Hao Sun, Sheng-Qiang Zhao, Fang-Yu Peng, Rong Yan, Xiao-Wei Tang
{"title":"Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes","authors":"Hao Sun, Sheng-Qiang Zhao, Fang-Yu Peng, Rong Yan, Xiao-Wei Tang","doi":"10.1007/s40436-024-00520-1","DOIUrl":null,"url":null,"abstract":"<div><p>There are a large number of low rigidity parts in the aerospace field, and how to achieve high-performance manufacturing in their multistage machining processes has received increasing attention. Optimizing the distribution of machining allowance and machining parameters is one of the most convenient ways to improve the machining performance of these parts. In this paper, firstly, considering the machining accuracy and machining efficiency comprehensively, the error efficiency cooperation coefficient of low rigidity parts during machining is established. Based on the semi-parametric regression theory and measured data, the machining error transfer factor within the cooperation coefficient is calibrated. Secondly, the machining optimization strategy based on the Bayesian framework is proposed, and the optimization of multiple machining parameters is realized with the goal of minimizing the error efficiency cooperation coefficient. Finally, the optimization software of machining processes of low rigidity parts for engineering application is developed. In the verification experiments of blade parts, the error efficiency cooperation coefficient is reduced to 0.032 1 after optimization, and the average improvement of machining errors of all measured points is <span>\\(14.31\\,{\\upmu {\\text{m}}}\\)</span>. Besides, the above method is applied to low rigidity shaft parts, and the effectiveness of the proposed method is further verified.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"13 3","pages":"584 - 605"},"PeriodicalIF":3.8000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s40436-024-00520-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
There are a large number of low rigidity parts in the aerospace field, and how to achieve high-performance manufacturing in their multistage machining processes has received increasing attention. Optimizing the distribution of machining allowance and machining parameters is one of the most convenient ways to improve the machining performance of these parts. In this paper, firstly, considering the machining accuracy and machining efficiency comprehensively, the error efficiency cooperation coefficient of low rigidity parts during machining is established. Based on the semi-parametric regression theory and measured data, the machining error transfer factor within the cooperation coefficient is calibrated. Secondly, the machining optimization strategy based on the Bayesian framework is proposed, and the optimization of multiple machining parameters is realized with the goal of minimizing the error efficiency cooperation coefficient. Finally, the optimization software of machining processes of low rigidity parts for engineering application is developed. In the verification experiments of blade parts, the error efficiency cooperation coefficient is reduced to 0.032 1 after optimization, and the average improvement of machining errors of all measured points is \(14.31\,{\upmu {\text{m}}}\). Besides, the above method is applied to low rigidity shaft parts, and the effectiveness of the proposed method is further verified.
期刊介绍:
As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field.
All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.