Additive Manufacturing: Technology Maturation and Synchrotron X-Ray Microscopy Insights

Additive manufacturing (AM), aka 3D printing is a relatively new technology that has given rise to the “maker culture” and an intense interest in design. That has carried over into metals AM, which has jumped almost immediately into manufacturing of actual parts in a variety of alloys. In doing so it has liberated thinking about part design albeit within certain constraints and complex components have been deployed that were previously inaccessible, e.g., high temperature heat exchangers (HX). An example is described of the co-design of HX against printing constraints, alongside evolution in alloy choice. Nothing is ever as simple as it seems, however, and the reliability of parts that must carry load depends on the internal micro-structure, especially with respect to fatigue loading. This motivates detailed study of all aspects of materials microstructure ranging from defect structures to strain, all of which is ideally suited to the use of intense sources of high energy x-rays as only third generation light sources can deliver. Computed tomography (CT) has revealed the presence of porosity in all additively manufactured metals examined to date and confirmed that appropriate process control can limit it. CT has also provided data on surface condition that we are trying to link to fatigue performance. High speed radiography reveals even more crucial details of how laser light generates vapor cavities that can deposit voids past a critical instability point. “Hot” cracking has been imaged as it happens during the solidification process, which offers the possibility finding printing recipes for alloys previously considered off-limits to 3D printing. High speed, high resolution diffraction in stainless steel, alloy 718 and Ti-6Al-4V reveals unexpected solidification and precipitation sequences. Diffraction microscopy reveals the highly strained nature of printed metals and how microstructure and internal strain state evolves during subsequent annealing. Permeating all these activities is machine learning as an invaluable tool and aid to the researcher.<br/><br/>Support from multiple agencies is gratefully acknowledged, including NASA, DOE/BES, DOE/NNSA, ONR, NSF, OEA, Commonwealth of Pennsylvania, and Ametek.