AM is, without question, a new manufacturing paradigm. In its most unconstrained, futuristic sense (see Figure 1), AM is capable of producing net or near-net shapes: whose features span across length scales (Zhou et al. 2015 Riveiro et al. 2019 Kumar and Maji 2020 Marini and Corney 2020) whose topology may be topologically optimized or, emer- gently, generatively designed (Meng et al. 2020 Liu et al. 2018) whose local materials state1, and thus properties/perfor- mance, may be controlled spatially by tuning the process, post process, and/or composition spaces (Tammas-Williams and Todd 2017 Li et al. 2020) and where the local materials state may be both designed and measured during component manufacture, providing a digital record/twin that can be used to both verify/validate the process space and predict the properties/performance of the part during service. To ensure that the material is of a sufficient quality with respect to the design metrics (such as dimensions, properties, and performance), it is necessary to develop and bring to bear new advanced metrology and evaluation tools during the man- ufacturing process. Among the most promising techniques are those that are based upon conventional NDE approaches, yet their applicability requires a direct connection with the mate- rials state. Within AM, there are new physics that operate, which sci- entists and engineers (and companies/organizations) need to understand. For example, as most AM techniques are fusion- based processes, the composition of the as-deposited material may be different than the composition of the starting powder or wire, through either the preferential loss of some volatile elements or the gettering of other elements from the surround- ing atmosphere (Carroll et al. 2015 Sato and Kuwana 1995 ME | AMNDEOVERVIEW 1 Materials state includes, but is not limited to: composition, solute distributions, microstructure (phases, their size, distribution, and correlations), crystallographic texture, and the presence of defect structures (e.g., dislocations, porosity, interfaces, cracks), across all length scales. This definition follows from materials state awareness (MSA), which is defined as “digitally enabled reliable nondestructive quantitative materials/damage characterization regardless of scale” (Buynak et al. 2008). The materials state is what a manufacturing process produces (or what evolves during service) and is also what governs the performance of the material (Buynak et al. 2008 Jacobs 2014 Aldrin and Lindgren 2018). Figure 1. Wide variety of applications of AM techniques: (a) additively manufactured bridge using the wire arc additive manufacturing (WAAM) technique (b) hydraulic hand 3D printed by Oak Ridge National Laboratory that houses electric motors and hydraulic components inside (c) 3D-printed metallic “space fabric” designed and manufactured by NASA and (d) AM meso-structures in a turbine blade. (Figure 1a is reprinted with permission from Feucht et al. [2020] Figure 1b is reused from Love et al. [2013] under Creative Commons Attribution License (CC BY) Figure 1c is reused from Good and Landau [2017] under Creative Commons Attribution License (CC BY) and Figure 1d is reprinted courtesy of The University of Sheffield.) 46 M A T E R I A L S E V A L U A T I O N • A P R I L 2 0 2 2

Semiatin et al. 2004). And, while much is known about the dynamic nature of the AM process, other research is leading to new insights into the formation and evolution of defects (Kenney et al. 2021 Quintana et al. 2021), the importance of fluid dynamics (Tammas-Williams et al. 2015 Hojjatzadeh et al. 2019) on the molten pool and presence of any keyhole, and the competition between molecular flow of gas and the vapor- ization of elemental species and their combined effect on the proximal powder (Yoder et al. 2021 Ahsan and Ladani 2020). These new physics are being discovered in sophisticated experimental facilities, including high-energy beam lines, where both high spatial and temporal data can be obtained (micrometer and microsecond). The state-of-the-art mea- surements are beginning to be correlated with some NDE approaches, as these are promising methods to correlate the physical mechanisms associated with AM with signals that can be measured during the AM process. It is clear that for both the realization of many of the promises of AM as well as the determination of different physical domains that the NDE approach has an important role to play. This paper consists of two primary components. First, it provides a brief review of some of what is known about the composition–process–materials state–performance rela- tionships in AM. Elements of this first section will include some aspects of NDE techniques, as relevant. New connections between aspects of the materials state and NDE techniques will be presented. Second, it provides a review of the applica- bility of different NDE techniques for both ex situ and in situ assessments of the materials state, and by extension, initial metrics of the quality of the as-manufactured materials and components. A Review of Additive Manufacturing Jim Williams, an internationally renowned expert on titanium, physical metallurgy, and microstructure-property relationships, as well as a former dean at both Carnegie Mellon University and Ohio State University, once provided the most pithy yet useful definition of AM: “It is the opposite of subtractive man- ufacturing.” In addition to its brevity, this “definition” is useful for two reasons. First, it is implicitly broad, as it does not invoke any of the prototypical details that are typically invoked yet constrict our perspective, such as the heat source (such as laser), geometries of the material that is added (such as molten pool), or incoming material type (such as powder). Second, it implies a capability that is important for the NDE community: the addition of volumes of material means that those volumes can be probed in a manner paralleling (following) the AM technique itself, providing a highly detailed perspective of the materials state. The intellectual property history of AM can most clearly be understood based upon this definition. From a certain perspective, civilization’s earliest methods of manufacturing involved AM, as exemplified by coil pots, which permitted indi- viduals to make clay pottery prior to the advent of the potter’s wheel. However, from a modern perspective, the earliest technical basis for metal-based AM is found in a 1920 patent by Ralph Baker (1920), who patented a method to produce dec- orative articles using electric arc welding to deposit beads of material onto previously deposited beads of the same metal. While this method was cited in other welding techniques in the 1960s, the next notable patent came in 1979 from Brown et al. (1979) while working at the United Technologies Corp. on a US Navy–funded project. In 1979, the inventors disclosed a process for the subsequent deposition of metallic layers that would be capable of producing bulk, rapidly solidified metals. In their work, they termed this technique “LAYERGLAZE,” and in their patent, they included the possibilities of multiple heat sources (including both electron beams and lasers) and of multiple material forms (including both powder and wire). While this was not pursued fully at the time, there is a direct connection between this patent and Sandia National Laboratories’ work on a directed energy deposition system with a powder-blown delivery system and a laser energy source known as Laser Engineered Net Shaping (LENS™), which resulted in the first commercial company for metals-based AM, Optomec, and the first commercial sale in 1998 to Ohio State University. Other key technology patents in the 1980s that have benefited the AM community are rooted in polymeric materials, including the work of Hideo Kodama in 1981 (Kodama 1998), Charles Hull’s work in stereolithography in 1984 (Hull 1984), and Charles Hull’s first 3D printer in 1987 (3D Systems 2021). In the earliest days of metals-based AM using LENS systems, there were simultaneous efforts to understand the processing-property space, including the first appearance of metrics that combined key “feed and speed” parameters into energy density terms (Yin and Felicelli 2010 Hofmeister et al. 2001) understand the composition–microstructure–property space, including the use of elemental blends (Schwender et al. 2001 AlMangour et al. 2017) and produce the initial work into producing composi- tionally graded structures (Zhang and Bandyopadhyay 2019 Bandyopadhyay and Heer 2018 Obielodan and Stucker 2013 Balla et al. 2009). Industries and agencies began to fund work to develop the first processing–structure–property databases and began to place AM metallic parts into service (Collins et al. 2014, 2016). Within the past decade, there have been sustained efforts in developing and integrating computational tools to predict the geometry (including distortion and residual stress), microstructure, properties, and performance of AM parts (Smith et al. 2016a King et al. 2015). The level of sophistication and availability of machines is now sufficiently robust that in 2019, it was even shown that it was possible to 3D print and “fly” a certain superhero suit (All3DP 2021). It is difficult to bound the variations of AM systems. The scale of the systems ranges from aerosol jet-like processes, which have submicrometer resolution and are used to manu- facture functional devices, to large-area AM, which produces parts with dimensions of multiple meters (Lim et al. 2012 Williams et al. 2016). While most metals-relevant AM systems involve fusion (pools of liquid metal), there are other inno- vative AM techniques that are solid state (such as the MELD A P R I L 2 0 2 2 • M A T E R I A L S E V A L U A T I O N 47