Machine Learning‐Based Automatic Scratch Performance Quantification on Polymeric Surfaces

ABSTRACT Quantitative scratch resistance characterization constitutes an important step in the performance evaluation of polymeric surfaces. By applying the standardized ASTM and ISO scratch test methodology, the present work discusses an approach to automate scratch detection by applying a U‐Net convolutional neural network (CNN) based on contrast differences between the scratched region and its surrounding area. The U‐Net architecture is generally suitable for pixel‐wise segmentation due to its encoder–decoder structure, essential for locating and quantifying scratches. Utilizing the contrast differential between a scratch and its background, the model can learn to quantify scratch length and differentiate between subtle surface features. This automated approach will eliminate tedious manual steps by allowing real‐time, high‐throughput analysis of surface defects. These results establish the efficiency of U‐Net for scratch characterization and the possibility of integrating this system for scratch performance evaluation automation, and an eventual artificial intelligence design of scratch‐resistant polymeric systems.