Machine Learning-Based Classification of Irregular Shape Defects in Metal Additive Manufacturing

We are developing algorithms for recognition of internal material defects in metal additive manufacturing from images obtained with pulsed thermal tomography (PTT). In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. Training the CNN on irregular defect shapes, such as shapes imported from scanning electron microscopy (SEM) images of metallic laser powder bed fusion (LPBF)-printed specimens, would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. In one approach, we have developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. In another approach, we are generating synthetic irregular-shaped defects, which have similar metrics (fractal dimension and area) as SEM images. Preliminary results demonstrate viability of these approaches.