李文龙,陈燕,赵杨,吕旖旎.采用神经网络和遗传算法优化磁粒研磨TC4弯管工艺参数[J].表面技术,2020,49(6):330-336.
LI Wen-long,CHEN Yan,ZHAO Yang,LYU Yi-ni.Optimizing Technological Parameters of Magnetite Grinding TC4 Elbow by Neural Network and Genetic Algorithms[J].Surface Technology,2020,49(6):330-336 |
| 采用神经网络和遗传算法优化磁粒研磨TC4弯管工艺参数 |
| Optimizing Technological Parameters of Magnetite Grinding TC4 Elbow by Neural Network and Genetic Algorithms |
| 投稿时间：2019-05-30 修订日期：2020-06-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2020.06.040 |
| 中文关键词: 磁粒研磨 弯管 内表面 表面粗糙度 BP神经网络 遗传算法 TC4钛合金 |
| 英文关键词:magnetic particle grinding elbow inner surface surface roughness BP neural network genetic algorithm TC4 titanium alloy |
| 基金项目:国家自然科学基金（51775258）；辽宁省自然科学基金重点项目（20170540458）；精密与特种加工教育部重点实验室基金（B201703） |
| 作者 | 单位 |
| 李文龙 | 辽宁科技大学 机械工程与自动化学院，辽宁 鞍山 114051 |
| 陈燕 | 辽宁科技大学 机械工程与自动化学院，辽宁 鞍山 114051 |
| 赵杨 | 辽宁科技大学 机械工程与自动化学院，辽宁 鞍山 114051 |
| 吕旖旎 | 辽宁科技大学 机械工程与自动化学院，辽宁 鞍山 114051 |
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| Author | Institution |
| LI Wen-long | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China |
| CHEN Yan | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China |
| ZHAO Yang | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China |
| LYU Yi-ni | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China |
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| 中文摘要: |
| 目的 提高研磨TC4弯管内表面质量及加工效率，对磁粒研磨加工工艺参数进行优化。方法 首先设定最优表面质量为优化的目标，然后将影响磁粒研磨TC4弯管内表面质量的四个主要工艺参数作为优化对象，对所要建立的神经网络隐含层节点数的个数进行试验，并选择最优值，之后建立反映TC4弯管内表面粗糙度和主要工艺参数的非线性映射模型，最终使用遗传算法得到TC4弯管内表面粗糙度最优值和磁粒研磨加工TC4弯管内表面的最优工艺参数组合，并且通过试验验证其预测结果的精确性。结果 通过建立结构为4-5-1的BP神经网络，并利用遗传算法的预测，得到了磁粒研磨加工TC4弯管最优工艺参数配置组合：磁极转速为570 r/min，加工间隙为2.0 mm，磨料粒径为178 μm（80目），进给速度为80 mm/min。结论 使用BP神经网络创建的反映TC4弯管内表面粗糙度与加工TC4弯管内表面工艺参数之间的映射模型具有较好的精度，同时应用遗传算法全局寻优得到了最佳的工艺参数，是一种准确度较高的优化磁粒研磨TC4弯管内表面加工工艺参数的新方法。 |
| 英文摘要: |
| The work aims to optimize the process parameters of magnetic abrasive finishing to improve the magnetic abrasive finishing quality and processing efficiency of the inner surface of the TC4 elbow. Firstly, the optimum surface quality was set as an optimization target. Secondly, the four main process parameters affecting the inner surface quality of the magnetic abrasive finishing were taken as an optimization object, the number of nodes in the hidden layers of the neural network to be set up was tested to select optimal value. Thirdly, a nonlinear mapping model that reflects the internal surface roughness and main process parameters of the TC4 elbow was establish. Finally, by using genetic algorithm, the optimal surface roughness of TC4 elbow and the optimal technological parameter combination of magnetic abrasive finishing for TC4 elbow was obtained, and the accuracy of the prediction results was verified by experiments. By establishing a BP neural network with a structure of 4-5-1 and predicting with genetic algorithm, the optimal process parameter configuration of the TC4 elbow for magnetic abrasive finishing was obtained as follows: the magnetic pole speed was 570 r/min, the machining gap was 2.0 mm, the diameter of the abrasive was 178 μm (80 meshes) and the feed rate was 80 mm/min. The mapping model created by the BP neural network to reflect the surface roughness of the inner surface of the TC4 elbow and the process parameters of the inner surface of the TC4 elbow has good precision, and the optimum process parameter is obtained by global optimization with genetic algorithm. It is a new method with high accuracy to optimize the processing parameters of the inner surface of TC4 elbow of magnetic abrasive finishing. |
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