Uncertainty evaluation of small wear measurements on complex technological surfaces by machine vision-aided topographical methods

Wear assessment is an essential feature within the Industry 4.0 framework to optimise machining and control durability of components made of innovative materials. Complex topographies often make wear measurement a challenging task. Literature tackles it by comparing the final topography with the unworn state, either by empirical methods or by registration via machine vision algorithms. This paper develops a framework to evaluate the related measurement uncertainty, so far lacking, by exploiting instruments metrological characteristics and statistical modelling. This framework is applied to an industrially relevant case study to compare the performances of accredited methods for wear measurement available in literature.