RT info:eu-repo/semantics/article
T1 Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution
A1 Garrido Labrador, José Luis
A1 Puente Gabarri, Daniel
A1 Ramírez Sanz, José Miguel
A1 Ayala Dulanto, David
A1 Maudes Raedo, Jesús M.
K1 ensembles
K1 unbalanced datasets
K1 internet of things
K1 rotation forests
K1 milling
K1 Informática
K1 Computer science
AB The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.
PB MDPI
YR 2020
FD 2020-07
LK http://hdl.handle.net/10259/5379
UL http://hdl.handle.net/10259/5379
LA eng
NO Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds.
DS Repositorio Institucional de la Universidad de Burgos
RD 26-abr-2024