A novel method based on three-dimensional profilometry data and MATLAB analysis software is described to identify surface features on cold-rolled stainless steel strip. The aim of the method is to detect automatically pits and roll marks that can be observed in optical or SEM micrographs. Pits are identified by locating regions which are significantly deeper than the immediately 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 show 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 tailored to detect less severe features. Application of the method, either for research purposes or routine industrial inspection, will require tuning of these 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 samples 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 transformed into shallow pits. The pit identification method is used to estimate the effect of trapped oil on lubrication. Results suggest that this expelled oil will contribute significantly to the lubrication of the surrounding area. Finally, a good correlation is demonstrated between strip surface reflectance measurements and the estimated pit area.
|Number of pages||11|
|Publication status||Published - Aug 2000|
- Stainless steel
- Surface characterisation