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Open AccessArticle

Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution

1
Departamento de Ingeniería Informática, University of Burgos, 09001 Burgos, Spain
2
Departamento de Ingeniería Informática, Fundación Centro Tecnológico de Miranda de Ebro, 09200 Miranda de Ebro, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4606; https://doi.org/10.3390/app10134606
Received: 3 June 2020 / Revised: 25 June 2020 / Accepted: 30 June 2020 / Published: 2 July 2020
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. View Full-Text
Keywords: ensembles; unbalanced datasets; internet of things; rotation forests; milling ensembles; unbalanced datasets; internet of things; rotation forests; milling
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MDPI and ACS Style

Garrido-Labrador, J.L.; Puente-Gabarri, D.; Ramírez-Sanz, J.M.; Ayala-Dulanto, D.; Maudes, J. Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution. Appl. Sci. 2020, 10, 4606. https://doi.org/10.3390/app10134606

AMA Style

Garrido-Labrador JL, Puente-Gabarri D, Ramírez-Sanz JM, Ayala-Dulanto D, Maudes J. Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution. Applied Sciences. 2020; 10(13):4606. https://doi.org/10.3390/app10134606

Chicago/Turabian Style

Garrido-Labrador, José L.; Puente-Gabarri, Daniel; Ramírez-Sanz, José M.; Ayala-Dulanto, David; Maudes, Jesus. 2020. "Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution" Appl. Sci. 10, no. 13: 4606. https://doi.org/10.3390/app10134606

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