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A quality correlation algorithm for tolerance synthesis in manufacturing operations
Computers & Industrial Engineering, Volume: 93, Pages: 1 - 11
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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 optim...
|Published in:||Computers & Industrial Engineering|
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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.
7Epsilon, Six Sigma, No-Fault-Found product failures, in-tolerance faults, in-process quality improvement, and cause and effect analysis.
College of Engineering