基于多源信息融合和集成學(xué)習(xí)的薄壁件 銑削加工變形誤差預(yù)測(cè)

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中圖分類號(hào)：TH17

DOI：10.3969/j.issn.1004-132X.2025.06.013 開放科學(xué)（資源服務(wù)）標(biāo)識(shí)碼（OSID）：

Thin-walled Workpiece Milling Deformation Error Prediction Based on Multi-source Information Fusion and Ensemble Learning

YIN Jia1ZHENG Jian2 LIU Yao3* JIA Baoguo1DUAN Xiaorui1 1.AVIC Xi'an Aircraft Industry Group Company Ltd.，Xi'an，710089 2.School of Mechano-Electronic Engineering，Xidian University，Xi'an，710071 3.School of Communications and Information Engineering，Xi'an University of Posts and Telecommunications，Xi'an，710121

Abstract： In practical machining processes，the dimensional accuracy of thin-walled workpiece was significantly afected by multiple factors including cutting forces， forced vibrations，chatter phenomena，geometric characteristics of workpiece and material properties，rendering deformation prediction and control particularly challnging.A multi-source information fusion method for deformation error prediction in thin-walled workpiece miling processes was developed. Machining parameters，vibration 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.

Key words： thin-walled workpiece；milling process;； deformation error； multi-source information fusion；ensemble learning

0 引言

薄壁零件廣泛應(yīng)用于電子信息、航空航天等領(lǐng)域，是一類非常重要的典型零部件，但其剛度較低，在銑削加工過程中受切削力、強(qiáng)迫振動(dòng)、顫振等多方面因素影響極易出現(xiàn)較大的變形誤差，導(dǎo)致最終產(chǎn)品加工質(zhì)量下降。（剩余12550字）