Usage of Real Time Machine Vision in Rolling Mill

Jiří David, Pavel Švec, Vít Pasker and Romana Garzinová
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Jiří David: Department of Automation and Computing in Industry, VŠB-Technical University Ostrava, 17. Listopadu 2173/15, 70800 Ostrava, Czech Republic
Pavel Švec: Department of Automation and Computing in Industry, VŠB-Technical University Ostrava, 17. Listopadu 2173/15, 70800 Ostrava, Czech Republic
Vít Pasker: Department of Automation and Computing in Industry, VŠB-Technical University Ostrava, 17. Listopadu 2173/15, 70800 Ostrava, Czech Republic
Romana Garzinová: Department of Automation and Computing in Industry, VŠB-Technical University Ostrava, 17. Listopadu 2173/15, 70800 Ostrava, Czech Republic

Sustainability, 2021, vol. 13, issue 7, 1-18

Abstract: This article deals with the issue of computer vision on a rolling mill. The main goal of this article is to describe the designed and implemented algorithm for the automatic identification of the character string of billets on the rolling mill. The algorithm allows the conversion of image information from the front of the billet, which enters the rolling process, into a string of characters, which is further used to control the technological process. The purpose of this identification is to prevent the input pieces from being confused because different parameters of the rolling process are set for different pieces. In solving this task, it was necessary to design the optimal technical equipment for image capture, choose the appropriate lighting, search for text and recognize individual symbols, and insert them into the control system. The research methodology is based on the empirical-quantitative principle, the basis of which is the analysis of experimentally obtained data (photographs of billet faces) in real operating conditions leading to their interpretation (transformation into the shape of a digital chain). The first part of the article briefly describes the billet identification system from the point of view of technology and hardware resources. The next parts are devoted to the main parts of the algorithm of automatic identification—optical recognition of strings and recognition of individual characters of the chain using artificial intelligence. The method of optical character recognition using artificial neural networks is the basic algorithm of the system of automatic identification of billets and eliminates ambiguities during their further processing. Successful implementation of the automatic inspection system will increase the share of operation automation and lead to ensuring automatic inspection of steel billets according to the production plan. This issue is related to the trend of digitization of individual technological processes in metallurgy and also to the social sustainability of processes, which means the elimination of human errors in the management of the billet rolling process.

Keywords: metallurgy; machine vision; neural networks; control; applications; steel industry; optical devices; artificial intelligence; machine learning; intelligent systems; artificial neural networks; image processing; image classification; real-time systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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