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dc.contributor.authorJuez Gil, Mario 
dc.contributor.authorErdakov, Ivan Nikolaevich
dc.contributor.authorBustillo Iglesias, Andrés 
dc.contributor.authorPimenov, Danil Yurievich
dc.date.accessioned2020-06-09T14:08:57Z
dc.date.available2020-06-09T14:08:57Z
dc.date.issued2019-07
dc.identifier.issn2090-1232
dc.identifier.urihttp://hdl.handle.net/10259/5333
dc.description.abstractHighly 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.en
dc.description.sponsorshipGovernment of the Russian Federation, Russia (contractNo02.A03.21.0011), by theproject TIN2015-67534-P of the Ministerio de Economía Competitividad of the Spanish Government, Spain, and the project BU085P17 of the Junta de Castilla y León (both projects co-financed through European-Union FEDER funds) and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund with the EDU/1100/2017 pre-doctoral fellowshipses
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofJournal of Advanced Research. 2019. V. 18, p. 173-184es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHadfield steelen
dc.subjectResource savingsen
dc.subjectLifetime predictionen
dc.subjectRegression treesen
dc.subjectMulti-layer perceptronsen
dc.subjectArtificial intelligenceen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimesen
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.jare.2019.03.008es
dc.identifier.doi10.1016/j.jare.2019.03.008
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/BU085P17
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/TIN2015-67534-P
dc.journal.titleJournal of Advanced Researches
dc.volume.number18es
dc.page.initial173es
dc.page.final184es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion


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