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.