Automatic identification of feed marks in machined surface roughness profiles by correlating random variations

Abstract

A new method is detailed to identify the positions of the ends of the tool marks in linear surface roughness profiles. No feed mark shape must be assumed and the process is entirely automated. The approach may find applications in automated quality control, surface texture classification, and modeling of metal cutting processes. Validation was done with 40 finish hard turning specimens. The method relies on the justified hypotheses that a feed mark profile is a superposition of a fixed and a random component, and that the random component has a spatial period equal to one feed mark length. A brief typology of tool mark particularities revealed by the method is presented as well as observations on the correlation of the random events within marks and between marks, both at short and at long range. Feed marks difficult to identify by visual inspection were easily identified with the method and evidence of overlapping tool marks and unstable regions was discovered. The limits of the method are also explored.