中国机械工程2025，Vol.36Issue(6)：1261-1268,8.DOI:10.3969/j.issn.1004-132X.2025.06.013
基于多源信息融合和集成学习的薄壁件铣削加工变形误差预测
Thin-walled Workpiece Milling Deformation Error Prediction Based on Multi-source Information Fusion and Ensemble Learning
摘要
Abstract
In practical machining processes,the dimensional accuracy of thin-walled workpiece was significantly affected by multiple factors including cutting forces,forced vibrations,chatter phe-nomena,geometric characteristics of workpiece and material properties,rendering deformation pre-diction and control particularly challenging.A multi-source information fusion method for deformation error prediction in thin-walled workpiece milling processes was developed.Machining parameters,vi-bration signals,and other relevant data were integrated to establish a deformation error prediction model through Stacking ensemble learning methodology,with comprehensive experimental validation performed.Comparative analyses reveal that the constructed model demonstrates superior robustness,higher accuracy,and enhanced practicality when compared with conventional data-driven prediction methods.关键词
薄壁件/铣削加工/变形误差/多源信息融合/集成学习Key words
thin-walled workpiece/milling process/deformation error/multi-source information fusion/ensemble learning分类
机械制造引用本文复制引用
尹佳,郑健,刘尧,贾保国,段晓蕊..基于多源信息融合和集成学习的薄壁件铣削加工变形误差预测[J].中国机械工程,2025,36(6):1261-1268,8.基金项目
陕西省科技重大专项(2019zdzx01-01-02) （2019zdzx01-01-02）
