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Root Cause Detection with an Ensemble Machine Learning Approach in the Multivariate Manufacturing Process

  • Deniz Demircioglu DirenEmail author
  • Semra Boran
  • Ihsan Hakan Selvi
  • Tugcen Hatipoglu
Conference paper
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)

Abstract

Quality control in multivariate manufacturing processes should be applied with multi variate control charts. Although this method is sufficient, it doesn’t include the causes of uncontrolled situations. It only shows samples that are out of control. A variety of methods are required to determine the root cause(s) of the uncontrolled situations. In this study, a classification model, based on the ensemble approach of machine learning classification algorithms, is proposed for determining the root cause(s). Algorithms are compared according to predictive accuracy, kappa value and root square mean error rates as performance criteria. Results show that Neural Network ensemble techniques are more efficient and successful than individual Neural Network learning algorithms.

Keywords

Multivariate process Multivariate quality control charts Mason young tracy decomposition Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Deniz Demircioglu Diren
    • 1
    Email author
  • Semra Boran
    • 1
  • Ihsan Hakan Selvi
    • 1
    • 2
  • Tugcen Hatipoglu
    • 3
  1. 1.Industrial Engineering DepartmentSakarya UniversitySakaryaTurkey
  2. 2.Information Systems Engineering DepartmentSakarya UniversitySakaryaTurkey
  3. 3.Industrial Engineering DepartmentKocaeli UniversityKocaeliTurkey

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