Using computer vision to analyse fracture strains of oxide scale layers on a macro level

Using computer vision to analyse fracture strains of oxide scale layers on a macro level


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Abstract. Hot forging has established itself as an efficient process for the manufacture of highly stressed components. The high semi-finished product temperatures significantly increase the deformation capacity and enable the production of complex geometries. However, high semi-finished product temperatures of up to 1250 °C also lead to increased oxide scale formation. Therefore, oxide scale plays an important role in the context of hot forming processes. Due to the contrasting properties between steel substrates and oxide scale, the appearance of oxide scale affects numerous influencing factors, such as changed friction conditions or thermophysical properties. With increasing interest in numerical process prediction arises the demand to take into account the behaviour of oxide scale in finite-element simulations. In addition to the numerical mapping of the crack behaviour, the challenge in mapping the oxide scale is to determine suitable parameters for describing the failure behaviour. Therefore, this work focuses on a novel procedure to characterise the failure of oxide scale under process relevant conditions of hot forging.

Computer Vision, Fracture Types, Oxide Scale, Tensile Test

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: WESTER Hendrik, HUNZE-TRETOW Jan Niklas, BRUNOTTE Kai, BEHRENS Bernd-Arno, Using computer vision to analyse fracture strains of oxide scale layers on a macro level, Materials Research Proceedings, Vol. 41, pp 802-811, 2024


The article was published as article 88 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] B.-A. Behrens, R. Kawalla, B. Awiszus, A. Bouguecha, M. Ullmann, M. Graf, C. Bonk, A. Chugreev, and H. Wester, Numerical Investigation of the Oxide Scale Deformation Behaviour with Consideration of Carbon Content during Hot Forging,Procedia Engineering 207 (2017) 526–531.
[2] R. Kawalla and F. Steinert, Untersuchung des Einflusses von Prozessparametern in der Fertigstraße auf die Tertiärzunderausbildung,Materialwissenschaft und Werkstofftechnik 1 (38) (2007) 36–42.
[3] L. Luong and T. Heijkoop, The influence of scale on friction in hot metal working,Wear 71 (1) (1981) 93–102.
[4] M. Schütze, Mechanical properties of oxide scales,Oxid Met 44 (1-2) (1995) 29–61.
[5] H. Utsunomiya, S. Doi, K. Hara, T. Sakai, and S. Yanagi, Deformation of oxide scale on steel surface during hot rolling,CIRP Annals 58 (1 (2009) 271–274.
[6] G. Korpała, M. Ullmann, M. Graf, H. Wester, A. Bouguecha, B. Awiszus, B.-A. Behrens, and R. Kawalla, Modelling the influence of carbon content on material behavior during forging. DOI=10.1063/1.5008226.
[7] Graf, M. (2013. Modellierung des Umformverhaltens von Zunder entlang der Prozesskette Warmband. Freiberger Forschungshefte. B, Werkstofftechnologie 353. Technische Universität Bergakademie Freiberg, Freiberg.
[8] M. Graf and R. Kawalla, Scale Behaviour and Deformation Properties of Oxide Scale during Hot Rolling of Steel,KEM 504-506 (2012) 199–204.
[9] J. Favergeon, G. Moulin, A. Makni, and L. Lahoche, The Effect of Oxide Scale on the Mechanical Behavior of Low Alloyed Steel at High Temperature, MSF 522-523 (2006) 401–408.
[10] L. Suárez, Y. Houbaert, X. V. Eynde, and R. Colás, High temperature deformation of oxide scale,Corrosion Science 51 (2) (2009) 309–315.
[11] M. Schütze, P. F. Tortorelli, and I. G. Wright, Development of a Comprehensive Oxide Scale Failure Diagram,Oxid Met 73 (3-4) (2010) 389–418.
[12] M. Krzyzanowski and J. H. Beynon, The tensile failure of mild steel oxides under hot rolling conditions,Steel Research 70 (1) (1999) 22–27.
[13] W. Gao, X. Zhang, L. Yang, and H. Liu (2010, An improved Sobel edge detection. In 3rd International Conference on Computer Science and Information Technology. IEEE) 67–71 (2010).
[14] A. Savitzky and M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures,Anal. Chem. 36 (8) (1964) 1627–1639.
[15] R. Jardim and F. Morgado-Dias, Savitzky–Golay filtering as image noise reduction with sharp color reset,Microprocessors and Microsystems 74 (2020).
[16] T. Bergelt, M. Graf, J. N. Hunze, B.-A. Behrens, and T. Lampke, Investigation of scale properties and layer growth depending on the carbon and chromium content in steel, European Oxide Scale Conference (2022).