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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(5): 458-462 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.454

A Hybrid Risk Evaluation Model for Automotive Production

Mehmet Turgut and Alp Ustundag
Abstract—Failure mode and effects analysis (FMEA) is used commonly for the prioritization of failures in the automotive production. Traditional FMEA determines the risk priorities of failure modes, which require the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode to be evaluated. However, it has some drawbacks so that affect the risk evaluation and correction action. It is very difficult for three risk factors to be evaluated precisely. Additionally, traditional method cannot capture different team members’ opinions and prioritize failure modes under different types of uncertainties. So, in this study, FMEA using fuzzy evidential approach and grey theory are used to prioritize the failures for a truck production company in Turkey. Degrees of relation for the six failure modes are determined. The defect of “unstable” is found as the most important and serious risk according to the analysis.

Index Terms—Automotive production, failure mode and effects analysis (FMEA), fuzzy evidential approach, grey theory, risk prioritization.

The authors are with the Istanbul Technical University, Industrial Engineering Department Macka 34367 Istanbul, Turkey (e-mail: turgutmeh@itu.edu.tr, ustundaga@itu.edu.tr).

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Cite: Mehmet Turgut and Alp Ustundag, "A Hybrid Risk Evaluation Model for Automotive Production," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 458-462, 2014.

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