Data and Analysis Code for Kantzos 2019: Design of Interpretable CNN. . .

Paper Abstract: We present the application of a Convolutional Neural Network (CNN) to relate stress concentrations to surface roughness. Stress concentrations at the low points of rough surfaces are one of the primary causes of fatigue crack initiation but there is no generally accepted way to analyze rough surfaces to predict crack initiation. Synthetically generated rough surfaces, instantiated in a mechanical model allow for the simulation of stress concentrations, creating a database of surface images and corresponding mechanical data. In this work, the CNN is designed and trained to interpret a height map of a surface and, from that data, to predict the stress concentrations created by the surface. Using a simple architecture, the CNN achieved R2 = 0.75 in prediction for test images, i.e., those not used in training. This CNN can be adapted for experimental surfaces thus creating a new and straightforward tool for prediction of crack initiation. Considerable work was taken to minimize the complexity of the CNN architecture and to make it interpretable via viewports.

Herein are 4 files that can be downloaded to analyze the data, reproduce figures from the paper, and train new CNNs. The files are compressed directories.