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Prevention of Failures in the Footwear Production Process by Applying Machine Learning

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10.1007/978-981-16-6128-0_2
 
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hdl:2117/366334

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Tierra Arévalo, Marcelo
Ayala Chauvin, Manuel Ignacio
Nacevilla, Carmen
Fuente Morató, Albert de laMés informacióMés informacióMés informació
Document typePart of book or chapter of book
Defense date2021-09-18
PublisherSpringer
Rights accessRestricted access - publisher's policy
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
At 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).
CitationTierra, M. [et al.]. Prevention of Failures in the Footwear Production Process by Applying Machine Learning. A: "Smart Innovation, Systems and Technologies". Berlín: Springer, 2021, p. 12-23. 
URIhttp://hdl.handle.net/2117/366334
DOI10.1007/978-981-16-6128-0_2
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-981-16-6128-0_2
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  • Doctorat en Sostenibilitat - Capítols de llibre [5]
  • Departament d'Enginyeria mecànica - Capítols de llibre [18]
  • CITCEA - Centre d'Innovació Tecnològica en Convertidors Estàtics i Accionaments - Capítols de llibre [5]
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