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Proceedings Paper

Surface roughness estimation of shot-blasted steel bars using machine vision
Author(s): Sami Lyden; Heikki A. Kalviainen; Jari Nykanen
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Paper Abstract

During the manufacturing process steel bars are cleaned of roll scale by shot blasting, before further processing the bars by drawing. The main goal of this project is to increase the automation of the shot blasting process by machine vision. For this purpose a method is needed for estimating the surface roughness and other anomalies from the steel bars from digital images after the shot blasting. The goal of this method is to estimate if the quality of shot blasting is sufficient considering the quality of the final products after the drawing. In this project a method for normalising the images is considered and several methods for estimating the actual roughness level are experimented. During the experiments a best method was one where the roughness levels are calculated directly from the images as if the images were similar to other measuring sources and the grey-level values in the images represent the deviation on the bar surface. This at least separates the different samples.

Paper Details

Date Published: 25 October 2004
PDF: 12 pages
Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); doi: 10.1117/12.571435
Show Author Affiliations
Sami Lyden, Lappeenranta Univ. of Technology (Finland)
Heikki A. Kalviainen, Lappeenranta Univ. of Technology (Finland)
Jari Nykanen, Imatra Steel (Finland)


Published in SPIE Proceedings Vol. 5608:
Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Roning, Editor(s)

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