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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/5333

    Título
    A regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimes
    Autor
    Juez Gil, MarioAutoridad UBU Orcid
    Erdakov, Ivan Nikolaevich
    Bustillo Iglesias, AndrésAutoridad UBU Orcid
    Pimenov, Danil Yurievich
    Publicado en
    Journal of Advanced Research. 2019. V. 18, p. 173-184
    Editorial
    Elsevier
    Fecha de publicación
    2019-07
    ISSN
    2090-1232
    DOI
    10.1016/j.jare.2019.03.008
    Resumen
    Highly tensile manganese steel is in great demand owing to its high tensile strength under shock loads. All workpieces are produced through casting, because it is highly difficult to machine. The probabilistic aspects of its casting, its variable composition, and the different casting techniques must all be considered for the optimisation of its mechanical properties. A hybrid strategy is therefore proposed which combines decision trees and artificial neural networks (ANNs) for accurate and reliable prediction models for ore crushing plate lifetimes. The strategic blend of these two high-accuracy prediction models is used to generate simple decision trees which can reveal the main dataset features, thereby facilitating decision-making. Following a complexity analysis of a dataset with 450 different plates, the best model consisted of 9 different multilayer perceptrons, the inputs of which were only the Fe and Mn plate compositions. The model recorded a low root mean square error (RMSE) of only 0.0614 h for the lifetime of the plate: a very accurate result considering their varied lifetimes of between 746 and 6902 h in the dataset. Finally, the use of these models under real industrial conditions is presented in a heat map, namely a 2D representation of the main manufacturing process inputs with a colour scale which shows the predicted output, i.e. the expected lifetime of the manufactured plates. Thus, the hybrid strategy extracts core training dataset information in high-accuracy prediction models. This novel strategy merges the different capabilities of two families of machine-learning algorithms. It provides a high-accuracy industrial tool for the prediction of the full lifetime of highly tensile manganese steel plates. The results yielded a precision prediction of (RMSE of 0.061 h) for the full lifetime of (light, medium, and heavy) crusher plates manufactured with the three (experimental, classic, and highly efficient (new)) casting methods.
    Palabras clave
    Hadfield steel
    Resource savings
    Lifetime prediction
    Regression trees
    Multi-layer perceptrons
    Artificial intelligence
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/5333
    Versión del editor
    https://doi.org/10.1016/j.jare.2019.03.008
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    Attribution-NonCommercial-NoDerivatives 4.0 Internacional
    Documento(s) sujeto(s) a una licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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    Juez-jav_2019.pdf
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