Use of Artificial Intelligence in Classification of Mill Scale Defects. Issue 3 (16th June 2014)

• Record Type: Journal Article
• Authors: Lechwar, Szymon; Rauch, Łukasz; Pietrzyk, Maciej
• Is Part Of: Steel research international. Volume 86:Issue 3(2015:Mar.)
• Page Start: 266
• Page End: 277
• Publication Date: 2014-06-16
• Subjects: Steel -- Periodicals; Steel -- Metallurgy -- Periodicals; 669.142
• DOI: 10.1002/srin.201400016
• Languages: English
• ISSNs: 1611-3683

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="srin201400016-sec-0001" sec-type="section"> <p>The subject of the work was to design and implement classification model for various kinds of mill scales recognition at typical hot rolling mill (HRM). Input data were measured by the automatic surface inspection system, which provided numerous features describing single image, considered as a defect. The data were analyzed by using feature selection methods. Afterwards, determined most important subsets of features, were applied to build scale classification models. The most efficient model and its key variables were used to distinguish different kinds of scales. Implementation of the model in production system allowed to show a gain of overall mill scales classification accuracy. In the paper, the main attention is put on the research methodology and artificial intelligence (AI) methods used to classify scale defects.</p> </sec> </abstract>