Energy Profile Prediction of Milling Processes Using Machine Learning Techniques

Mühlbauer M, Würschinger H, Polzer D, Hanenkamp N (2020)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2020

Publisher: Springer Vieweg

Edited Volumes: Machine Learning for Cyber Physical Systems

Series: Technologien für die intelligente Automation

City/Town: Berlin, Heidelberg

Pages Range: 1-11

ISBN: 9783662627457

DOI: 10.1007/978-3-662-62746-4_1

Abstract

The prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.

Authors with CRIS profile

Matthias Mühlbauer Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Hubert Würschinger Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Dominik Polzer Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Nico Hanenkamp Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen

How to cite

APA:

Mühlbauer, M., Würschinger, H., Polzer, D., & Hanenkamp, N. (2020). Energy Profile Prediction of Milling Processes Using Machine Learning Techniques. In Beyerer J., Maier A., Niggemann O. (Eds.), Machine Learning for Cyber Physical Systems. (pp. 1-11). Berlin, Heidelberg: Springer Vieweg.

MLA:

Mühlbauer, Matthias, et al. "Energy Profile Prediction of Milling Processes Using Machine Learning Techniques." Machine Learning for Cyber Physical Systems. Ed. Beyerer J., Maier A., Niggemann O., Berlin, Heidelberg: Springer Vieweg, 2020. 1-11.

BibTeX: Download