Application of a Multivariate Process Control Technique for Set-Up Dominated Low Volume Operations

Cox, S.; Anderson, S.; Gray, N.; Vogt, O.; Kotsialos, A.; Goh, Yee Mey; Case, Keith

Abstract

In traditional high-volume manufacturing applications, the timing of control adjustments to processes is based on parametric Statistical Process Control (SPC) methods, such as Shewhart X & R charts. In high-value, high-complexity and low-volume industries, where production runs are in the order of tens rather than thousands, traditional SPC approaches are not easily applicable. A manufactured component's complexity, with multiple critical features to monitor, increases the difficulty for a process operator to maintain all of them within their design tolerances. In response to this, this paper presents a framework of nonparametric SPC, called multivariate Set-Up Process Algorithm (mSUPA), for managing control adjustment when required. mSUPA uses a simple to interpret traffic light system for alerting process operators when an adjustment is required. mSUPA is underpinned by multivariate statistics and probability theory for validating a process set up. The case of mSUPA application to a real industry process is discussed.

Citation

Cox, S., Anderson, S., Gray, N., Vogt, O., Kotsialos, A., Goh, Y. M., & Case, K. (2016, September). Application of a Multivariate Process Control Technique for Set-Up Dominated Low Volume Operations. Presented at 14th International Conference on Manufacturing Research, Loughborough, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 14th International Conference on Manufacturing Research
• Start Date: Sep 6, 2016
• End Date: Sep 8, 2016
• Publication Date: Sep 1, 2016
• Deposit Date: Feb 5, 2017
• Publicly Available Date: Feb 6, 2017
• Pages: 535-540
• Series Title: Advances in transdisciplinary engineering
• Series Number: 3
• Series ISSN: 2352-751X,2352-7528
• Book Title: Advances in manufacturing technology XXX : proceedings of the 14th International Conference on Manufacturing Research, incorporating the 31st National Conference on Manufacturing Research, September 6-8, 2016, Loughborough University, UK.
• ISBN: 9781614996675
• DOI: https://doi.org/10.3233/978-1-61499-668-2-535
• Public URL: https://durham-repository.worktribe.com/output/1149127