Automated evaluation of Rockwell adhesion tests for PVD coatings using convolutional neural networks

An automated method for the classification of the adhesion strength of thin PVD coatings applied on hardened steel substrates is presented in this study using deep neural networks. For the determination of the adhesion strength Rockwell-indentation tests were carried out according to VDI 3198. For this approach, pre-trained convolutional neural networks are adapted to classify microscopic images into the expected adhesion classes HF 1 to HF 6 using transfer learning with a dataset of 1650 already evaluated indentation images. The classification performance of the Matlab implemented network models AlexNet, GoogLeNet and inception-v3 is compared with test and verification images of Rockwell indentations. The inception-v3 network shows good accuracy for polished (roughness Sa < 20 nm), hardened steel substrates with deposited thin coatings of a thickness up to 5 μm. The classifications of the implemented models exhibit an agreement of approximately 85–90% compared to human assessment. The evaluation is robust against disturbance variables such as different exposure times, brightness, image contrasting and magnifications. Different image capture devices can be used with no effect on the classification. The networks show promising results for automated industrial applications, such as in-line adhesion control in coating processes, as they do not require human operator support.