Capability-based remaining useful life prediction of machining tools considering non-geometry and tolerancing features with a hybrid model

ABSTRACT

Machining tools are vital components of intelligent manufacturing systems whose state and remaining useful life (RUL) determine product quality. Specifically, the wearing of tool reduces its capability of production yield and diminishes product quality. Therefore, a capability-based RUL prediction approach is proposed in this paper to thoroughly evaluate the state and RUL of machining tools. First, the connotation of tool capability is discussed, and a framework for quality assurance capability-based RUL prediction is proposed. Product quality, which can be used to assess the capability of tool, is modelled and expanded to consider non-geometric dimensioning and tolerancing (non-GD&T) features based on the classic geometric dimensioning and tolerancing (GD&T) system. Second, a physics-based model of process is developed to estimate the non-GD&T features and calculate tool wear. Third, a hybrid data-driven and physics-based model is developed to quantitatively assess the capability of tool based on the comprehensive quality estimation. Finally, a case study of rolling machining tool is carried out to verify the effectiveness and proactiveness of the proposed framework, and the final result highlights its rationality and accuracy in estimating the RUL of machining tools with better interpretation.

KEYWORDS:

• Machining process
• remaining useful life
• hybrid model
• tool capability
• non-geometric dimensioning and tolerancing features

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work described in this paper was supported by National Natural Science Foundation of China (71971181, 72032005 and 72071007) and by Research Grant Council (RGC) of Hong Kong (11203519, 11200621, and 9360163). It is also funded by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).

Notes on contributors

Yuqing Zhang

Yuqing Zhang is currently a PhD student in the Department of Advanced Design and Systems Engineering, City University of Hong Kong. He has received the B.S. and M.S. degree in the School of Reliability and Systems Engineering from Beihang University, Beijing, in 2016 and 2019, respectively. His doctoral research interests are the non-geometric dimensioning data simulation analysis, prognostics, and health management of intelligent manufacturing system, and he has published over five papers on international journals and conferences including International Journal of Production Research, etc.

Min Xie

Min Xie is the Chair Professor of Industrial Engineering in the City University of Hong Kong. He received the M.Sc. Engineering Physics from Royal Institute of Technology, Stockholm, Sweden, in 1984, and the Ph.D. degree in Quality Technology from the Linkoping University, Sweden, in 1987. He was the Acting Head (SEEM at CityU) during fall 2011. He also served as Associate Dean at College of Science and Engineering at CityU. He has published over 300 journal papers and 100 conference papers. He currently serves as editor, associate editor, and on the editorial board of over 15 international journals. He has served as conference chair in a number of conferences and delivered keynote speeches at many others.

Yihai He

Yihai He is a professor (PhD supervisor) at the School of Reliability and Systems Engineering, Beihang University, People’s Republic of China. He received the Ph.D. degree in manufacturing and systems engineering from Beihang University in 2006. His main research interests are reliability in manufacturing, advanced quality engineering techniques, and Prognostic and Health Management (PHM) of intelligent manufacturing system, and he has published over 100 papers on international journals and conferences including IEEE Transactions on Reliability, Reliability Engineering & System Safety, etc. His homepage is http://qpr.buaa.edu.cn.

Xiao Han

Xiao Han received his Bachelor of Engineering degree in safety engineering from Beihang University in 2016. He is a Doctor of Philosophy candidate at the School of Reliability and Systems Engineering, Beihang University, People’s Republic of China. His main research interests are model-based PHM, reliability modelling, and predictive maintenance of intelligent manufacturing systems, and he has published over 10 papers on international journals and conferences including Reliability Engineering & System Safety, International Journal of Production Research, etc.