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A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis / Raed S. Batbooti; R.S. Ransing; M.R. Ransing

Computers & Industrial Engineering

Swansea University Author: Ransing, Rajesh

  • Accepted Manuscript under embargo until: 3rd March 2018

Abstract

A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard’s risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently, Ransing, Batbooti, Giannetti, and Ransing (2016) pro...

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Published in: Computers & Industrial Engineering
ISSN: 0360-8352
Published: 2016
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa29741
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Abstract: A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard’s risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently, Ransing, Batbooti, Giannetti, and Ransing (2016) proposed a quality correlation algorithm (QCA) for risk based tolerance synthesis. The quality correlation algorithm is based on the principal component analysis (PCA) and a co-linearity index concept (Ransing, Giannetti, Ransing, & James 2013). The uncertainty in QCA results on mixed data sets is quantified and analysed in this paper.The uncertainty is quantified using a bootstrap sampling method with bias-corrected and accelerated confidence intervals. The co-linearity indices use the length and cosine angles of loading vectors in a p-dimensional space. The uncertainty for all p-loading vectors is shown in a single co-linearity index plot and is used to quantify the uncertainty in predicting optimal tolerance limits. The effects of re-sampling distributions are analysed. The QCA tolerance limits are revised after estimating the uncertainty in limits via bootstrap sampling. The proposed approach has been demonstrated by analysing in-process data from a previously published case study.
College: College of Engineering