A Bayesian Approach to Process Model Evaluation in Short Run SPC

Research Area: Volume 7,Issue 2, March 2018 Year: 2018
Type of Publication: Article Keywords: Bayesian Statistics, Big Data, Quality Assurance, Statistical Process Control, Short Run SPC
  • Eike Permin
  • Christoph Voigtmann
  • Robert Schmitt
Journal: IJEIR Volume: 7
Number: 2 Pages: 92-97
Month: March
ISSN: 2277-5668
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.

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