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

  • 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.

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Metadaten
Author of HS ReutlingenZimmermann, Alfred
URN:urn:nbn:de:bsz:rt2-opus4-30761
DOI:https://doi.org/10.1016/j.procs.2020.09.033
Erschienen in:Procedia computer science
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of Publication:2020
Tag:anode; automatic inspection; deep learning; industry 4.0
Volume:176
Issue:Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020
Page Number:9
First Page:3536
Last Page:3544
DDC classes:004 Informatik
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International