2026-04-17T09:16:55Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/83552024-01-17T01:05:24Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Checa Cruz, David Urbikain Pelayo, Gorka Beranoagirre, Aitor Bustillo Iglesias, Andrés López de Lacalle, Luis N. 2024-01-16T13:22:16Z 2024-01-16T13:22:16Z 2022-01 0951-192X http://hdl.handle.net/10259/8355 10.1080/0951192X.2022.2027020 1362-3052 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. 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. application/pdf eng Taylor & Francis International Journal of Computer Integrated Manufacturing. 2022, V. 35, n. 9, p. 951-971 https://doi.org/10.1080/0951192X.2022.2027020 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Multiplayer perceptron Virtual reality Serrated cutters Energy optimization Ensembles Informática Tecnología Computer science Technology Using Machine-Learning techniques and Virtual Reality to design cutting tools for energy optimization in milling operations info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion International Journal of Computer Integrated Manufacturing 35 9 951 971