2026-04-17T09:33:39Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/83552024-01-17T01:05:24Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Repositorio Institucional de la Universidad de Burgos
author
Checa Cruz, David
author
Urbikain Pelayo, Gorka
author
Beranoagirre, Aitor
author
Bustillo Iglesias, Andrés
author
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.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Multiplayer perceptron
Virtual reality
Serrated cutters
Energy optimization
Ensembles
Using Machine-Learning techniques and Virtual Reality to design cutting tools for energy optimization in milling operations
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/8355/1/Checa-ijcim_2022.pdf
File
MD5
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1319887
application/pdf
Checa-ijcim_2022.pdf