Residual elastic strains and the resulting stresses are key aspects of most metal additive manufacturing (AM) approaches and strongly affect both the build process and part performance. Computational modeling of these stresses allows the manufacturer to compensate for the distortions that occur during the build process and to predict where part failures may occur. Such simulations must be validated against rigorous measurement datasets. Here, energy dispersive synchrotron X-ray diffraction is used to characterize the location-specific elastic strains within additively manufactured laser powder bed fusion (PBF-LB) nickel Alloy 718 test artifacts. Elastic strains are measured along two orthogonal directions from 2248 measurement locations and the measurement uncertainties are assessed. Comparisons are made between the measurement results and modeling predictions submitted by the AM modeling community. These measurements are part of the Additive Manufacturing Benchmark Test Series (AM Bench), a broad effort to produce measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many AM build methods and material classes. All the measurement data are available online with download links at www.nist.gov/ambench .

Metal additive manufacturing (AM) offers a unique opportunity for production of advanced materials and complex geometries. However, variability in microstructure and properties challenges conventional approaches to design, process optimization, qualification, and materials selection. Modeling and simulation can improve understanding of AM processing and materials, but also poses major challenges for existing computational methods. Simultaneously, modern scientific computing hardware has become increasingly complex, most notably with the adoption of hybrid architectures such as Graphical Processing Units (GPUs). If appropriately utilized, emerging computational capabilities provide an opportunity to reveal new insight into AM processing and the resulting material structure and properties. In this review we describe the computational AM landscape, identify critical gaps, and highlight opportunities to impact the development and application of AM. First, the requirements and challenges of representative AM problem statements will be defined. These problems range from scientific studies to industrial applications and are designed to capture the breadth of challenges facing the AM community. Next, the current state of AM modeling and simulation is evaluated, broken down by enabling hardware and software, process simulation, microstructure simulation, and property simulation. Each section describes the diversity of simulation approaches and associated trade-offs in physical fidelity and computational expense. Each area is then assessed based on their suitability and readiness for current and developing computational architectures. Lastly, the greatest opportunities for future research and application are highlighted, including gaps in modeling capabilities, opportunities for near-term application, and key scientific challenges.

Additive manufacturing of metal alloys produces microstructures that are typically very different from those produced by more traditional manufacturing approaches. Computer simulations are useful for connecting processing, structure, and performance for these materials, but validation data that span this full range is difficult to produce. This research is part of a broad effort by the Additive Manufacturing Benchmark Test Series to produce such datasets for laser powder bed fusion builds of nickel Alloy 718. Here, single laser tracks produced with variations in laser power, scan velocity, and laser diameter, and arrays of adjacent laser tracks on bare wrought Alloy 718 plates are examined using optical microscopy, electron backscatter diffraction, and energy dispersive spectroscopy.

Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. We employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.

The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

Metal additive manufacturing (AM) is a transformative set of technologies that are increasingly being used for demanding structural applications. However, persistent challenges regarding reliability and properties of the printed parts seriously impact qualification and certification (Q&C). Computational approaches can mitigate these challenges, but availability of benchmark measurement data for model validation is a key requirement. Q&C will be discussed in the context of the Computational Materials for Qualification and Certification (CM4QC) steering group, a tightly focused collaboration of aviation-focused companies, research and regulatory government agencies, and universities that is working to develop a roadmap for increasing the use of computational approaches in the aviation Q&C process. Benchmark measurement data will be discussed in the context of the Additive Manufacturing Benchmark Test Series (AM Bench), a broad collaboration of 10 NIST divisions and about 20 external organizations, including several that are collaborators on CM4QC, that provide rigorous measurement data for validating AM simulations for a wide range of AM technologies and material systems. Technical standards also play an important role for Q&C and the confluence between CM4QC, AM Bench, and standards organizations will be discussed.

The Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench .

The current study aims to enhance our understanding of process-microstructure relationships in laser powder bed fusion (PBF-LB) of Inconel 718, utilizing computational fluid dynamics (CFD), phase field modeling, and machine learning. We developed a precise CFD model to simulate critical thermal dynamics in PBF-LB, addressing challenges posed by extreme temperature gradients and cooling rates. A high-throughput phase field model was then employed to predict microstructural evolution, focusing on the effects of solidification velocity on nucleation, grain growth, and component distribution. Additionally, we introduced the diffusion probabilistic field model (Diff-PFM), a machine learning model based on deep generative modeling. Trained on over 400 simulations, this model replicates intricate microstructural features and validates against experimental EBSD measurements. The integration of these sophisticated models establishes a robust framework for accurately predicting and controlling microstructure in LPBF processes, offering essential insights for the design of high-performance components and setting a new exemplar for additive manufacturing of metal alloys.

The Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of additive manufacturing benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. To this end, single-track (1D) and pad (2D) scans on bare plate nickel alloy 718 were completed with thermography, cross-sectional grain orientation and local chemical composition maps, and cross-sectional melt pool size measurements. The laser power, scan speed, and laser spot size were varied for single tracks, and the scan direction was varied for pads. This article focuses on the cross-sectional melt pool size measurements and presents the predictions from challenge problems. Single-track depth correlated with volumetric energy density while width did not (within the studied parameters). The melt pool size for pad scans was greater than single tracks due to heat buildup. Pad scan melt pool depth was reduced when the laser scan direction and gas flow direction were parallel. The melt pool size in pad scans showed little to no trend against position within the pads. Uncertainty budgets for cross-sectional melt pool size from optical micrographs are provided for the purpose of model validation.

The Additive Manufacturing Benchmark Test Series (AM Bench) provides comprehensive measurement data for additive manufacturing (AM) simulations and modeling. As part of the primary AM Bench 2022 measurements, tensile testing at different strain rates and microstructural characterization on AM nickel alloy 625 (UNS N06625) in the as-built condition are performed. Tensile specimens are built with an X-Y alternating scan strategy on a commercial powder bed fusion machine and machined in accordance with ASTM E8 subsize specimen geometry. Each specimen is tested at a nominal strain rate of 10-3 s-1 or 10-2 s-1 on a servohydraulic material testing machine. Stereo digital image correlation (DIC) is used to report a virtual extensometer strain up to fracture and to show heterogeneous deformation with local crosshatch strain banding throughout the gauge section at both strain rates. Anisotropic deformation is also measured where the principal strains deviate from the isotropic strain path. Microstructural analysis using scanning electron microscopy (SEM) imaging and electron backscatter diffraction (EBSD) are performed to help illuminate the microstructural role in the anisotropy and heterogeneous deformation. Finally, nanoindentation followed by EBSD of the indents are performed to investigate the difference in the indentation response of different grain orientations and grain locations with respect to the laser track.

The explosive growth of additive manufacturing (AM) is matched by an equally strong push in research and development to support those applications. The relative complexity of these manufacturing processes and the materials they generate still elicit a great need for better understanding of the interactions between the fabrication parameters (e.g., material deposition rate, applied thermal energy, etc.) and the material development and evolution. Complex problems require complex tools, and with the boom in AM, the development of computational models and simulations to predict all aspects of these processes has grown in parallel. Incredible diversity exists in the types of models, the level of physical or computational complexity, the range of materials, and applications. But all require something similar: input of or reference to relevant physical values achieved through accurate measurements. Measurements are essential, not just in the development of AM models, but in the testing and validation of their predictions.

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

This report documents the goals, organization and outcomes of a Technical Interchange Meeting (TIM) on Computational Materials Approaches for Qualification by Analysis, co-organized by NASA, NIST and the FAA. The TIM was held at NASA Langley Research Center on January 15-16, 2020. Approximately 60 subject matter experts (SMEs) representing 8 aerospace manufacturers, 7 government organizations and 2 universities participated. Expertise of the SMEs spanned the Technology Readiness Level (TRL) scale from the low-to-mid TRL focus of government laboratories and universities to the high TRL perspective of the regulatory organizations and aerospace manufacturers. During this TIM, the future needs of the government regulators and manufacturers motivated the overall discussion and framed the input given by the participants. Hence, the key objectives of the TIM were to understand existing gaps in model-based, e.g., computational materials, processing and performance predictions for aerospace materials and components and forecast how they can be matured to support material, process and part-level qualification and certification (Q&C). The TIM focused on process-intensive metallic materials technologies, including, but not limited to, additive manufacturing. Participation was roughly evenly divided among the processing and performance tracks, suggesting that both topic areas are generally perceived as being both valuable and requiring additional investment. The output of this TIM may be used by both participating and other organizations, in part, as guidance for future national efforts on maturing computational materials capabilities for use in the Q&C of advanced metallic material systems in aerospace applications.

During the laser-powder bed fusion ( L -PBF) process, high laser intensities, short interaction times and highly localized heat input drive large thermal gradients that result in a state of high residual stresses. Generally, the residual stresses that develop during the L -PBF process can compromise the performance of the component. Up to now, the literature has indicated that the magnitude of the residual stresses can be affected by various process parameters. In this study, all process parameters such as laser power and speed are held fixed and the focus is solely on the effect of the laser scan strategy on the three-dimensional residual stress state of L -PBF metallic components. Four Ti-6Al-4V bridge shaped components were built using island and continuous scanning patterns parallel and offset 45° from the sample axes. High-energy X-ray diffraction was used to determine the residual stress field in each of the components. Two of them were re-measured after being partially removed from the build plate. The assumptions implicit in diffraction measurements of stress are reviewed and discussed in depth because the unique microstructure associated with L-PBF Ti-6Al-4V renders the validity of those assumptions uncertain. Specifically, additional data was collected and analyzed to evaluate the relationship between grain scale and macroscopic scale stresses. The observed residual stresses were large, ½ to ¾ of the yield strength, particularly the build direction stresses near the lateral edges of the bridges. In this work, the higher stresses were observed in the bridges built via the island scan strategies, chiefly near the edges of the parts.

Additive manufacturing (AM) of metals provides great flexibility in manufacturing parts with complex geometrical shapes and is fast becoming an attractive option for the fabrication of high-valued metal components in aerospace, oil & gas, and biomedical industries.The rapid heating and cooling during AM fabrication, which by nature is a highly nonequilibrium process, often leads to significant microstructural heterogeneity uncommon to wrought and cast alloys.Such heterogeneity creates tremendous challenge in the qualification and eventual certification of AM metal parts for many applications.

Inconel 625, a nickel-based superalloy, has drawn much attention in the emerging field of additive manufacturing (AM) because of its excellent weldability and resistance to hot cracking. The extreme processing condition of AM often introduces enormous residual stress (hundreds of MPa to GPa) in the as-fabricated parts, which requires stress-relief heat treatment to remove or reduce the internal stresses. Typical residual stress heat treatment for AM Inconel 625, conducted at 800 °C or 870 °C, introduces a substantial precipitation of the δ phase, a deleterious intermetallic phase. In this work, we used synchrotron-based in situ scattering and diffraction methods and ex situ electron microscopy to investigate the solid-state transformation of an AM Inconel 625 at 700 °C. Our results show that while the δ phase still precipitates from the matrix at this temperature, its precipitation rate and size at a given time are both smaller when compared with their counterparts during typical heat treatment temperatures of 800 °C and 870 °C. A comparison with thermodynamic modeling predictions elucidates these experimental findings. Our work provides the rigorous microstructural kinetics data required to explore the feasibility of a promising lower-temperature stress-relief heat treatment for AM Inconel 625. The combined methodology is readily extendable to investigate the solid-state transformation of other AM alloys.

To understand the process-microstructure relationships in additive manufacturing (AM), it is necessary to predict the solidification characteristics in the melt pool. This study investigates the influence of Marangoni driven fluid flow on the predicted melt pool geometry and solidification conditions using a continuum finite volume model. A calibrated laser absorptivity was determined by comparing the model predictions (neglecting fluid flow) against melt pool dimensions obtained from single laser melt experiments on a nickel super alloy 625 (IN625) plate. Using this calibrated efficiency, predicted melt pool geometries agree well with experiments across a range of process conditions. When fluid mechanics is considered, a surface tension gradient recommended for IN625 tends to overpredict the influence of convective heat transfer, but the use of an intermediate value reported from experimental measurements of a similar nickel super alloy produces excellent experimental agreement. Despite its significant effect on the melt pool geometry predictions, fluid flow was found to have a small effect on the predicted solidification conditions compared to processing conditions. This result suggests that under certain circumstances, a model only considering conductive heat transfer is sufficient for approximating process-microstructure relationships in laser AM. Extending the model to multiple laser passes further showed that fluid flow also has a small effect on the solidification conditions compared to the transient variations in the process. Limitations of the current model and areas of improvement, including uncertainties associated with the phenomenological model inputs are discussed.