A Comparative Investigation Introducing Regularization Techniques in Linear Regression Models for Quality Prediction in Forming Technology

Vicaria A, Vogel-Heuser B, Krüger M, Merklein M, Lechner M (2024)

Publication Type: Conference contribution

Publication year: 2024

Publisher: IEEE Computer Society

Pages Range: 1230-1235

Conference Proceedings Title: IEEE International Conference on Industrial Engineering and Engineering Management

Event location: Bangkok, THA

ISBN: 9798350386097

DOI: 10.1109/IEEM62345.2024.10857210

Abstract

In this investigation, linear models used for quality prediction of a final product are compared and evaluated using data from a real manufacturing process in forming technology (i.e., flexible rolling process). Two alternative methods for simplifying the feature selection method for the quality prediction model of manufactured blanks are presented. This work proposes implementing L1 and L2 regularization techniques in the original regression model. The method is then evaluated based on model complexity and performance metrics using the final predictions. By comparing these indicators, the effectiveness and benefits of the proposed method are confirmed. A simplification in the model-building effort and feature selection process is developed while providing an efficient and comparable accuracy in the predicted quality of the manufactured blanks.

Authors with CRIS profile

Marion Merklein Institute of Manufacturing Technology (LFT) Michael Lechner Institute of Manufacturing Technology (LFT)

Involved external institutions

Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Vicaria, A., Vogel-Heuser, B., Krüger, M., Merklein, M., & Lechner, M. (2024). A Comparative Investigation Introducing Regularization Techniques in Linear Regression Models for Quality Prediction in Forming Technology. In IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1230-1235). Bangkok, THA: IEEE Computer Society.

MLA:

Vicaria, A., et al. "A Comparative Investigation Introducing Regularization Techniques in Linear Regression Models for Quality Prediction in Forming Technology." Proceedings of the 2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024, Bangkok, THA IEEE Computer Society, 2024. 1230-1235.

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