Metal additive manufacturing (AM) greatly expands the design freedom and near-net shape production of metallic components across multiple length scales. However, defects arising from starting materials, processing conditions, and post-processing may significantly affect the structural integrity and operational performance of metal AM parts (e.g. fatigue, corrosion, permeability). Therefore, accurate detection, characterization, and prediction of defects has great potential for immediate impact in the production of fully-dense and defect free metal AM materials and structures.

We seek to elucidate common defects and defect formation mechanisms encountered in typical metal powder bed AM processes through novel experimental techniques involving synchrotron x-ray computed tomography (mXCT) and dynamic x-ray radiography (DXR). We primarily focus on the nature of porosity transfer to the finished part to investigate the manifestation of defects from the starting material, processing variables, and post-processing treatments. Furthermore, the use of process mapping and geometric modeling to predict common AM defects is also investigated utilizing this fundamental understanding of defect formation. Our preliminary work in this area has focused on the biomedical and aerospace alloy Ti-6Al-4V. Overall, this proof-of-concept work points to the fact that large-scale defects in LPBF materials can be successfully predicted and thus mitigated/minimized via appropriate selection of processing parameters. Future investigations on characterizing and predicting structural defects resultant from residual stress and distortion in complex shapes fabricated by metal AM are currently being undertaken.