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Traditional design and qualification methodologies for parts manufactured by traditional methods are being applied to Additive Manufacturing (AM) without understanding the nuances of the machines. While mapping process variables and tracking build data is helpful, some variables such as build geometry, support structure, and part melt order have not been researched in depth. Changing these variables can result in significant variations in material properties and defect structure such that the process appears to be unreliable compared to traditional manufacturing. Therefore, this research focuses on the need to understand the effects of overlooked variables such as melt order and nested geometry on the distribution of defects and bulk material properties in Ti-6Al-4 V alloy builds manufactured using the Arcam AB ® electron beam powder bed fusion process 1 . This study collected and analyzed process log data and near infrared (NIR) images for every layer to correlate trends in porosity formation and mechanical performance. The location of pores, while naturally stochastic, is heavily influenced by the cross-sectional area as detected by NIR images and correlates with the failure sites from uniaxial testing.

Local microstructure control in electron beam powder bed fusion (EB-PBF) is of great interest to the additive manufacturing community to realize complex part geometry with targeted performance. The local microstructure control relies on having a detailed understanding of local melt pool physics (e.g., 3-D melt pool shape as well as spatial and temporal variations of thermal gradient (G) and solidification rate (R)). In this research, a new scan strategy referred to as ghost beam is numerically evaluated as a candidate to achieve the targeted G and R of IN718 alloy. The boundary conditions for simulations, including the speed (490 mm/s) and spatial locations of the beam within a given layer, are obtained by using series of snapshot images, recorded at 12,000 frames per second, using a high-speed camera. The heat transfer simulations were performed using TRUCHAS an open-source software deployed within a high-performance computational infrastructure. The simulation results showed that reheating at short beam on-time and time delay decreases both G and R. Local variation of R at the center of the melt pool trailing edge showed periodic temporal fluctuations. Finally, the ghost beam scan strategy was compared to other existing raster and spot scan strategies.

The use of manufacturing to generate topology optimized components shows promise for designers. However, designers who assume that additive manufacturing follows traditional manufacturing techniques may be misled due to the nuances in specific techniques. Since commercial topology optimization software tools are neither designed to consider orientation of the parts nor large variations in properties, the goal of this research is to evaluate the limitations of an existing commercial topology optimization software (i.e. Inspire®) using electron beam powder bed fusion (i.e. Arcam®) to produce optimized Ti-6Al-4V alloy components. Emerging qualification tools from Oak Ridge National Laboratory including in-situ near-infrared imaging and log file data analysis were used to rationalize the final performance of components. While the weight savings of each optimized part exceeded the initial criteria, the failure loads and locations proved instrumental in providing insight to additive manufacturing with topology optimization. This research has shown the need for a comprehensive understanding of correlations between geometry, additive manufacturing processing conditions, defect generation, and microstructure for characterization of complex components such as those designed by topology optimization.

High-temperature Al-9Cu-6Ce and Al-9Cu-6Ce-1Zr (wt%) alloys were designed for fabrication with laser powder bed fusion additive manufacturing (AM). An ultra-fine eutectic structure comprising FCC-Al and particles of a previously unidentified Al8Cu3Ce intermetallic phase was obtained with an inter-particle spacing of approximately 280 nm. The inherent hot-tearing resistance of the eutectic alloys resulted in > 99.5% relative density. A thermodynamic model suggested improved hot-tearing resistance of the present alloys relative to the benchmark AM AlSi10Mg alloy. The Al-Cu-Ce alloy exhibited superior thermal stability with approximately 75% of the as-fabricated hardness retained after 200 h exposure at 400 °C, owed to the coarsening resistance of the intermetallic particles. The Al-Cu-Ce-Zr alloy age-hardened through precipitation of nanoscale Al3Zr precipitates. The aged microstructure was stable at 350 °C with a 13% higher hardness after 200 h exposure compared to the as-fabricated condition. The combined influence of ultra-fine spacing and coarsening resistance of the intermetallic particles resulted in the higher yield strength of the Al-Cu-Ce and Al-Cu-Ce-Zr alloys compared to AM AlSi10Mg and Scalmalloy at temperatures greater than 200 °C. This work essentially demonstrates that thermally stable Al alloys with exceptional mechanical properties can be produced by additive manufacturing.

Spatial control of microstructure within a three-dimensional component has been a dream of materials scientists for centuries. However, limitations in traditional manufacturing processes prevent detailed control over the distribution of microstructures in a single part. Here, we demonstrate the ability to control grain structure and crystallographic texture during metal additive manufacturing for arbitrary cross-sections of a practical size, with profound implications for the design and optimization of next-generation products. The key to this advance is a new geometry agnostic scan path algorithm that manipulates the spatial distribution of solidification conditions. Utilizing a fundamental understanding of solidification dynamics and a model of the heat transfer during processing, we have designed this algorithm to manipulate the natural competition between epitaxial dendrite growth and grain nucleation. With this algorithm, we successfully controlled the grain structure of Ni-based superalloy IN718 in the shape of the Mona Lisa.

A novel technique was developed to control the microstructure evolution in Alloy 718 processed using Electron Beam Melting (EBM). In situ solution treatment and aging of Alloy 718 was performed by heating the top surface of the build after build completion scanning an electron beam to act as a planar heat source during the cool down process. Results demonstrate that the measured hardness (478 ± 7 HV) of the material processed using in situ heat treatment similar to that of peak-aged Inconel 718. Large solidification grains and cracks formed, which are identified as the likely mechanism leading to failure of tensile tests of the in situ heat treatment material under loading. Despite poor tensile performance, the technique proposed was shown to successively age Alloy 718 (increase precipitate size and hardness) without removing the sample from the process chamber, which can reduce the number of process steps in producing a part. Tighter controls on processing temperature during layer melting to lower process temperature and selective heating during in situ heat treatment to reduce over-sintering are proposed as methods for improving the process.

Electron beam melting (EBM) is a metal powder bed fusion additive manufacturing (AM) technology that makes possible the fabrication of three-dimensional near-net-shaped parts directly from computer models. EBM technology has been continuously evolving, optimizing the properties and the microstructure of the as-fabricated alloys. Ti-6Al-4V ELI (Extra Low Interstitials) titanium alloy is the most widely used and studied alloy for this technology and is the focus of this work. Several research works have been completed to study the mechanisms of microstructure formation, evolution, and its subsequent influence on mechanical properties of the alloy. However, the relationship is not completely understood, and more systematic research work is necessary in order to attain a better understanding of these features. In this work, samples fabricated at different locations, orientations, and distances from the build platform have been characterized, studying the relationship of these variables with the resulting material intrinsic characteristics and properties (surface topography, microstructure, porosity, micro-hardness and static mechanical properties). This study has revealed that porosity is the main factor controlling mechanical properties relative to the other studied variables. Therefore, in future process development, decreasing the porosity should be considered the primary goal in order to improve mechanical properties.

Recycling metal powders in the Additive Manufacturing (AM) process is an important consideration in affordability with reference to traditional manufacturing. Metal powder recyclability has been studied before with respect to change in chemical composition of powders, effect on mechanical properties of produced parts, effect on flowability of powders and powder morphology. However, these studies involve ex situ characterization of powders after many use cycles. In this paper, we propose a data-driven method to understand in situ behavior of recycled powder on the build platform. Our method is based on comprehensive analysis of log file data from various sensors used in the process of printing metal parts in the Arcam Electron Beam Melting (EBM) ® system. Using rake position data and rake sensor pulse data collected during Arcam builds, we found that Inconel 718 powders exhibit additional powder spreading operations with increased reuse cycles compared to Ti-6Al-4V powders. We substantiate differences found in in situ behavior of Ti-6Al-4V and Inconel 718 powders using known sintering behavior of the two powders. The novelty of this work lies in the new approach to understanding powder behavior especially spreadability using in situ log file data that is regularly collected in Arcam EBM® builds rather than physical testing of parts and powders post build. In addition to studying powder recyclability, the proposed methodology has potential to be extended generically to monitor powder behavior in AM processes.

A computational modeling approach to simulate residual stress formation during the electron beam melting (EBM) process within the additive manufacturing (AM) technologies for Inconel 718 is presented in this paper. The EBM process has demonstrated a high potential to fabricate components with complex geometries, but the resulting components are influenced by the thermal cycles observed during the manufacturing process. When processing nickel based superalloys, very high temperatures (approx. 1000 °C) are observed in the powder bed, base plate, and build. These high temperatures, when combined with substrate adherence, can result in warping of the base plate and affect the final component by causing defects. It is important to have an understanding of the thermo-mechanical response of the entire system, that is, its mechanical behavior towards thermal loading occurring during the EBM process prior to manufacturing a component. Therefore, computational models to predict the response of the system during the EBM process will aid in eliminating the undesired process conditions, a priori, in order to fabricate the optimum component. Such a comprehensive computational modeling approach is demonstrated to analyze warping of the base plate, stress and plastic strain accumulation within the material, and thermal cycles in the system during different stages of the EBM process.