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 , Yizhang Luo , Xin Jiang , Cheng Su , 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.
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.