Abstract
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.
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Acknowledgments
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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Kabasakal, İ., Keskin, F.D., Koçak, A., Soyuer, H. (2020). A Prediction Model for Fault Detection in Molding Process Based on Logistic Regression Technique. In: Durakbasa, N., Gençyılmaz, M. (eds) Proceedings of the International Symposium for Production Research 2019. ISPR ISPR 2019 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-31343-2_31
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DOI: https://doi.org/10.1007/978-3-030-31343-2_31
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