A Prediction Model for Fault Detection in Molding Process Based on Logistic Regression Technique

  • İnanç KabasakalEmail author
  • Fatma Demircan Keskin
  • Aydin Koçak
  • Haluk Soyuer
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Industry 4.0 is often described as a wave of transformation that enforces the digitalization of processes to create integrated and autonomous systems. In this regard, the collection of process data is a necessity to analyze data with advanced techniques for various purposes. Statistical techniques in machine learning might provide solutions for fault detection and other tasks in manufacturing processes. In our study, a learning model is proposed for a fault prediction task with the use of Logistic Regression. The data used for the analysis involve measurements from sequential processes carried out in a large-scale wheel rim manufacturer. The pre-processing and analysis of process data was introduced along with a case study. Moreover, findings of the model were presented and the potential use of the model will be discussed.


Machine learning Logistic regression Binary classification Fault prediction Industry 4.0 



We would like to thank Maxion İnci Wheel Group and Logo Business Solutions for their support and contribution in this study.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • İnanç Kabasakal
    • 1
    Email author
  • Fatma Demircan Keskin
    • 1
  • Aydin Koçak
    • 1
  • Haluk Soyuer
    • 1
  1. 1.Department of Business AdministrationEge UniversityİzmirTurkey

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