RT info:eu-repo/semantics/article
T1 Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth
A1 Bustillo Iglesias, Andrés
A1 Pimenov, Danil Yurievich
A1 Mia, Mozammel
A1 Kapłonek, Wojciech
K1 Face milling
K1 Wear
K1 Tool life
K1 Tool condition monitoring
K1 Flatness deviation
K1 Cutting power
K1 Random forest
K1 SMOTE
K1 Ingeniería mecánica
K1 Mechanical engineering
K1 Máquinas herramientas
K1 Machine-tools
K1 Inteligencia artificial
K1 Artificial intelligence
AB 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.
PB Springer
SN 0956-5515
YR 2020
FD 2020-09
LK https://hdl.handle.net/10259/11214
UL https://hdl.handle.net/10259/11214
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 19-abr-2026