A quality correlation algorithm for tolerance synthesis in manufacturing operations. (March 2016)

• Record Type: Journal Article
• Authors: Ransing, R.S.; Batbooti, R.S.; Giannetti, C.; Ransing, M.R.
• Is Part Of: Computers & industrial engineering. Volume 93(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-03
• Subjects: 7Epsilon -- Six sigma -- No-Fault-Found product failures -- In-tolerance faults -- In-process quality improvement -- Cause and effect analysis; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.12.008
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An approach to embed ISO 9001:2015s risk based thinking for in-process quality improvement is proposed. The algorithm determines optimal and avoid ranges within the process variation including process interactions. It is shown how the technique can be used to satisfy the requirements of Clause 6.1 of the ISO9001:2015 standard. Abstract: 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 ISO9001:2015s risk based thinking approach. The corresponding factor and response variable pairs are chosen by analysing the mixed data set formulation proposed by Giannetti et al. (2014) and co-linearity index algorithm proposed by Ransing, Giannetti, Ransing, and James (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.