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A Bayesian approach to process model evaluation in short run SPC

: Permin, Eike; Voigtmann, Christoph; Schmitt, Robert H.

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International journal of engineering innovations and research : IJEIR 7 (2018), No.2, pp.92-97
ISSN: 2277-5668
Journal Article, Electronic Publication
Fraunhofer IPT ()
Bayesian statistic; Big Data; quality assurance; Statistical process control; short run SPC

With the availability of Big Data in manufacturing, historical data to initially characterize a process is available in abundance. In fact, evaluating and selecting the best-fitted data set replaces data availability as major concern for setting up a short run SPC. We argue that due to the constant rise in computing power, it might not always be necessary to decide on one specific data set for a priori process characterization and modelling, but instead do most of the evaluation a posteriori. Thus, we introduce a new method to combine expert knowledge and Bayesian statistics for short run SPC in data-rich manufacturing environments. After a discussion on the methodology, its applicability and convergence, its application to turbine blade manufacturing is presented.