A Bayesian Approach to Process Model Evaluation in Short Run SPC
Research Area: | Volume 7,Issue 2, March 2018 | Year: | 2018 |
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Type of Publication: | Article | Keywords: | Bayesian Statistics, Big Data, Quality Assurance, Statistical Process Control, Short Run SPC |
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Journal: | IJEIR | Volume: | 7 |
Number: | 2 | Pages: | 92-97 |
Month: | March | ||
ISSN: | 2277-5668 | ||
Abstract: | 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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