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Título : Prevention of Failures in the Footwear Production Process by Applying Machine Learning
Autor : Tierra-Arévalo, José
Ayala-Chauvin, Manuel
Nacevilla, Carmen
De la Fuente-Morato, Albert
Fecha de publicación : 2022
Editorial : Smart 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 2021
Resumen : 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).
URI : https://www.semanticscholar.org/paper/Prevention-of-Failures-in-the-Footwear-Production-Tierra-Ar%C3%A9valo-Ayala-Chauvin/cf5267835d066ce2ceacad5860ab0387ad20d6cc
http://repositorio.uti.edu.ec//handle/123456789/3050
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