Automatic monitoring of steel strip positioning error based on semantic segmentation
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ORIGINAL ARTICLE
Automatic monitoring of steel strip positioning error based on semantic segmentation Aline de Faria Lemos1
´ Vince Nagy1 · Leonardo Adolpho Rodrigues da Silva2 · Balazs
Received: 22 April 2020 / Accepted: 30 July 2020 © The Author(s) 2020
Abstract The misalignment of steel strips in relation to the roller table centerline still is an impairment for the rolling mill production lines. Nowadays, the strip position correction remains largely in the purview of human analysis, in which the strip steering is traditionally a semi-manual operation. Automating the alignment process could reduce the maintenance costs, damage to the plant, and prevent material losses. The first step into the automatization is to determine the strip position and its referred error. This study presents a method that employs semantic segmentation based on convolution neural networks to estimate steel strips positioning error from images of the process. Additionally, the system mitigates the influences of mechanical vibration on the images. The system performance was assessed by standard semantic segmentation evaluation metrics and in comparison with the dataset ground truth. The results showed that 97% of the estimated positioning errors are within a 2-pixel margin. The method demonstrated to be a robust real-time solution as the networks were trained from a set of low-resolution images acquired in a complex environment. Keywords Steckel mill · Steel strips positioning error · Semantic segmentation · Convolution neural network · Hot strips
1 Introduction Steel strips are manufactured from cast slabs, which undergo several times between a pair of work rolls with decreasing gaps until the achievement of the intended thickness reduction [20, 21, 39, 40]. In Steckel mill lines, during the hot rolling process, the strips are driven by the roller table in the rolling direction. However, the rolling procedure is susceptible to impel the strips perpendicularly to this direction, which could induce misalignment. For Aline de Faria Lemos
[email protected] Leonardo Adolpho Rodrigues da Silva [email protected] Bal´azs Vince Nagy [email protected] 1
Department of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest, Hungary
2
Department of Telecommunications and Mechatronics Engineering, Federal University of S˜ao Jo˜ao del-Rei, Ouro Branco, Brazil
instance, the unaligned strips are prone to collide with the side guides and mill structure [4, 11]. As the 20-ton strips are rolled at 10 m/s, the collisions are an impairment for the production lines, provoking material losses and damaging the mill structure and equipment. Annually, the material loss due to collisions and equipment failure expenses are about one million euros [11]. Traditionally, the process of correcting the alignment of the strips is semi-manual. In this process, a human operator observes the strip position through real-time images of the process, which are acquired from analog cameras settled
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