Xiaohui Jiang , Fangxu Hu , Chongjun Wu , Chao Luo , Zhijian Lin , Ermakov Boris Sergeevich
{"title":"Deformation prediction model for milling residual stresses in complex thin-walled parts with variable curvature","authors":"Xiaohui Jiang , Fangxu Hu , Chongjun Wu , Chao Luo , Zhijian Lin , Ermakov Boris Sergeevich","doi":"10.1016/j.jmapro.2025.02.032","DOIUrl":null,"url":null,"abstract":"<div><div>Complex thin-walled parts with variable curvature are crucial in aerospace, yet their manufacturing-induced residual stresses often lead to deformation, posing challenges in surface residual stress measurement and deformation prediction due to the complex geometric coordinate system. This study develops a model for predicting the distribution of subsurface residual stresses and the resulting deformation. By integrating the segmented polynomial fitting and random forest regression methods, a subsurface residual stress distribution prediction model is established and verified through experiments on machining and stress detection of such parts. Additionally, a special fixture for stress detection is designed. Based on ABAQUS software, a deformation simulation model considering residual stress is developed. Through continuous two-month monitoring of parts with different processing parameters, it is found that the simulation results align with the experimental trend, with an error range of 6.7 %–12.4 %. Eventually, a deformation prediction model based on process parameters is constructed, which can effectively predict the deformation of these parts and provides a theoretical foundation for deformation control.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"139 ","pages":"Pages 156-171"},"PeriodicalIF":6.1000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Processes","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1526612525001744","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Complex thin-walled parts with variable curvature are crucial in aerospace, yet their manufacturing-induced residual stresses often lead to deformation, posing challenges in surface residual stress measurement and deformation prediction due to the complex geometric coordinate system. This study develops a model for predicting the distribution of subsurface residual stresses and the resulting deformation. By integrating the segmented polynomial fitting and random forest regression methods, a subsurface residual stress distribution prediction model is established and verified through experiments on machining and stress detection of such parts. Additionally, a special fixture for stress detection is designed. Based on ABAQUS software, a deformation simulation model considering residual stress is developed. Through continuous two-month monitoring of parts with different processing parameters, it is found that the simulation results align with the experimental trend, with an error range of 6.7 %–12.4 %. Eventually, a deformation prediction model based on process parameters is constructed, which can effectively predict the deformation of these parts and provides a theoretical foundation for deformation control.
期刊介绍:
The aim of the Journal of Manufacturing Processes (JMP) is to exchange current and future directions of manufacturing processes research, development and implementation, and to publish archival scholarly literature with a view to advancing state-of-the-art manufacturing processes and encouraging innovation for developing new and efficient processes. The journal will also publish from other research communities for rapid communication of innovative new concepts. Special-topic issues on emerging technologies and invited papers will also be published.