RT info:eu-repo/semantics/article
T1 Improving the accuracy of machine-learning models with data from machine test repetitions
A1 Bustillo Iglesias, Andrés
A1 Reis, Roberto
A1 Rocha Machado, Alisson
A1 Pimenov, Danil Yurievich
K1 Machine learning
K1 Artificial intelligence
K1 Ensembles
K1 Brandsma facing tests
K1 Tool geometry
K1 Turning
K1 Ingeniería mecánica
K1 Mechanical engineering
K1 Máquinas herramientas
K1 Machine-tools
K1 Inteligencia artificial
K1 Artificial intelligence
AB The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.
PB Springer
SN 0956-5515
YR 2020
FD 2020-09
LK https://hdl.handle.net/10259/11215
UL https://hdl.handle.net/10259/11215
LA eng
NO This investigation was partially supported by Project TIN2015-67534-P of the Ministerio de Economía y Competitividad of the Spanish Government and by Project BU085P17 of the Junta de Castilla y León, both co-financed through European Union FEDER funds. The work was supported through Act 211 of the Government of the Russian Federation, under contract No. 02.A03.21.0011.
DS Repositorio Institucional de la Universidad de Burgos
RD 11-may-2026