COX, STEVEN (2015) Simulation and Control of Univariate and Multivariate Set-Up Dominant Process. Doctoral thesis, Durham University.
|PDF - Accepted Version|
This thesis explores the use of statistically valid process improvement tools in low-volume applications. Setting out the following research questions: How can the Six Sigma Measure and Analyse phases of a chronic quality problem be statistically validated in a low-volume process? How can a statistically valid approach for process control be implemented in a low-volume process? And how can this tool be extended to ﬁt multivariate processes and can the calculation of control parameter adjustments be automated?
In answer, the thesis presents an enhanced PROcess VAriation Diagnosis Tool (PROVADT) method, driving a Six Sigma improvement project through the Measure and Analyse phases. PROVADT provides a structured sampling plan to perform a Multi-Vari study, Isoplot, Gage R&R and Provisional Process Capability in as few as twenty samples and eighty measurements, making the technique suited to low-volume applications. The enhanced PROVADT method provides a Gage R&R without confounded variation sources, as was the case in the original method, and its practical application was demonstrated through two case studies.
Process control tools for low-volume, high-variety manufacturing applications were developed. An adjustable traﬃc-light chart, with control limits linked to tolerance and simple decision rules, was used for monitoring univariate processes. This tool, the Set-Up Process
Algorithm (SUPA), uses probability theory to provide 98% conﬁdence that the process is operating at a pre-speciﬁed minimum level of Cp in as few as ﬁve samples. SUPA was extended to deal with high-complexity applications, resulting in multivariate SUPA (mSUPA). mSUPA maintains SUPA’s principles, but presents the information about multiple process features on one chart, rather than multiple univariate charts. To supplement the mSUPA tool, a theoretical method for calculating optimal process adjustment when a multivariate process is oﬀ-target was introduced, combining discrete-event simulation and numerical optimisation to calculate adjustments.
|Item Type:||Thesis (Doctoral)|
|Award:||Doctor of Philosophy|
|Faculty and Department:||Faculty of Science > Engineering and Computing Science, School of (2008-2017)|
|Copyright:||Copyright of this thesis is held by the author|
|Deposited On:||19 Jan 2016 10:34|