Additive Manufacturing Benchmark Research Articles

A Dynamic Volumetric Heat Source Model for Laser Additive Manufacturing

Melt pool scale models of laser powder bed fusion (LPBF) offer insights into the process-structure-property relationships in additive manufacturing (AM). These models often neglect physical phenomena such as vapor cavity formation and fluid mechanics to reduce computational demands. Instead, volumetric heat source models are used to represent the effects that these phenomena have on the predicted melt pool dimensions. Generally, the dimensions and effective absorption of the volumetric heat source are calibrated to reproduce melt pool dimensions observed in metallographic cross sections taken from single-track experiments on bare plate. However, the transient nature of LPBF often deviates the melt pool dimensions from the assumed steady-state conditions of single-track experiments, motivating the need for a volumetric heat source model that more generally considers the dynamic relationship between melt pool shape and laser-material interactions. Here, we introduce a two-parameter volumetric heat source model that integrates several existing models into a generalized mathematical expression, providing independent control over the radial heat distribution via the parameter k and the volumetric shape of the heat source via the parameter m. This parameterization enables the calibration of melt pool shape predictions through simultaneous adjustment of these parameters, while keeping the radial heat source dimensions consistent with the experimental spot size (D4σ) and constraining the heat source depth and absorption to physically derived expressions for cavities. Consequently, the proposed volumetric heat source model adapts to changes in the local melt pool conditions due to scanning strategy and part geometry by dynamically adjusting the heat source depth and absorption. We demonstrate the capabilities of the proposed model through comparisons with a collection of experiments from the Additive Manufacturing Benchmark (AMBench).

Multi-physics modeling of the 2022 NIST additive manufacturing benchmark (AM-Bench) test series

Multi-physics modeling of the 2022 NIST additive manufacturing benchmark (AM-Bench) test series

Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

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.

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

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.

A highly efficient computational approach for fast scan-resolved microstructure predictions in metal additive manufacturing on the scale of real parts

In metal additive manufacturing (AM), fast and efficient simulation approaches are essential to explore the full potential of these promising processes, particularly in generating components with tailored microstructures via laser powder bed fusion (LPBF). Due to the inherent multiscale nature of LPBF, existing approaches often need to resort to strong simplifications, such as layer-wise heating models, to make part-scale simulations feasible. In contrast, the present article proposes a scan-resolved approach, which consistently resolves the laser scan path in a coupled thermo-microstructural model of LPBF. Building on a high-performance computing model for the thermal problem, we propose a highly efficient implementation of a recently developed microstructure model for Ti-6Al-4V with three main constituents: stable αs-phase, martensitic αm-phase and β-phase. The implementation is tailored to modern hardware features using vectorization and fast approximations of transcendental functions. A performance model and selected numerical examples of LPBF manufacturing of parts on the centimeter scale are studied to verify the high degree of optimization. Depending on the specific example, results were obtained with moderate computational resources in a few hours to days. We demonstrate how the proposed scan-resolved model allows us to predict the correlation between scan strategy and resulting microstructure composition, an aspect that layer-wise heating models cannot capture. The numerical examples include scan-resolved thermo-microstructure simulations of the full NIST AM Benchmark cantilever specimen. It is shown that varying the build plate temperature by only 100K can significantly change the microstructure composition from αm- to αs-dominated.

Location-Specific Microstructure Characterization Within AM Bench 2022 Laser Tracks on Bare Nickel Alloy 718 Plates

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.

Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges

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.

Physics guided heat source for quantitative prediction of IN718 laser additive manufacturing processes

Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developed Additive Manufacturing Computational Fluid Dynamics code (AM-CFD) combined with a cylindrical heat source is implemented to accurately predict these experiments. Heuristic heat source calibration is proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model are initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agrees quantitatively with NIST measurements for different process conditions (laser spot diameter, laser power, and scan speed). A scaling law based on keyhole formation is also utilized in calibrating the parameters of the cylindrical heat source and predicting the challenge experiments. In addition, an improvement on the heat source model is proposed to relate the Volumetric Energy Density (VEDσ) to the melt pool aspect ratio. The model shows further improvement in the prediction of the experimental measurements for the melt pool, including cases at higher VEDσ. Overall, it is concluded that the appropriate selection of laser heat source parameterization scheme along with the heat source model is crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations.

Providing a Rigorous Benchmark Measurement Foundation for Modeling-Informed Qualification and Certification of Metal Additive Manufactured 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.

A highly efficient computational approach for fast scan-resolved simulations of metal additive manufacturing processes on the scale of real parts

This article proposes a novel high-performance computing approach for the prediction of the temperature field in powder bed fusion (PBF) additive manufacturing (AM) processes. In contrast to many existing approaches to part-scale simulations, the underlying computational model consistently resolves physical scan tracks without additional heat source scaling, agglomeration strategies or any other heuristic modeling assumptions. A growing, adaptively refined mesh accurately captures all details of the laser beam motion. Critically, the fine spatial resolution required for resolved scan tracks in combination with the high scan velocities underlying these processes mandates the use of comparatively small time steps to resolve the underlying physics. Explicit time integration schemes are well-suited for this setting, while unconditionally stable implicit time integration schemes are employed for the interlayer cool down phase governed by significantly larger time scales. These two schemes are combined and implemented in an efficient fast operator evaluation framework providing significant performance gains and optimization opportunities. The capabilities of the novel framework are demonstrated through realistic AM examples on the centimeter scale including the first scan-resolved simulation of the entire NIST AM Benchmark cantilever specimen, with a computation time of less than one day. Apart from physical insights gained through these simulation examples, also numerical aspects are thoroughly studied on basis of weak and strong parallel scaling tests. As potential applications, the proposed thermal PBF simulation approach can serve as a basis for microstructure and thermo-mechanical predictions on the part-scale, but also to assess the influence of scan pattern and part geometry on melt pool shape and temperature, which are important indicators for well-known process instabilities.

Additive Manufacturing Benchmark 2022 Subcontinuum Mesoscale Tensile Challenge (CHAL-AMB2022-04-MeTT) and Summary of Predictions.

This additive manufacturing benchmarking challenge asked the modelling community to predict the stress-strain behavior and fracture location and pathway of an individual meso-scale (gauge dimensions of approximately 200 μm thickness, 200 μm width, 1mm length) tension specimen that was excised from a wafer of nickel allow IN625 manufactured by laser powder bed fusion (L-PBF). The data used for the challenge questions and answers are provided in a public dataset (https://data.nist.gov/od/id/mds2-2587). Testing models against the data is still possible, although a good-faith blinded prediction should be attempted before reading this article, as the results are contained herein. The uniaxial tension test was pin loaded, conducted at quasi-static strain rates under displacement control, and strain was measured via non-contact methods (digital image correlation). The predictions are challenging since the number of grains contained in the thickness of the specimen are sub-continuum. In addition, pores can be heterogeneously distributed by the L-PBF process, as opposed to intentionally seeded defects. The challenge provided information on chemical composition, grain and subgrain structure (surface-based measurements via electron backscatter diffraction and scanning electron microscopy) and pore structure (volume-based measurements via X-Ray computed tomography) along the entire gauge length for the tension specimen. During the challenge, prediction responses were collected from six different groups. Prediction accuracy compared to the measurements varied, with elastic modulus and strain at ultimate tensile strength consistently over-predicted, while most other values were a mix of over- and under-predicted. Overall, no one model performed best at all predictions. Failure-related properties proved quite challenging to predict, likely in part due to the data provided as well as the inherent difficulty in predicting fracture. Future directions and areas of improvement are discussed in the context of improving model maturity and measurement uncertainty.

Evaluate the effect of melt pool convection on grain structure of IN625 in laser melting process using experimentally validated process-structure modeling

During the laser based manufacturing process such as metal additive manufacturing (AM) and laser cladding, enhancing or inhibiting liquid metal flow within the laser-induced melt pool provides a promising approach to tune material microstructure and the resulting mechanical properties. However, the effect of convection flow, specifically the dominant Marangoni convection flow in the melt pool, on the as-solidified material microstructure is still vague. This study aims to reveal convection-modified grain evolution in the laser melting process, e.g., the selective laser melting AM. We use a process-microstructure model and systematically design comparative simulation cases (with and without convection flow) to identify the effects of convection flow on melt pool geometry, solidification conditions, and as-solidified grain structure formation. The model is validated by secondary electron images and electron backscatter diffraction of the laser melted IN625 alloy provided by NIST Additive Manufacturing Benchmark Test Series. It is found that the strong Marangoni convection flow can widen the melt pool and notably affect the solidified microstructure in terms of grain growth directions and bulk nucleation events. This study provides a quantitative basis for controlling the as-solidified microstructure by manipulating the convection flow in the laser-induced melt pool.

An additive manufacturing benchmark artifact and deviation measurement method

Additive manufacturing (AM) is established as a new class for fabricating 3D physical prototypes layer by layer. Given that a large number of different 3D printers (AM machines) are present now (as a product of renowned manufacturers or custom-made products), the design of a benchmark artifact for evaluation of the AM processes is very important. This implies quality evaluation of the capabilities and limitations of each AM process and also the geometrical testing of the 3D printers. The paper proposes a benchmark artifact, according to the criteria for modeling of the benchmark artifact, with a large number of basic features for its geometrical evaluation. Although there are a large number of commercial software packages, this paper also proposes a low-cost developed measurement method of geometrical deviation that could be also adjustable for requirements in an easy way. Verification of the proposed deviation measurement method has been done by several experiments. The experiments include fabrication of the proposed benchmark artifact on available 3D printers by an FFF (fused filament fabrication) process also known as FDM (fused deposition modeling), and after that its scanning. Verification is done by comparing quality evaluation of each fabricated part by developed method and commercial software.

Location-Specific Microstructure Characterization Within IN625 Additive Manufacturing Benchmark Test Artifacts

Additive manufacturing (AM) of alloys creates segregated microstructures with significant differences from those of traditional wrought alloys. Understanding how the local build conditions generate specific microstructures is essential for developing post-build heat treatments with the goal of producing parts with reliable and predictable properties. This research examines the position- and orientation-dependent microstructures within IN625 Additive Manufacturing Benchmark Test specimens, including three-dimensional AM builds and individual laser traces on bare metal plates. Detailed characterizations of the solidification microstructures, compositional heterogeneities, grain structures and orientations, and melt pool geometries are described.

In Situ Measurements of Melt-Pool Length and Cooling Rate During 3D Builds of the Metal AM-Bench Artifacts

This work provides results and analysis of the in situ thermal measurement acquired during the 3D builds performed for the 2018 additive manufacturing benchmark tests. The objective is to provide context for post-process characterization of distortion, residual strain, and microstructure, which are reported elsewhere in this Journal, and to provide validation data for thermal models of the build process. Four bridge artifacts (75 mm long, 5 mm wide, 12.5 mm tall) are created in each of three builds using a commercial laser powder bed fusion system. The builds are performed using nickel super alloy 625 (IN625). High-speed infrared thermography performed during four of the builds is used to measure the melt-pool length and cooling rate within a select region. The temperature of the substrate and build volume is measured during the fifth build to provide data to establish the boundary conditions for thermal models.

Outcomes and Conclusions from the 2018 AM-Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

The Additive Manufacturing Benchmark (AM-Bench) test series was established to provide rigorous measurement test 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 2018, the first round of AM-Bench measurements and the first AM-Bench conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both LPBF and material extrusion processing of polymers. In all, 46 blind modeling simulations were submitted by the international AM community in comparison with the in situ and ex situ measurements. Analysis of these submissions provides valuable insight into existing AM modeling capabilities. The AM-Bench data are permanently archived and freely accessible online.

Topographic Measurement of Individual Laser Tracks in Alloy 625 Bare Plates

Additive manufacturing (AM) combines all of the complexities of materials processing and manufacturing into a single process. The digital revolution made this combination possible, but the commercial viability of these technologies for critical parts may depend on digital process simulations to guide process development, product design, and part qualification. For laser powder bed fusion (LPBF), one must be able to model the behavior of a melt pool produced by a laser moving at a constant velocity over a smooth bare metal surface before taking on the additional complexities of this process. To provide data on this behavior for model evaluations, samples of a single-phase nickel-based alloy were polished smooth and exposed to a laser beam at 3 different power and speed settings in the National Institute of Standards and Technology (NIST) Additive Manufacturing Metrology Testbed (AMMT) and a commercial AM machine. The solidified track remaining in the metal surface after the passing of the laser is a physical record of the position of the air-liquid-solid interface of the melt pool trailing behind the laser. The surface topography of these tracks was measured and quantified using confocal laser scanning microscopy (CLSM) for use as benchmarks in AM model development and validation. These measurements are part of the Additive Manufacturing Benchmark Test Series (AM-Bench).

Residual Strain Predictions for a Powder Bed Fusion Inconel 625 Single Cantilever Part

The laser powder bed fusion (LPBF) process involves using a laser beam to selectively melt metal powder with a desired shape on a substrate to create a part layer-by-layer. As an Additive Manufacturing (AM) process, laser powder bed fusion (commonly referred to as selective laser melting—SLM) offers superior design freedom over conventional manufacturing methods and enables the production of complex, lightweight geometries with applications in the aerospace, automotive, and biomedical industries. In addition to enhanced design freedom, AM technologies provide improved material utilization and allow for reduced assembly needs. However, the reliability and repeatability of additively manufactured parts is a challenge to the wide-scale adoption of the technology for safety critical parts. A critical limitation of process optimization is the prediction and control of residual stresses, distortion, and microstructure evolution. This work focuses on the development and implementation of a numerical modeling technique for the prediction of residual strains within an Inconel 625 LPBF part. The model, using the SIMULIA Additive Manufacturing Scenario App, based on the Abaqus 2018 finite element solver, was developed and analyzed as part of a submission for the NIST AM Benchmark 2018. A sequentially coupled thermo-mechanical analysis was adopted to replicate the building conditions of a single cantilever beam built at the NIST laboratories. The results of the blind study were compared to X-ray diffraction (XRD) measurements of the physical build. The predicted three-dimensional residual strain field showed a high level of accuracy and the submission described in this paper received joint first prize in the residual elastic strain category of the NIST AM Benchmark 2018. The results presented in this paper reflect only findings before the benchmark measurements were posted on the NIST website.

Accurate Prediction of Melt Pool Shapes in Laser Powder Bed Fusion by the Non-Linear Temperature Equation Including Phase Changes

In this contribution, we validate a physical model based on a transient temperature equation (including latent heat) w.r.t. the experimental set AMB2018-02 provided within the additive manufacturing benchmark series, established at the National Institute of Standards and Technology, USA. We aim at predicting the following quantities of interest: width, depth, and length of the melt pool by numerical simulation and report also on the obtainable numerical results of the cooling rate. We first assume the laser to posses a double ellipsoidal shape and demonstrate that a well calibrated, purely thermal model based on isotropic thermal conductivity is able to predict all the quantities of interest, up to a deviation of maximum 7.3\% from the experimentally measured values. However, it is interesting to observe that if we directly introduce, whenever available, the measured laser profile in the model (instead of the double ellipsoidal shape) the investigated model returns a deviation of 19.3\% from the experimental values. This motivates a model update by introducing anisotropic conductivity, which is intended to be a simplistic model for heat material convection inside the melt pool. Such an anisotropic model enables the prediction of all quantities of interest mentioned above with a maximum deviation from the experimental values of 6.5\%. We note that, although more predictive, the anisotropic model induces only a marginal increase in computational complexity.

Benchmark Study of Thermal Behavior, Surface Topography, and Dendritic Microstructure in Selective Laser Melting of Inconel 625

In the NIST additive manufacturing benchmark (AM-Bench) experiments, melt pool geometry, cooling rates, surface topography, and dendritic microstructure in laser melted Inconel 625 were used to challenge and validate computational models of the melting and solidification process. To this end, three thermal models incorporating different physics are compared with the experimental data. It is identified that the heat convection enhanced by the thermocapillary flow inside the melt pool and heat loss caused by vaporization play pivotal roles to guarantee the accuracy of the predictions, and thus should be considered in the thermal model. Neglecting fluid flow and vaporization leads to nearly 100% difference in cooling rate during solidification, and 20% difference in cooling rate after solidification from the results. With the most accurate thermal model, surface topographies of the melt tracks are predicted and quantitatively analyzed. Using the Kurz-Fisher model, the primary dendrite arm spacing is predicted based on the thermal gradient and solidification rate predictions, while elemental segregation is predicted using the Scheil-Gulliver model and a non-equilibrium solidification model. Additionally, it is shown that increasing scan speed inhibits elemental microsegregation.