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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Freeform thermoelectrics in single-step manufacturing: additive manufacturing of bismuth-telluride thermoelectrics

The project succeeded in producing crack-free Bismuth Telluride thermoelectric parts with density exceeding 98% through laser powder bed fusion (LPBF) additive manufacturing (AM). This greatly exceeded the highest previously reported density of 88% and is the highest among all semiconducting materials processed by LPBF. The additively manufactured material shows comparable Seebeck coefficient as conventional form and can be made into complex geometries with reduced material loss. On the other hand, measured properties are dramatically sensitive to the AM process parameters used, such that with identical composition, the Seebeck coefficient can be controllably tuned from +120 µV/K to -207 µV/K, which means the material switches between an n-type to a p-type semiconductor depending on processing. These changes are accompanied by significant differences in the as-processed microstructure due to rapid solidification. A machine learning protocol was developed and greatly reduced the experimental burden of the project, reducing the typical process optimization period of 2 years to 6 months. The project was fully successful in the objective of producing defect-free, complex geometry of bismuth-telluride parts through LPBF, but only partially successful in achieving performance goals. First, cost reduction of manufacturing, as measured by material waste, was successfully reduced by up to 70% compared to conventional manufacturing methods. This exceeded the proposed 30% reduction in materials waste needed to reach the 20% cost reduction goal of the project. On the other hand, the device performance, as measured by Seebeck coefficient, failed to reach the 40% improvement in efficiency. Rather, we observe comparable Seebeck coefficient between AM samples and conventionally processed counterparts. The device-level efficiency improvement does exceed 40% for complex geometry samples due to shape-induced increase in temperature gradients, but this was not the originally proposed metric. The machine learning approach developed in this project greatly accelerated the process optimization and can be adopted for fast development of AM processing parameters for other brittle and otherwise difficult-to-print materials. For the public, we deliver an efficient and widely adoptable process for incorporating waste-heat harvesting thermoelectric devices in both industrial and commercial heat exchangers. The geometric flexibility allows the capturing device to conform to the shape of the heat source to improve the system-level conversion efficiency. The technique can be deployed on any commercial LBPF systems with zero modifications, thus poses minimal adoption barrier for any manufacturer that already employed AM technology. Beyond bismuth-telluride, the machine-learning guided optimization protocol can be used in the future to reduce both the time and cost of process development for AM of other energy conversion and harvesting materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multiscale and Machine Learning Modeling for Additive Manufacturing

Additive manufacturing (AM) techniques provide the opportunity to simultaneously design new materials and components with complex structures in less time, enabling faster material developments. Even though compositionally similar, the texture of the materials produced by such techniques is significantly different from conventionally manufactured materials. Additively manufactured materials produces highly heterogeneous microstructure within a single build. Such variations in the microstructure make qualifying AM products challenging for extreme environment applications. Understanding the AM process and its influence on the materials’ microstructures/properties is paramount for evaluating the workability and performance of the manufactured materials. The performance of AM materials for advanced nuclear reactor applications is of interest to the Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy. Hence, considering the microstructural variabilities in the AM products and their impact on the performance of the material, it is important to correlate the process conditions to the final product and establish a process-structure-property- performance (PSPP) correlation for AM materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Strong yet ductile nanolamellar high-entropy alloys by additive manufacturing

Additive manufacturing produces net-shaped components layer by layer for engineering applications. The additive manufacture of metal alloys by laser powder bed fusion (L-PBF) involves large temperature gradients and rapid cooling, which enables microstructural refinement at the nanoscale to achieve high strength. However, high-strength nanostructured alloys produced by laser additive manufacturing often have limited ductility. In this work, we use L-PBF to print dual-phase nanolamellar high-entropy alloys (HEAs) of AlCoCrFeNi 2.1 that exhibit a combination of a high yield strength of about 1.3 gigapascals and a large uniform elongation of about 14 percent, which surpasses those of other state-of-the-art additively manufactured metal alloys. The high yield strength stems from the strong strengthening effects of the dual-phase structures that consist of alternating face-centred cubic and body-centred cubic nanolamellae; the body-centred cubic nanolamellae exhibit higher strengths and higher hardening rates than the face-centred cubic nanolamellae. The large tensile ductility arises owing to the high work-hardening capability of the as-printed hierarchical microstructures in the form of dual-phase nanolamellae embedded in microscale eutectic colonies, which have nearly random orientations to promote isotropic mechanical properties. The mechanistic insights into the deformation behaviour of additively manufactured HEAs have broad implications for the development of hierarchical, dual- and multi-phase, nanostructured alloys with exceptional mechanical properties.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Optimization of Rocket Engine Components using Multi-Metallic Additive Manufacturing

Additive manufacturing (AM) is advancing many applications of component design for liquid rocket engines. AM has been demonstrated in various rocket component applications using a variety of monolithic metal alloys, many of which are traditional alloys for extreme environments. NASA and industry partners have focused in recent years to advance processing to create bimetallic and multicomponent AM processes and materials. The role of multi-metallic AM offers advantages since it can further optimize weight, optimize reliability and performance by increasing the strength to weight ratio of a component, and can optimize materials for various engineering requirements. NASA’s Rapid Analysis and Manufacturing Propulsion Technology (RAMPT) project has designed and manufactured a series of additively manufactured (AM) coupled combustion chambers, nozzles, and other engine components to advance new AM processes and materials with the goal of reducing cost and schedule for engine manufacturing. These designs incorporated multimetallic AM, which further enabled carbon-fiber composite overwrap to reduce overall thrust chamber assembly (TCA) mass. Various AM processes were demonstrated on these components using a copper-based alloy/superalloy bimetallic solution. The AM processes being explored individually and in combination for bimetallic applications include Laser Powder Bed Fusion (L-PBF), Laser Powder Directed Energy Deposition (LP-DED), and cold spray. The combination of bimetallic material combinations explored in this research include GRCop-based alloys and superalloys, Inconel 625 or NASA HR-1. One unique development that will be presented is the combustion chamber and nozzle as a single component by using freeform integrated DED to build the nozzle directly onto the aft end of the chamber. The various aspects of the additive manufacturing processes and challenges, materials characterization and mechanical testing, and hot-fire testing of bimetallic components in a relevant rocket engine environment will be discussed.

Additive Manufacturing↗

DOE OSTI2026

Attention-based 3D – convolutional neural network model for mechanical property predictions using visible light images in metal additive manufacturing

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process parameters to predict the ultimate tensile strength (UTS), yield strength (YS), total elongation (TE), and uniform elongation (UE). The 3D-CNN layers of the model employed attention mechanisms to prioritize features in the data, thereby improving prediction accuracy. Furthermore, the effect of each process parameter and anomaly class is investigated using attention-based dynamic sigmoid weighted gates to interpret the influence each class has on the final prediction. Different combinations of the in situ data were fed into the 3D-CNN, with varying amounts of image layers, to determine the ideal combination for predicting mechanical properties in situ. Here, the 3D-CNN model achieved mean absolute percentage errors (MAPE) below 5% for both UTS and YS while using only a single camera input and under half of the available image layers.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On the existence of orthorhombic martensite in a near-α titanium base alloy used for additive manufacturing

Additive manufacturing is a state-of-the-art production technology to produce tailor-made and highly complex parts. Among various other alloys, Ti base alloys are frequently used in this manufacturing technique due to their well-balanced properties and their wide range of applications. Allotropic phases and the occurrence of athermal phase transformations are the main reasons why these alloys hold a great development potential and are the basis of extensive use. High cooling rates during manufacturing lead to martensitic phases and the formation of nanometer-sized microstructures resulting in extraordinary strength. Simultaneously, such high cooling rates cause a high amount of lattice defects and the occurrence of residual stresses, which finally may result in delamination effects and cracks. Usually, a common approach to reduce residual stresses during additive manufacturing is to decrease thermal gradients by increasing the heat input or preheating the building platform. Instead of applying the typical approaches to lower thermal gradients, this study deals with the origin of the ‘softer’ orthorhombic martensite by accelerating the solidification process. The implementation of the orthorhombic phase in bulk components was inspired by a new phase transformation herein reported for the first time in the powder material, which also validates the possible occurrence of two martensitic phases in the same alloy. Various sophisticated characterization techniques like high energy and high-temperature X-ray diffraction, high-resolution transmission electron microscopy as well as atom probe tomography were applied to characterize this softer orthorhombic martensitic phase in detail aiming to highlight the opportunities accompanied by this new approach for additive manufacturing of titanium alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dynamic properties of 316l stainless steel repaired using electron beam additive manufacturing

Additive manufacturing has the potential to be used for repair of high-value parts, but the strength and integrity of such repaired parts, especially under dynamic loading, in still unknown. This work presents the results of high strain-rate dynamic testing of additively repaired stainless steel samples. The 316L samples were intentionally damaged and subsequently repaired with 308L stainless steel wire using electron beam additive manufacturing. The repaired samples were subjected to gas-gun impact testing to induce incipient damage and determine the role played by the repaired region in dictating damage and failure. The results show the following: 1) the equation of state of the repaired region is comparable to the original material as shown by the unchanged spall plane location, 2) the deformation behavior is similar to the original material as shown by the similar Hugoniot Elastic limit, 3) the spall strength of the repaired region is slightly higher than the original material due to varying grain size, and 4) the damage morphology in the repaired region is different and involves a high rate of void coalescence. In conclusion, despite these minor differences in the spall strength and the damage morphology, the overall dynamic response of the original material and repaired samples is similar suggesting that AM is a promising approach for repairing high-value components.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Effects of digital fringe projection operational parameters on detecting powder bed defects in additive manufacturing

Additive manufacturing is a technology transforming traditional production timelines. Specifically, metal additive manufacturing (MAM) has been increasingly adopted by a variety of industries, not only to prototype, but also to fulfill full production scale applications with much lower lead times. Like any maturing manufacturing technology, developments in verifying and validating processes are necessary to support continuous growth. Due to the complex nature of MAM, part quality and repeatability remain integral challenges that inhibit further adoption of MAM for critical component production. In this study, we present data taken from a developing in-process monitoring system designed to measure and detect powder bed defects (PBDs) in powder bed fusion MAM systems using surface height maps created with structured light illumination. We showcase the feasibility of the monitoring technique for in-process implementation by detecting streak PBDs with varying severities (height, width) created in a lab environment. We present results of powder bed measurements for varying experimental parameters of the structured light system such as illumination angle, illumination pattern, and number of illuminations. We also present an expression used to determine experimental height noise based on input parameters for PBD detection based on the instrument transfer function of the structured light monitoring system for arbitrary pixel intensity noise contributions. In conclusion, with the results of PBD detection across multiple experimental measurement parameters, we provide a best practices approach to in-process implementation of the monitoring system in powder bed fusion manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Polymer Nanocomposite Sensors with Improved Piezoelectric Properties through Additive Manufacturing

Additive manufacturing (AM) technology has recently seen increased utilization due to its versatility in using functional materials, offering a new pathway for next-generation conformal electronics in the smart sensor field. However, the limited availability of polymer-based ultraviolet (UV)-curable materials with enhanced piezoelectric properties necessitates the development of a tailorable process suitable for 3D printing. This paper investigates the structural, thermal, rheological, mechanical, and piezoelectric properties of a newly developed sensor resin material. The polymer resin is based on polyvinylidene fluoride (PVDF) as a matrix, mixed with constituents enabling UV curability, and boron nitride nanotubes (BNNTs) are added to form a nanocomposite resin. The results demonstrate the successful micro-scale printability of the developed polymer and nanocomposite resins using a liquid crystal display (LCD)-based 3D printer. Additionally, incorporating BNNTs into the polymer matrix enhanced the piezoelectric properties, with an increase in the voltage response by up to 50.13%. This work provides new insights for the development of 3D printable flexible sensor devices and energy harvesting systems.

42 ENGINEERING↗

DOE OSTI2023

Sensitization of 316L Stainless Steel made by Laser Powder Bed Fusion Additive Manufacturing

Additively manufactured (AM) 316L stainless steel (SS) manufactured by laser powder bed fusion (L-PBF) and wrought 316L SS were subjected to sensitization heat treatments at 700°C up to 100 h. Using two evaluation methods, double-loop electrochemical potentiokinetic reactivation (DL-EPR) and ditching tests, degree of sensitization (DOS) and intergranular corrosion (IGC) susceptibility was evaluated. It was found that the wrought samples showed slightly lower IGC susceptibility compared to their AM counterpart. DOS and IGC attacks increased with sensitization time for all samples. Dislocation cellular structures were found to have little to no impact on DOS and IGC for the AM samples. Sensitized at 100 h, the AM sample showed significant Cr depletion along high-angle grain boundaries (12.35 wt% on average) and exhibited Cr carbide precipitation. Mo-rich particles along grain boundaries were also observed. The DL-EPR test attacks the surface oxide film and grain boundaries while the ditching test attacks the melt pool boundaries and grain boundaries (IGC and pitting). Changes to the DL-EPR and ditching standards for AM application have been proposed in this work.

316L stainless steel↗

DOE OSTI2025

The Cost of Scaling Up in Large-Format Additive Manufacturing

Additive manufacturing (AM) of large objects has, over the last decade, required the scaling of existing material extrusion processes. The current generation of large-scale printers are primarily gantry robots with high-throughput extrusion systems. With workspaces approaching 50 m 3 , these printers have pushed the boundaries of achievable print volume while allowing the utilization of low-cost feedstocks, such as cementitious materials and polymer pellets, like those used in injection molding. Continued workspace expansion requires an examination of the inherent trade-offs, which impact capital and operational costs. Here, in this work, the authors examine these trade-offs to determine fundamental scaling laws for existing system architectures, survey the state of the art for alternative system configurations, and pose recommendations for future system designers to continue the evolution of large-scale AM systems.

3D printing↗

DOE OSTI2023

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. Here, the presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

42 ENGINEERING↗

DOE OSTI2026

A meshing framework for digital twins for extrusion based additive manufacturing

Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.

Additive manufacturing↗

DOE OSTI2025

Beam Shaping and Oscillation for Metals Additive Manufacturing

Additive Manufacturing (AM) is pivotal for next-generation industrial manufacturing but is hampered by process control challenges leading to unwanted defects and sub-optimal material properties. Our research builds on prior work that utilized beam oscillation techniques to mitigate these issues (Wu et al., 2023). We focus on investigating the influence of beam oscillation frequency on melt pool dynamics, utilizing in-situ synchrotron material characterization from the ESRF (Chen et al., 2026). Preliminary findings indicated that beam oscillation results in unique fluid flow patterns and enhanced melting stability. These findings suggested the possibility of optimized AM process control, including minimized defects and tailored thermal histories. We proposed further research to explore the effects of beam oscillation frequencies comprehensively, using the in-situ characterization capabilities at the ESRF. The goal was to quantify this relationship, offering a pathway to more precise AM process control and optimization. Our research is expected to offer new scientific insights into the fluid mechanics and thermal behaviour in AM, contributing to its broader applicability and commercial viability.

36 MATERIALS SCIENCE↗

DOE OSTI2023

1. Introduction to Additive Manufacturing

Additive manufacturing (AM) is the process of constructing an object by adding layer upon layer of material in specific locations, to build the object into its final shape. There are many different AM processes that can be used to add layers of material, such as extrusion, material jetting, and sheet lamination. All of the AM processes are a form of digital fabrication because the object to be constructed must first be modeled with a computer. The modeled object must be converted into machine instructions that an AM system can use to construct the object. This chapter serves as an introduction to the many different AM processes. Subsequent chapters will explore the intricacies of creating machine instructions for the AM processes introduced here.

Macdonald, Eric↗

NASA NTRS2015

A Fully Nonmetallic Gas Turbine Engine Enabled by Additive Manufacturing, Part II: Additive Manufacturing and Characterization of Polymer Composites

This publication is the second part of the three part report of the project entitled "A Fully Nonmetallic Gas Turbine Engine Enabled by Additive Manufacturing" funded by NASA Aeronautics Research Institute (NARI). The objective of this project was to conduct additive manufacturing to produce aircraft engine components by Fused Deposition Modeling (FDM), using commercially available polyetherimides-Ultem 9085 and experimental Ultem 1000 mixed with 10% chopped carbon fiber. A property comparison between FDM-printed and injection molded coupons for Ultem 9085, Ultem 1000 resin and the fiber-filled composite Ultem 1000 was carried out. Furthermore, an acoustic liner was printed from Ultem 9085 simulating conventional honeycomb structured liners and tested in a wind tunnel. Composite compressor inlet guide vanes were also printed using fiber-filled Ultem 1000 filaments and tested in a cascade rig. The fiber-filled Ultem 1000 filaments and composite vanes were characterized by scanning electron microscope (SEM) and acid digestion to determine the porosity of FDM-printed articles which ranged from 25 to 31%. Coupons of Ultem 9085, experimental Ultem 1000 composites and XH6050 resin were tested at room temperature and 400F to evaluate their corresponding mechanical properties. A preliminary modeling was also initiated to predict the mechanical properties of FDM-printed Ultem 9085 coupons in relation to varied raster angles and void contents, using the GRC-developed MAC/GMC program.

Polymer↗

DOE OSTI2024

7 Innovations in high-rate composite manufacturing: integrating additive manufacturing with compression molding process

Advanced composites play a pivotal role in modern engineering, offering exceptional strength-to-weight ratios and tailored properties, essential for various industries. High-rate composite manufacturing techniques have rapid production capabilities, which are essential for meeting the demands of industries requiring cost-saving, efficiency, and quick turnaround times. This chapter explores the Additive Manufacturing- Compression Molding (AM-CM) system developed by Oak Ridge National Laboratory (ORNL) for advanced composites manufacturing. The AM-CM system integrates additive manufacturing with compression molding, facilitating the production of polymer composite parts with superior mechanical properties and meticulously controlled microstructures. This innovative system not only ensures precise material deposition but also operates as a fast composite manufacturing process, enhancing productivity and performance, which are needed attributes across industrial applications. Through comprehensive mechanical testing and microstructural analysis, AM-CM promotes remarkable fiber alignment and reduced porosity in composite parts compared to alternative thermoplastic high-rate composite manufacturing methods. Furthermore, AM-CM enables overmolding reinforcement using continuous carbon fiber and supports selective reinforcement through customizable toolpaths. It also facilitates the production of hybrid materials to achieve tailored mechanical properties. Future advancements in AM-CM technology aim to enhance process efficiency, broaden material versatility, and improve part performance. This involves exploring novel materials, advancing process monitoring, implementing automation technologies, and integrating artificial intelligence (AI) and machine learning (ML) for predictive modeling and real-time optimization in composite manufacturing. These developments will establish the AM-CM system as a transformative technology in composite manufacturing, driving innovation across industries.

Hassen, Ahmed [ORNL] (ORCID:0000000328521222)↗

DOE OSTI2022

Towards underwater additive manufacturing via additive friction stir deposition

Given the challenges in feed material supply and quality control, metal additive manufacturing has rarely been implemented in austere environments, especially underwater. This paper explores the underwater operation potential of an emerging solid-state additive technology: additive friction stir deposition, wherein material feeding and bonding are enabled by mechanical forces with minimal influences from water. It is demonstrated that additive friction stir deposition of 304 stainless steel can be successfully performed with the print head and substrate immersed in water. High temperature is reached in the deposition zone (>60% melting temperature); the material deposition behavior is similar to that in typical open-air operation. The as-deposited material is fully-dense, having fewer annealing twins and a substantially smaller grain size than the feed material (4.98 μm vs. 31.44 μm ). Such microstructural changes stem from dynamic recrystallization caused by the large strain and high temperature introduced during deposition. In addition to grain refinement, small equiaxed dispersoids (~2–3 μm or less) are formed and evenly distributed in the austenite steel matrix. Rich in Cr, Mn, and O, these particles likely result from the reaction between the elements in stainless steel and water at elevated temperatures.

16 TIDAL AND WAVE POWER↗