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Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study

Chehri Hamou, Chehri Abdellah, Kiss László et Zimmerman Alfred. (2020). Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study. Procedia Computer Science, 176, p. 3536-3544.

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URL officielle: http://dx.doi.org/doi:10.1016/j.procs.2020.09.033

Résumé

Automatic fault detection using machine learning has become an exciting and promising area of research. This because it accurate and timely way to manage and classify with minimal human effort. In the computer vision community, deep-learning methods have become the most suitable approaches for this task. Anodes are large carbon blocks that are used to conduct electricity during the aluminum reduction process. The most basic function of anode rod inspection is to prevent a situation where the anode rod will not fit into the stub-holes of a new anode. It would be the case for a rod containing either severe toe-in, missing stubs, or a retained thimble on one or more stubs. In this work, to improve the accuracy of shape defect inspection for an anode rod, we use the Fast Region-based Convolutional Network method (Fast R-CNN), model. To train the detection model, we collect an image dataset composed of multi-class of anode rod defects with annotated labels. Our model is trained using a small number of samples, an essential requirement in the industry where the number of available defective samples is limited. It can simultaneously detect multi-class of defects of the anode rod in nearly real-time.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:176
Pages:p. 3536-3544
Version évaluée par les pairs:Oui
Date:2020
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:deep learning, automatic inspection, anode, industry 4.0, apprentissage en profondeur, inspection automatique, anode, industrie 4.0
Déposé le:17 mai 2021 17:03
Dernière modification:17 mai 2021 17:03
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