Uncertainty quantification and dynamic characteristics identification for predicting milling stability lobe based on surrogate model

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Guanying Huo , Yizhang Luo , Xin Jiang , Cheng Su , Zhiming Zheng
{"title":"Uncertainty quantification and dynamic characteristics identification for predicting milling stability lobe based on surrogate model","authors":"Guanying Huo ,&nbsp;Yizhang Luo ,&nbsp;Xin Jiang ,&nbsp;Cheng Su ,&nbsp;Zhiming Zheng","doi":"10.1016/j.rcim.2024.102922","DOIUrl":null,"url":null,"abstract":"<div><div>The prediction of chatter-free machining parameters suffers from inaccuracies in dynamic milling model inputs and simplification in milling process modeling, which may lead to a significant mismatch between the predicted stability boundary of the mathematical model and actual physical experiments. This study proposes a novel stability analysis method for milling operations based on a surrogate model that considers the effects of both uncertainties and variations in model inputs. The uncertainties of inputs are quantified by considering the statistical distribution of both cutting force coefficients and modal parameters, and the variations of modal parameters are identified through operational modal analysis (OMA). Furthermore, the proposed method introduces the statistical Kriging surrogate model of the spectral radius in the model parameter domain to propagate uncertainties to the stability lobe diagram (SLD). The confidence interval of the predicted stability boundary is obtained using the estimated prediction variance of the generated Kriging surrogate model. Finally, a mathematical measurement of SLD quality is presented, based on the similarities both in shape and position between the predicted and experimental stability boundaries. The cutting experimental verification and numerical analysis indicated that the robustness and accuracy of the SLD are considerably improved compared to the state-of-the-art methods. Thus, the proposed method holds significant promise for practical engineering applications in controlling milling stability on machining equipment such as CNC tools and industrial robots.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"93 ","pages":"Article 102922"},"PeriodicalIF":9.1000,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524002096","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

The prediction of chatter-free machining parameters suffers from inaccuracies in dynamic milling model inputs and simplification in milling process modeling, which may lead to a significant mismatch between the predicted stability boundary of the mathematical model and actual physical experiments. This study proposes a novel stability analysis method for milling operations based on a surrogate model that considers the effects of both uncertainties and variations in model inputs. The uncertainties of inputs are quantified by considering the statistical distribution of both cutting force coefficients and modal parameters, and the variations of modal parameters are identified through operational modal analysis (OMA). Furthermore, the proposed method introduces the statistical Kriging surrogate model of the spectral radius in the model parameter domain to propagate uncertainties to the stability lobe diagram (SLD). The confidence interval of the predicted stability boundary is obtained using the estimated prediction variance of the generated Kriging surrogate model. Finally, a mathematical measurement of SLD quality is presented, based on the similarities both in shape and position between the predicted and experimental stability boundaries. The cutting experimental verification and numerical analysis indicated that the robustness and accuracy of the SLD are considerably improved compared to the state-of-the-art methods. Thus, the proposed method holds significant promise for practical engineering applications in controlling milling stability on machining equipment such as CNC tools and industrial robots.
基于代理模型的铣削稳定性预测的不确定性量化与动态特性识别
无颤振加工参数的预测存在动态铣削模型输入不准确和铣削过程建模简化的问题,这可能导致数学模型预测的稳定性边界与实际物理实验结果存在明显的不匹配。本研究提出了一种新的铣削操作稳定性分析方法,该方法基于考虑不确定性和模型输入变化影响的替代模型。通过考虑切削力系数和模态参数的统计分布来量化输入的不确定性,并通过运行模态分析(OMA)识别模态参数的变化。此外,该方法在模型参数域引入谱半径的统计Kriging代理模型,将不确定性传递到稳定性波瓣图(SLD)中。利用所生成的Kriging代理模型估计的预测方差,得到了预测稳定性边界的置信区间。最后,基于预测边界和实验边界在形状和位置上的相似性,提出了SLD质量的数学测量方法。切削实验验证和数值分析表明,与现有方法相比,该方法的鲁棒性和精度得到了显著提高。因此,该方法在控制数控刀具和工业机器人等加工设备的铣削稳定性方面具有重要的实际工程应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信