Use of machine learning for automatic Rockwell adhesion test classification based on descriptive and quantitative features
Currently, thin-film coating adhesion classification is done manually guided by two standards, DIN 4856 and ISO 26443. Both standards provide classification guidance based on set of schematic illustrations. As such the interpretation of these standards is both prone to human error and lacks clear class parametrization, which would be necessary for enabling automatic adhesion classification at a finer resolution. In this paper we introduce a set of hand-crafted features used for the parametrization of the two thin-film hard coating adhesion classification standards, as well as a pipeline for automatic fine resolution adhesion classification. The developed features resemble key characteristics used by experts in the classification process, describing the properties of either delamination or cracking within the sample. Additionally, we explain the necessity for a revised approach to thin-film hard coating adhesion classification, specific requirements that the standard class parametrization process has to fulfill and the steps we have taken in tackling these challenges. The set of proposed features was used to train two different regression models whose results were further used for extensive evaluation of both feature applicability and model performance. The analyses show that the features are suitable for parametrizing standard's classes and highlight that ISO standard requires both delamination and cracking features, whereas DIN requires only delamination. This work introduces a novel method for fine scale thin-film hard coating adhesion classification which has not been available so far. Furthermore, the proposed features introduce a solid base for the definition of a future adhesion classification standard providing class parametrization necessary for automatic fine scale adhesion evaluation.