Product quality monitoring approach considering non-geometric dimensioning data with rapid production process simulation

ABSTRACT

Workpiece is composed of its profile and material. The non-geometric dimensioning interior features of products, such as residual stress, may determine the workpiece material properties and the product performance, which should be included into manufacture quality control. The influence of process parameter uncertainty and fluctuation on state of interior property could not be neglected. In this paper, a novel framework of monitoring workpiece quality considering interior features is presented. In this framework, product quality of interior property is considered. In this framework, a rapid simulation method is proposed to acquire the state of product interior features. According to actual process parameter measured by sensors, this simulation method could calculate simulation results of the workpiece’s non-geometric dimensioning interior features. By extracting data from off-line database and creating an on-line simulation model, the proposed method can finish the simulation of workpiece interior features rapidly. This simulation algorithm is proposed and discussed mathematically based on the multi-subdomain coupling method, and the simulation error is estimated. With the simulation method, this framework could supplement production quality assessment and control criteria. A case study of a rolling production process shows that this method is effective and could be used to monitor workpiece quality in-process.

KEYWORDS:

• Quality monitoring
• process simulation
• non-geometric dimensioning data
• interior features
• Finite Element Method(FEM)

Acknowledgment

The work described in this paper was supported by a grant from Research Grant Council of Hong Kong under a theme-based project grant (T32-101/15-R) and a GRF (CityU 11203519), and also by National Natural Science Foundation of China (71971181& 72071007).

Disclosure statement

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

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71971181,72071007]; Research Grant Council of Hong Kong under a theme-based project grant: [Grant Number T32-101/15-R]; General Research Fund (GRF): [Grant Number CityU 11203519].

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 focused on the non-geometric dimensioning data simulation analysis, prognostics and health management of intelligent manufacturing system.

Min Xie

Min Xie is the Chair Professor of Industrial Engineering in the City University of Hong Kong. Prof.Xie 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.

Prof Xie 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 an associate professor (PhD supervisor) at the School of Reliability and Systems Engineering, Beihang University in 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 PHM of intelligent manufacturing system, he has published over 100 papers on international journals and conferences including IEEE Transactions on Reliability, Reliability Engineering & System safety and etc. His homepage is: http://qpr.buaa.edu.cn.

Wei Dai

Wei Dai is an associate professor (PhD supervisor) at the School of Reliability and Systems Engineering, Beihang University in People’s Republic of China. He received the Ph.D degree in manufacturing and systems engineering from Beihang University in 2011.

His main research interests are process reliability, quality data analytics and manufacturing defect analysis, he has published over 60 papers on international journals and conferences including Computers & Industrial Engineering, International Journal of Computer Integrated Manufacturing and etc.