Using Machine-Learning techniques and Virtual Reality to design cutting tools for energy optimization in milling operations

ABSTRACT

The selection of a proper cutting tool in machining operations is a critical issue. Tool geometric parameters are essential for milling performance. However, the process engineer has very limited experience of the best parameter combination, due to the high cost of cutting tool tests. The same holds true for bachelor studies on machining processes. This study proposes a new strategy that combines experimental tests, machine-learning modelling and Virtual Reality visualization to overcome these limitations. First, tools with different geometric parameters are tested. Second, the experimental data are modeled with different machine-learning techniques (regression trees, multilayer perceptrons, bagging and random forest ensembles). An in-depth analysis of the influence of each input on model accuracy is performed to reduce experimental costs. The results show that the best model with no cutting-force inputs performed worse than the best model with all the inputs. Third, the most accurate model is used to build 3D graphs of special interest to engineering students as well as process engineers, for the optimization of power consumption under different cutting conditions. Finally, a Virtual Reality environment is presented to train engineering students in the study of the best tool design and cutting parameter optimization.

KEYWORDS:

• Multilayer perceptron
• virtual reality
• serrated cutters
• energy optimization
• ensembles

Acknowledgments

The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research, as well as Dr. Carlos Lopez from the University of Burgos for his kind-spirited and useful advice.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This investigation was partially supported by Projects Grua-RV and ACIS (Reference Number INVESTUN/18/0002 and INVESTUN/21/0002) of the Consejería de Empleo e Industria of the Junta de Castilla y León, co-financed through European Union FEDER funds, by project SMART-EASY project (Reference Number IDI-20191008) funded by the Spanish Centro para el Desarrollo Tecnológico e Industrial (CDTI), by Project Smart-Label (Reference Number PID2020-119894GB-I00) and project PDC2021-121792-I00, both funded by the Spanish Ministry of Science and Innovation and by Project Elkatek KK-2021/00003 funded by the Basque Government.