[1]李 湾,杨向东,明兴祖,等.面齿轮飞秒激光精修齿面粗糙度预测研究[J].机械与电子,2025,(08):23-29.
　LI Wan,YANG Xiangdong,MING Xingzu,et al.Research on Prediction of Tooth Surface Roughness for Femtosecond Laser Finishing of Face Gear[J].Machinery & Electronics,2025,(08):23-29.
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面齿轮飞秒激光精修齿面粗糙度预测研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年08期
页码:
23-29
栏目:
研究与设计
出版日期:
2025-08-25

文章信息/Info

Title:
Research on Prediction of Tooth Surface Roughness for Femtosecond Laser Finishing of Face Gear
文章编号:
1001-2257 ( 2025 ) 08-0023-07
作者:
李 湾 1 ; 杨向东 2 ; 明兴祖 2 ; 3 ; 伍昆军 2 ; 吴 陶 3
1. 湖南汽车工程职业大学机电工程学院,湖南株洲412000 ;
2. 广州华立学院机电工程学院,广东广州511325 ;
3. 湖南工业大学机械工程学院,湖南株洲412007?

Author(s):
LI Wan1 ;  YANG Xiangdong2 ;  MING Xingzu2 ;  3 ;  WU Kunjun2 ;  WU Tao3
( 1.School of Mechanical and Electrical Engineering , Hunan Automotive Engineering Vocational University , Zhuzhou 412000 , China ;?
2.School of Mechanical and Electrical Engineering , Guangzhou Huali College , Guangzhou 511325 , China ;?
3.School of Mechanical Engineering , Hunan University of Technology , Zhuzhou 412007 , China )
关键词:
飞秒激光精修; 面齿轮; 支持向量机回归; 齿面粗糙度; 预测
Keywords:
femtosecond laser finishing ;  face gear ;  support vector regression ;  tooth surface roughness ;  prediction
分类号:
TP18 ;TH132.41
文献标志码:
A
摘要:
采用齿面网格化处理和粗糙度测量技术,得到 4 个主要因素影响的粗糙度预测的训练集和测试集序列,考虑飞秒激光精修面齿轮实验的小样本特点,选择支持向量机回归 SVR 模型方法进行齿面粗糙度预测。通过对 83 组实验数据进行预处理,包括 48 组训练集和 35 组测试集,比较分析得出采用多项式核函数进行训练的总体预测误差较小,对测试集进行齿面粗糙度预测的评价参数(EMSE 、 ERMSE 、 EMAE 、 EMAPE )均保持较低水平,实现了较高精度的预测,说明该预测模型适用于飞秒激光精修齿面粗糙度的预测,特别是对粗糙度在 0.2~0.5 μm 区间的预测效果佳,为面齿轮齿面粗糙度预测提供了有效的技术参考。
Abstract:
By using tooth surface meshing processing and roughness measurement techniques , the training sets and test sets sequences of roughness prediction influenced by four main factors are obtained. Considering the small sample characteristics of face gear experiments of femtosecond laser finishing , the support vector machine regression SVR model is selected for tooth surface roughness prediction.By preprocessing 83 experimental data , which were divided into 48 training sets and 35 test sets , the comparative analysis shows that the overall prediction error of training with polynomial kernel function is relatively small , and the evaluation parameters ( EMSE , ERMSE , EMAE , EMAPE ) of tooth surface roughness prediction for the test set are kept at a low level , thus achieving a relatively high precision prediction.It shows that the prediction model is suitable for predicting tooth surface roughness in precision finishing of femtosecond laser , especially for roughness in the range of 0.2~0.5 μm , which provides an effective technical reference for predicting tooth surface roughness of face gear.

参考文献/References:

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相似文献/References:

[1]李 湾１,李学坤２,明 瑞２,等.面齿轮磨削力建模与工艺影响分析[J].机械与电子,2021,(07):3.
　LI Wan,LI Xuekun,MING Rui,et al.Modeling and Processing Analysis of Grinding Force of Face Gear[J].Machinery & Electronics,2021,(08):3.

备注/Memo

备注/Memo:
收稿日期: 2025-03-11
基金项目:湖南省自然科学基金资助项目( 2023JJ50207 );广东省普通高校重点领域专项( 2023ZDZX3050 ,2023ZDZX3051 );湖南省教育厅科学研究优秀青年项目( 22B0994 );国家自然科学基金资助项目( 51975192 )
作者简介:李 湾 ( 1989- ),女,湖北汉川人,工学硕士,讲师,研究方向为激光加工技术;杨向东 ( 1980- ),男,河南邓州人,工学硕士,副教授,研究方向为精密加工技术;明兴祖 ( 1964- ),男,湖南常德人,博士,教授,硕士研究生导师,研究方向为微纳与绿色制造技术,通信作者, E-mail : mxz9036@126.com 。
更新日期/Last Update: 2025-09-04