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The 3D metal additive manufacturing processes such as Laser Powder Bed Fusion requires measurement and characterization of process signatures in layer-to-layer, stripe-to-stripe, and tract-to track fusion of powder metal material with geometric parameters (width, length and overlap), identifying pores and defects from images and image streams obtained using digital optical microscopy, scanning electron microscopy or focus variation microscopy techniques and digital image correlation which is an optical method that employs tracking and image registration techniques for accurate 2D and 3D measurements of changes in images. The goal of this research project is to conduct in-situ (in-process) and automated post-process measurement and associated analysis of the quality in 3D fabricated metal parts by the development of image processing and statistical learning methods to correlate the process input parameter to the evaluated surface areal texture of the fabricated parts.
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