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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/5379

    Título
    Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution
    Autor
    Garrido Labrador, José LuisAutoridad UBU Orcid
    Puente Gabarri, Daniel
    Ramírez Sanz, José MiguelAutoridad UBU Orcid
    Ayala Dulanto, David
    Maudes Raedo, Jesús M.Autoridad UBU Orcid
    Publicado en
    Applied Sciences. 2020, V. 10, n. 13, 4606
    Editorial
    MDPI
    Fecha de publicación
    2020-07
    DOI
    10.3390/app10134606
    Zusammenfassung
    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.
    Palabras clave
    ensembles
    unbalanced datasets
    internet of things
    rotation forests
    milling
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/5379
    Versión del editor
    https://doi.org/10.3390/app10134606
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    Garrido-as_2020.pdf
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