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Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2128-7090
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-7106-0025
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-8884-2154
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
2019 (English)In: Applied Mathematical Modelling, ISSN 0307-904X, E-ISSN 1872-8480, Vol. 70, p. 365-377Article in journal (Refereed) Published
Abstract [en]

The competitiveness in the manufacturing industry raises demands for using recent data analysis algorithms for manufacturing process development. Data-driven analysis enables extraction of novel knowledge from already existing sensors and data, which is necessary for advanced manufacturing process refinement involving aged machinery. Improved data analysis enables factories to stay competitive against newer factories, but without any hefty investment. In large manufacturing operations, the dependencies between data are highly complex and therefore very difficult to analyse manually. This paper applies a deep learning approach, using a recurrent neural network with long short term memory cells together with an attention mechanism to model the dependencies between the measured product shape, as measured before the most critical manufacturing operation, and the final product quality. Our approach predicts the ratio of flawed products already before the critical operation with an AUC-ROC score of 0.85, i.e., we can detect more than 80 % of all flawed products while having less than 25 % false positive predictions (false alarms). In contrast to previous deep learning approaches, our method shows how the recurrent neural network reasons about the input shape, using the attention mechanism to point out which parts of the product shape that have the highest influence on the predictions. Such information is crucial for both process developers, in order to understand and improve the process, and for process operators who can use the information to learn how to better trust the predictions and control the process.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 70, p. 365-377
Keywords [en]
Attention mechanism, Recurrent Neural Networks, Interpretable AI, Steel Rolling
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16588DOI: 10.1016/j.apm.2019.01.027ISI: 000468714000021Scopus ID: 2-s2.0-85060941525OAI: oai:DiVA.org:his-16588DiVA, id: diva2:1283525
Projects
DataFlow
Funder
Vinnova, 2017-01531Available from: 2019-01-29 Created: 2019-01-29 Last updated: 2019-07-10Bibliographically approved

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Ståhl, NiclasMathiason, GunnarFalkman, GöranKarlsson, Alexander

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