Summary
Rule extraction for root cause analysis in manufacturing process optimization is an alternative to traditional approaches to root cause analysis based on process capability indices and variance analysis. Process capability indices alone do not allow to identify those process parameters which have the major impact on quality since these indices are only based on measurement results and do not consider the explaining process parameters. Variance analysis is subject to serious constraints concerning the data sample used in the analysis. In this work a rule search approach using Branch and Bound principles is presented, considering both the numerical measurement results and the nominal process factors. This combined analysis allows to associate the process parameters with the measurement results and therefore to identify the main drivers for quality deterioration of a manufacturing process.
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Hrycej, T., Strobel, C.M. (2008). Extraction of Maximum Support Rules for the Root Cause Analysis. In: Prokhorov, D. (eds) Computational Intelligence in Automotive Applications. Studies in Computational Intelligence, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79257-4_6
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DOI: https://doi.org/10.1007/978-3-540-79257-4_6
Publisher Name: Springer, Berlin, Heidelberg
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