Authors: Mohammadhossein Amini; Shing I. Chang
Addresses: Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, 66506, USA ' Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, 66506, USA
Abstract: Recent advances in cyber-physical systems and the Internet of things (IoT) have enabled the possible development of smart production systems. However, the complexity of such a system has posed significant challenges for traditional quality engineering methods, especially in monitoring and diagnosis of system performance. The traditional practices for monitoring or controlling multistage systems either treat each stage as an individual entity or model all stages as a whole. The formal approach mainly focuses on the most critical stages while ignores information from the other stages. In contrast, the latter approach attempts to build one model to account for all stages. In a complex production system, this latter approach is impractical, if not impossible. This research provides a control strategy by proposing an intelligent process monitoring system for high dimensional multistage processes using predictive models built from historical data. A repository dataset is used to demonstrate the implementation of the proposed framework.
Keywords: multistage manufacturing systems; data-driven; process monitoring; smart manufacturing; quality engineering; machine learning; predictive modelling; semiconductor manufacturing.
International Journal of Mechatronics and Manufacturing Systems, 2020 Vol.13 No.4, pp.299 - 322
Received: 02 Jan 2020
Accepted: 26 Aug 2020
Published online: 31 Dec 2020 *