04 mm/min, and abrasive size of 197.91 μm. The adjusted process parameters were: spindle speed of 1020 r/min, machining gap of 1.50 mm, feed rate of 1.0 mm/min and abrasive size of 196 μm. The test roughness was 0.093 μm, and the error from the calculated optimal surface roughness was 2.21%. The combination of magnetic abrasive finishing and optimization parameters can effectively improve the surface quality of TC4 material after processing. |
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