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dc.contributor.authorTierra-Arévalo, José-
dc.contributor.authorAyala-Chauvin, Manuel-
dc.contributor.authorNacevilla, Carmen-
dc.contributor.authorDe la Fuente-Morato, Albert-
dc.date.accessioned2022-06-12T03:21:58Z-
dc.date.available2022-06-12T03:21:58Z-
dc.date.issued2022-
dc.identifier.urihttps://www.semanticscholar.org/paper/Prevention-of-Failures-in-the-Footwear-Production-Tierra-Ar%C3%A9valo-Ayala-Chauvin/cf5267835d066ce2ceacad5860ab0387ad20d6cc-
dc.identifier.urihttp://repositorio.uti.edu.ec//handle/123456789/3050-
dc.description.abstractAt present, the handcrafted footwear sector is affected by the high competitiveness due to the increasing automation of companies. In this sense, in order to improve its competitiveness, a system was proposed to predict the failures of a production system and to carry out preventive maintenance actions. Samples were taken from 25 productions and 7 activities were established: cutting, stitching, pre fabrication, final preparation, gluing, assembly and finishing. The company produces batches of 90 pairs per day, with a standard time of 274.53 min and a promised productivity of 1.8. A support vector machine model was developed to predict the possible failures of the process taking as a reference the standard time of each stage. Finally, the results allow predicting the faults to optimise the production process by applying Support Vector Machine (SVM).es
dc.language.isoenges
dc.publisherSmart Innovation, Systems and Technologies. Volume 262 SIST, Pages 12 - 23. 8th International Conference on Sustainable Design and Manufacturing, KES-SDM 2021. Virtual, Online. 15 September 2021 through 17 September 2021es
dc.rightsclosedAccesses
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es
dc.titlePrevention of Failures in the Footwear Production Process by Applying Machine Learninges
dc.typearticlees
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