Unified Uncertainty-based Quality Improvement

Within manufacturing different methodologies are used to quantify uncertainty. These include uncertainty evaluation (GUM), measurement systems analysis (MSA) and statistical process control (SPC). The different methods are used at different stages. GUM is used for instrument calibrations, MSA is used for factory measurements and SPC is used for the production processes. Each is well adapted to the domain within which it is used but fundamentally deals with the same quantities and considerations. Differences in terminology and approach lead to confusion and a lack of traceability as uncertainty is propagated through the manufacturing process. This work, presented at the LAMDAMAP conference, presents an initial framework for a unified uncertainty-based approach. This treats manufacturing processes and factory measurements in the same way as instrument calibrations.

Title: A Unified Approach to Uncertainty for Quality Improvement

Authors: J E Muelaner, M Chappell, and P S  Keogh

Conference: Laser Metrology and Machine Performance XII. 2017. Renishaw Innovation Centre, UK

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To improve quality, process outputs must be measured. A measurement with no statement of its uncertainty gives no meaningful information. The Guide to the expression of Uncertainty in Measurement (GUM) aims to be a universal framework for uncertainty. However, to date, industry lacks such a common approach. A calibration certificate may state an Uncertainty or a Maximum Permissible Error. A gauge study gives the repeatability and reproducibility. Machines have an accuracy. Processes control aims to remove special cause variation and to monitor common cause variation. There are different names for comparable metrics and different methods to evaluate them. This leads to confusion. Small companies do not necessarily have experts able to implement all methods. This paper considers why multiple methods are currently used. It then gives a common language and approach for the use of uncertainty in all areas of manufacturing quality.

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