2026-04-19T15:14:55Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112142026-01-15T01:05:31Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Bustillo Iglesias, Andrés 81 600 0000-0003-2855-7532 Pimenov, Danil Yurievich 74d03e66-c1bf-488a-a090-70369ab2125f 600 Mia, Mozammel 36e246f4-56d6-4b9d-a69b-8234664bbc69 600 Kapłonek, Wojciech 3ea414cf-3ccb-4df1-80d1-ceb76cdb6414 600 2026-01-14T09:42:23Z 2026-01-14T09:42:23Z 2020-09 0956-5515 https://hdl.handle.net/10259/11214 10.1007/s10845-020-01645-3 1572-8145 The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models. The work was supported by Act 211 Government of the Russian Federation, Contract No. 02.A03.21.0011 and partially supported by the Project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and the Project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León both co-financed from European Union FEDER funds. The research was carried out within the South-Ural State University Project 5-100 from 2016 to 2020 aimed to increase the competitiveness of leading Russian universities among the world research and educational centers. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. application/pdf eng Springer Journal of Intelligent Manufacturing. 2020, V. 32 n. 3, p. 895-912 https://doi.org/10.1007/s10845-020-01645-3 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Face milling Wear Tool life Tool condition monitoring Flatness deviation Cutting power Random forest SMOTE Ingeniería mecánica Máquinas herramientas Inteligencia artificial Mechanical engineering Machine-tools Artificial intelligence Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Journal of Intelligent Manufacturing 32 3 895 912 THUMBNAIL Bustillo-joim_2021.pdf.jpg Bustillo-joim_2021.pdf.jpg IM Thumbnail image/jpeg 4570 https://riubu.ubu.es/bitstream/10259/11214/4/Bustillo-joim_2021.pdf.jpg 139c8a00ccab6668d7ae3e1c23117ca6 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/11214/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/11214/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Bustillo-joim_2021.pdf Bustillo-joim_2021.pdf application/pdf 2045723 https://riubu.ubu.es/bitstream/10259/11214/1/Bustillo-joim_2021.pdf 11fd229615223f6acef9931faa418b0b MD5 1 10259/11214 oai:riubu.ubu.es:10259/11214 2026-01-15 02:05:31.844 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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