2026-06-10T11:28:14Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/53792021-11-02T12:00:24Zcom_10259_4219com_10259_5086com_10259_2604com_10259_5377col_10259_4220col_10259_5378

Garrido Labrador, José Luis Puente Gabarri, Daniel Ramírez Sanz, José Miguel Ayala Dulanto, David Maudes Raedo, Jesús M. 2020-07-16T09:02:31Z 2020-07-16T09:02:31Z 2020-07 http://hdl.handle.net/10259/5379 10.3390/app10134606 2076-3417 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. 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. application/pdf eng MDPI Applied Sciences. 2020, V. 10, n. 13, 4606 https://doi.org/10.3390/app10134606 info:eu-repo/grantAgreement/MINECO/TIN2015-67534-P info:eu-repo/grantAgreement/JCyL/CCTT1/17/BU/0003 info:eu-repo/grantAgreement/JCyL/BU085P17 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess ensembles unbalanced datasets internet of things rotation forests milling Informática Computer science Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Applied Sciences 10 13 4606