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Pure data-driven machine learning challenges for pFMEA : a case study

Mahdi Mokhtarzadeh (UGent) , Jorge Iván Rodríguez Echeverría (UGent) , Zafer Zeren, Johan Van Noten and Sidharta Gautama (UGent)
(2024) IFAC PAPERSONLINE. In IFAC-PapersOnLine 58(19). p.658-663
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Abstract
Manufacturing processes are susceptible to quality defects, resulting in overall equipment effectiveness reduction. Proactive and reactive methods, such as process failure mode and effects analysis, and root cause analysis, have been developed to eliminate potential causes of failure modes. In this study, data from an assembly case is evaluated using supervised machine learning methods to analyze the challenges of purely data-driven failure mode detection. Assembly step execution times, as indicators, and end-of-the-line quality checklists, as the failure modes, are used to gain insights into failure mode detection. Challenges for data-driven methods are discussed and possible future research streams are proposed.
Keywords
FMEA, RCA, Failure analysis, Explainable AI, Hybrid intelligence systems, MANAGEMENT, SUPPORT

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MLA
Mokhtarzadeh, Mahdi, et al. “Pure Data-Driven Machine Learning Challenges for PFMEA : A Case Study.” IFAC PAPERSONLINE, vol. 58, no. 19, 2024, pp. 658–63, doi:10.1016/j.ifacol.2024.09.225.
APA
Mokhtarzadeh, M., Rodriguez Echeverría, J., Zeren, Z., Van Noten, J., & Gautama, S. (2024). Pure data-driven machine learning challenges for pFMEA : a case study. IFAC PAPERSONLINE, 58(19), 658–663. https://doi.org/10.1016/j.ifacol.2024.09.225
Chicago author-date
Mokhtarzadeh, Mahdi, Jorge Rodriguez Echeverría, Zafer Zeren, Johan Van Noten, and Sidharta Gautama. 2024. “Pure Data-Driven Machine Learning Challenges for PFMEA : A Case Study.” In IFAC PAPERSONLINE, 58:658–63. https://doi.org/10.1016/j.ifacol.2024.09.225.
Chicago author-date (all authors)
Mokhtarzadeh, Mahdi, Jorge Rodriguez Echeverría, Zafer Zeren, Johan Van Noten, and Sidharta Gautama. 2024. “Pure Data-Driven Machine Learning Challenges for PFMEA : A Case Study.” In IFAC PAPERSONLINE, 58:658–663. doi:10.1016/j.ifacol.2024.09.225.
Vancouver
1.
Mokhtarzadeh M, Rodriguez Echeverría J, Zeren Z, Van Noten J, Gautama S. Pure data-driven machine learning challenges for pFMEA : a case study. In: IFAC PAPERSONLINE. 2024. p. 658–63.
IEEE
[1]
M. Mokhtarzadeh, J. Rodriguez Echeverría, Z. Zeren, J. Van Noten, and S. Gautama, “Pure data-driven machine learning challenges for pFMEA : a case study,” in IFAC PAPERSONLINE, Vienna, Austria, 2024, vol. 58, no. 19, pp. 658–663.
@inproceedings{01JA38KYMXARFVJEA8ED11EEQ0,
  abstract     = {{Manufacturing processes are susceptible to quality defects, resulting in overall equipment effectiveness reduction. Proactive and reactive methods, such as process failure mode and effects analysis, and root cause analysis, have been developed to eliminate potential causes of failure modes. 
In this study, data from an assembly case is evaluated using supervised machine learning methods to analyze the challenges of purely data-driven failure mode detection. Assembly step execution times, as indicators, and end-of-the-line quality checklists, as the failure modes, are used to gain insights into failure mode detection.
Challenges for data-driven methods are discussed and possible future research streams are proposed.}},
  author       = {{Mokhtarzadeh, Mahdi and Rodríguez Echeverría, Jorge Iván and Zeren, Zafer and Van Noten, Johan and Gautama, Sidharta}},
  booktitle    = {{IFAC PAPERSONLINE}},
  issn         = {{2405-8971}},
  keywords     = {{FMEA,RCA,Failure analysis,Explainable AI,Hybrid intelligence systems,MANAGEMENT,SUPPORT}},
  language     = {{eng}},
  location     = {{Vienna, Austria}},
  number       = {{19}},
  pages        = {{658--663}},
  title        = {{Pure data-driven machine learning challenges for pFMEA : a case study}},
  url          = {{http://doi.org/10.1016/j.ifacol.2024.09.225}},
  volume       = {{58}},
  year         = {{2024}},
}
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