Automated characterisation of crosscut tests for coatings

Scientists have presented a deep learning-based automated characterisation of crosscut tests for coatings via image segmentation.

Lab results are shown on a graphically displayed tablet.
Excited state energies and lifetimes of the thioxanthone derivatives have been determined. Image source: mcmurryjulie - Pixabay (symbol image).

A manual scratch test to measure the scratch resistance of coatings applied to a certain substrate is usually used to test the adhesion of a coating. Despite its significant amount of subjectivity, the crosscut test is widely considered to be the most practical measuring method for adhesion strength with a good reliability. Intelligent software tools help to improve and optimise systems combining chemistry, engineering based on high-throughput formulation screening (HTFS) technologies and machine learning algorithms to open up novel solutions in material sciences. Nevertheless, automated testing often misses the link to quality control by the human eye that is sensitive in spotting and evaluating defects as it is the case in the crosscut test.

Deep convolutional networks

Researchers now present a method for the automated and objective characterisation of coatings to drive and support Chemistry 4.0 solutions via semantic image segmentation using deep convolutional networks. The algorithm evaluated the adhesion strength based on the images of the crosscuts recognising the delaminated area and the results were compared with the traditional classification rated by the human expert.

The study has been published in Journal of Coatings Technology and Research, Volume 19, 2022.

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