[1]唐嘉斌,郭龙文,王 天,等.doi: 10.3969/j.issn.1001-3849.2025.07.0011基于DBO-LSTM的磁粒研磨SUS304细长管内表面工艺参数优化研究[J].电镀与精饰,2025,(07):64-73.
　Tang Jiabin,Guo Longwen,Wang Tian,et al.Optimization study of process parameters of magnetic particle grinding SUS304 slender tube inner surface based on DBO-LSTM[J].Plating & Finishing,2025,(07):64-73.
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doi: 10.3969/j.issn.1001-3849.2025.07.0011基于DBO-LSTM的磁粒研磨SUS304细长管内表面工艺参数优化研究()

《电镀与精饰》[ISSN:1001-3849/CN:12-1096/TG]

卷:
期数:
2025年07
页码:
64-73
栏目:
出版日期:
2025-07-31

文章信息/Info

Title:
Optimization study of process parameters of magnetic particle grinding SUS304 slender tube inner surface based on DBO-LSTM
作者:
唐嘉斌1; 2; 郭龙文3; 王 天1; 2; 肖春芳4; 矫彦婷1; 2; 韩 冰1; 2*
(1. 辽宁科技大学 机械工程与自动化学院,辽宁 鞍山 114051 ;2. 辽宁省复杂工件表面特种加工重点实验室,辽宁 鞍山 114051 ;3. 新乡航空工业集团有限公司,河南 新乡 453000 ;4. 长沙航空职业技术学院, 湖南 长沙 410124)
Author(s):
Tang Jiabin1; 2;  Guo Longwen3;  Wang Tian1; 2;  Xiao Chunfang4;  Jiao Yanting1; 2;  Han Bing1; 2*
(1. College of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China; 2. Liaoning Provincial Key Laboratory of Special Machining of Complex Workpiece Surfaces, Anshan 114051; 3. Xinxiang Aviation Industry (Group) Co., Ltd., Xinxiang 453000, China; 4. Changsha Aviation Vocational and Technical College, Changsha 410124, China)
关键词:
磁粒研磨; SUS304细长管; DBO-LSTM预测模型; 表面粗糙度值; 工艺参数
Keywords:
magnetic particle grinding;  SUS304 slender tube;  DBO-LSTM prediction model;  surface roughness value;  process parameters
分类号:
TG176
文献标志码:
A
摘要:
利用磁粒研磨技术对SUS304不锈钢细长管内表面光整加工时，由于影响研磨效果的因素众多，导致最佳工艺参数难以确定。本文设计4因素4水平正交试验，分析主轴转速、磁极进给速度、粗精加工磨粒粒径组合和粗精加工时间比对表面粗糙度值的影响。并构建蜣螂算法(DBO)优化长短期记忆神经网络(LSTM)的表面粗糙度预测模型，预测模型的拟合优度R2为0.976 9，均方根误差(RMSE)为0.017 9，平均绝对误差(MAE)为0.016 2。再次利用DBO算法进行全局寻优，得到最佳工艺参数组合为：主轴转速2 715 r/min，外部磁极进给速度为4 mm/s，粗精加工磨粒粒径组合为80目和100目，粗精加工时间比为1.35∶1，预测的表面粗糙度Ra为0.080 μm。对上述工艺参数进行微调并进行试验，结果表明：得到的表面粗糙度Ra为0.078 μm，与预测值的相对误差RE约为2.56%。应用最佳工艺参数组合进行试验，在降低SUS304细长管内表面粗糙度值的同时，还能改善其表面微观形貌，提升加工效率。
Abstract:
When magnetic grain grinding technology is utilized for SUS304 stainless steel slender tube internal surface finishing processing, the optimal process parameters are difficult to determine due to the many factors affecting the grinding effect. In this paper, a 4-factor and 4-level orthogonal test is designed to analyze the effects of spindle speed, external magnetic pole feed rate, roughing and finishing grain size combination and roughing and finishing time ratio on the surface roughness value. The four process parameters were taken as input values, and the surface roughness values were taken as output values, and the surface roughness prediction model of dung beetle algorithm (DBO) optimized long and short-term memory neural network (LSTM) was constructed, and the goodness of fit of the prediction model was R2 of 0.9769, the root mean squared error (RMSE) was 0.0185, and the mean absolute error (MAE) was 0.0164. The global optimization was performed by using DBO algorithm again, and the global optimization was obtained by using DBO algorithm, and the global optimization was carried out by using DBO algorithm again. algorithm for global optimization, the optimal combination of process parameters was obtained as follows: spindle speed of 2,715 r/min, external magnetic pole feed rate of 4 mm/s, roughing and finishing grain size combinations of 80 mesh and 100 mesh, roughing and finishing time ratio of 1.35∶1, and the predicted surface roughness value Ra of 0.080 μm. The above process parameters were fine-tuned and tested. The above process parameters were fine-tuned and tested, and the results showed that the surface roughness value Ra obtained was 0.078 μm, and the relative error RE with the predicted value was about 2.56%. The optimum combination of process parameters obtained was tested to improve the surface micro-morphology and enhance the machining efficiency while reducing the roughness value of the inner surface of SUS 304 slender tubes.

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更新日期/Last Update: 2025-07-08