Identification of surface features on cold-rolled stainless steel strip

A novel method based on three-dimensional profilometry data and MATLAB anal ysis software is described to identify surface features on cold-rolled stai nless steel strip. The aim of the method is to detect automatically pits an d roll marks that can be observed in optical or SEM micrographs. Pits are i dentified by locating regions which are significantly deeper than the immed iately adjacent surface. Deep or steep features which extend a significant distance in the direction of rolling are identified as roll marks. Results for typical cold-rolled stainless steel sheet shaw that the algorithms are effective in identifying the more obvious pits and roll marks. By suitable adjustment of the tolerances used in the analysis, the method can be tailor ed to detect less severe features. Application of the method, either for re search purposes or routine industrial inspection will require tuning of the se tolerances to detect pits of the severity relevant to the end use of the strip. The methodology has been applied to a series of rolled strip sample s to track the evolution of pits and roll marks during a schedule. Results show how the initially large area of deep pits is rapidly eliminated and tr ansformed into shallow pits. The pit identification method is used to estim ate the effect of trapped oil on lubrication. Results suggest that this exp elled oil will contribute significantly to the lubrication of the surroundi ng area. Finally, a good correlation is demonstrated between strip surface reflectance measurements and the estimated pit area. (C) 2000 Elsevier Scie nce S.A. All rights reserved.