A quality correlation algorithm for tolerance synthesis in manufacturing operations

The clause 6.1 of the ISO9001:2015 quality standard requires organisations to take specific actions to determine and address risks and opportunities in order to minimize undesired effects in the process and achieve process improvement. This paper proposes a new quality correlation algorithm to optimise tolerance limits of process variables across multiple processes. The algorithm uses reduced p-dimensional principal component scores to determine optimal tolerance limits and also embeds ISO9001:2015’s risk based thinking approach. The corresponding factor and response variable pairs are chosen by analysing the mixed data set formulation proposed by Giannetti etl al. (2014) and co-linearity index algorithm proposed by Ransing et al. (2013). The goal of this tolerance limit optimisation problem is to make several small changes to the process in order to reduce undesired process variation. The optimal and avoid ranges of multiple process parameters are determined by analysing in-process data on categorical as well as continuous variables and process responses being transformed using the risk based thinking approach. The proposed approach has been illustrated by analysing in-process chemistry data for a nickel based alloy for manufacturing cast components for an aerospace foundry. It is also demonstrated how the approach embeds the risk based thinking into the in-process quality improvement process as required by the ISO9001:2015 standard

A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis

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