Embracing Deep Learning for Crack Segmentation in SEM Images of Metal Additive Manufacturing

Metal additive manufacturing is an important manufacturing technique due to its cost effective and rapid prototyping capabilities. This technique has been widely used in various industries ranging from aerospace to military defense. Depending on different metal additive manufacturing settings, microstructures, such as cracks, would be generated for finished parts within additive manufacturing procedures. Those microstructures could lead to undesired defects, which may negatively impact the quality of the fabricated products, especially when they are delivered for mission critical tasks. This study developed a deep learning based computer vision approach for microstructure, especially crack recognition in images collected from Scanning Electron Microscope (SEM). Through performing segmentation of cracks in the SEM images of different magnifying factors, manufacturing quality could be administered quantitatively.