Robust machine learning algorithms for image segmentation in biphasic ceramics

Historically, the development of armor ceramics can be largely described as heuristic. Recently, advanced machine learning algorithms are being developed to accelerate advanced material discovery. As many important material properties depend on microstructure, segmentation algorithms applied to scanning electron microscope (SEM) images enable quantification of identified features. The desired goal is to relate key image metrics to quantified physical properties and make useful performance predictions and improvements faster than otherwise possible. Collecting large image datasets with high signal to noise ratio, even with automation, can be laborious. Moreover, traditional methods of establishing image ground truth often rely on supervised hand-tracing, which precludes application to 1000's of images. This study creates an approximate ground truth automatically using Otsu's algorithm to evaluate large image data sets with varying signal to noise ratio and understand the influence of noise on network model efficiency. The robustness of a U-net algorithm, commonly used for image segmentation, was optimized by introducing artificial noise to the training data. Initial work assessed the performance of generative adversarial networks in applying artificial noise to the images. Next, a U-net was generated while incorporating artificial and real noise into the training and validation sets respectively. The impact of training using artificial noise upon the accuracy of the resulting U-Net in segmenting real images of low quality are described below.