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dc.contributor.authorGarcía Nieto, Paulino José
dc.contributor.authorGarcía-Gonzalo, Esperanza
dc.contributor.authorSánchez Lasheras, Fernando
dc.contributor.authorBernardo Sánchez, Antonio
dc.date2020-12
dc.date.accessioned2022-04-05T10:04:50Z
dc.date.available2022-04-05T10:04:50Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12815
dc.description.abstractThe purpose of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. The principal feature of chromium plating is its endurance in the face of the wear and corrosion. This industrial process has a vast range of applications in many different areas. In the performance of this process, some difficulties can be found. Some of the most common are melt deposition, milky white chromium deposition, rough or sandy chromium deposition and lack of toughness of the layer or wear and lack of thickness of the layer deposited. This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). The outcomes indicate that PSO/SVM–based model together with radial basis function (RBF) kernel has permitted to foretell the thickness of the chromium layer created in this industrial process satisfactorily. Thus, two kinds of outcomes have been obtained: firstly, this model permits to determine the ranking of relevance of the seven independent input variables investigated in this industrial process. Finally, the high achievement and lack of complexity of the model indicate that the PSO/SVM method is very interesting compared to other conventional foretelling techniques, since a coefficient of determination of 0.9952 is acquired.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 4
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2835es_ES
dc.rightsopenAccesses_ES
dc.subjectsupport vector machinees_ES
dc.subjectparticle swarm optimizationes_ES
dc.subjectmachine learninges_ES
dc.subjectregressiones_ES
dc.subjecthard chromium plating processes_ES
dc.subjectIJIMAIes_ES
dc.titleChrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Modeles_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.11.004


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