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DOE OSTI2025

Defect And Damage Characterization Of Additively Manufactured Titanium Alloy Ti-5553 Using Traditional Computed Tomography Volume Segmentation And Machine Learning Algorithms

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. Further, the results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

36 MATERIALS SCIENCE↗

DOE OSTI2023

On the strength and fracture toughness of an additive manufactured CrCoNi medium-entropy alloy

An additively manufactured (nominally equiatomic) CrCoNi alloy was processed by laser powder bed fusion (LPBF). At ambient temperatures (298 K), this medium-entropy alloy displayed a yield strength, σ y of ~691 ± 9 MPa, and an ultimate tensile strength, σ u of ~926 ± 15.2 MPa; at cryogenic temperatures (77 K), yield and tensile strengths increased respectively to σ y ~ 944 ± 6 MPa and σ u ~ 1382 ± 11 MPa. These strength levels are 57 and 44% higher than that of the wrought alloy, due to strengthening from the solidification cellular structures intertwined with dislocations in the LPBF CrCoNi. The crack-initiation fracture toughness, K JIc was measured to be ~183.7 ± 28 MPa√m at 298 K; this value marginally decreased by ~4% to ~176 ± 11 MPa√m at 77 K. These K JIc values of the LPBF CrCoNi were 11% and 35% lower than the wrought CrCoNi alloy at 298 K and 77 K, respectively. Here, the resistance to crack growth of the LPBF CrCoNi from its hierarchical micro- and meso-structures was evaluated using nonlinear-elastic fracture mechanics by measuring R-curve behavior in the form of the J-integral as a function of crack extension. The specific features of the hierarchical microstructures at different length-scales provide a basis for the strengthening and toughening properties of this additively manufactured medium-entropy alloy. This correlation between the deformation and the hierarchical microstructures at different length-scales may provide future guidance for improving the fracture toughness properties of medium-entropy alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Al-Cu-Ce(-Zr) alloys with an exceptional combination of additive processability and mechanical prope

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Bahl, Sumit↗

DOE OSTI2025

Vector-level feedforward control of LPBF melt pool area using a physics-based thermal model

Laser powder bed fusion (LPBF) is an additive manufacturing technique that has gained popularity thanks to its ability to produce geometrically complex, fully dense metal parts. However, these parts are prone to internal defects and geometric inaccuracies, stemming in part from variations in the melt pool. Here, this paper proposes a novel vector-level feedforward control framework for regulating melt pool area in LPBF. By decoupling part-scale thermal behavior from small-scale melt pool physics, the controller provides a scale-agnostic prediction of melt pool area and efficient optimization over it. This is done by operating on two coupled lightweight models: a finite-difference thermal model that efficiently captures vector-level temperature fields and a reduced-order, analytical melt pool model. Each model is calibrated separately with minimal single-track and 2D experiments, and the framework is validated on a complex 3D geometry in both Inconel 718 and 316L stainless steel. Results showed that feedforward vector-level laser power scheduling reduced geometric inaccuracy in key dimensions by 62%, overall porosity by 16.5%, and photodiode root-mean-squared deviation by 38.5% on average. Overall, this modular, data-efficient approach demonstrates that proactively compensating for known thermal effects can significantly improve part quality while remaining computationally efficient and readily extensible to other materials and machines.

Additive manufacturing↗

DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

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).

36 MATERIALS SCIENCE↗

DOE OSTI2021

Effect of the scanning strategy on the formation of residual stresses in additively manufactured Ti-6Al-4V

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. Here in this work, the higher stresses were observed in the bridges built via the island scan strategies, chiefly near the edges of the parts.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Optimization of Processing, Microstructure, and Hardness of an Al–Ce–Ni–Mn–Zr Alloy With Laser Additive Manufacturing

Here, this study examines the processing behavior, microstructure, surface roughness, and hardness properties of an aluminum alloy containing 8.2 Ce, 4.5 Ni, 0.5 Mn, and 0.7 Zr (wt%) fabricated using laser powder bed fusion. Sixty samples were produced across a range of laser powers, scan speeds, and hatch spacings to evaluate their effect on porosity, hardness, and microstructural features. Porosity was measured using X-ray computed tomography, while microstructure and surface roughness were characterized by scanning electron (SEM) and laser confocal microscopy. High dense and cracking-free Al–Ni–Ce alloy was successfully manufactured. Porosity showed a U-shaped dependence on energy input, increasing under both insufficient and excessive melting conditions. Hardness increased with cooling rate due to finer cellular structures and solute redistribution. A general statistical model was developed to capture the relationships between processing parameters and material response. Results identify a narrow processing window defined by laser powers between 350 and 370 W, scan speeds from 1400 to 1800 mm/s, and hatch distances between 0.14 and 0.18 mm. Within this window, porosity is minimized (below 0.01%) and hardness is maximized (up to 160 HV), demonstrating that careful control of these parameters enables dense, high strength aluminum components suitable for demanding structural applications.

Aluminum alloys↗

DOE OSTI2025

High-pressure high-temperature melting and recrystallization of nanolamellar high-entropy alloys

Additively manufactured (AM) High Entropy Alloys (HEAs) are notable for their exceptional high-yield strength and large tensile ductility. The nanolamellar Eutectic HEA (EHEA) AlCoCrFeNi 2.1 was fabricated by laser powder bed fusion (L-PBF) in the as-printed form (EHEA1) and subsequently annealed at 1000oC (EHEA2) and 600oC (EHEA3) to achieve a broad range of mechanical properties. EHEA2 and EHEA3 samples were studied using Scanning transmission electron microscopy (STEM), energy dispersive X-ray diffraction (EDXRD) at high-pressures and high temperatures, nanoindentation hardness and modulus measurements. According to EDXRD EHEA2 and EHEA3 are composed of B2 and L1 2 phases. High-pressure high-temperature EDXRD studies show melting for EHEA2 at 1698 ± 25 K at a pressure of 6.5 GPa and melting for EHEA3 at 1598 ± 25 K at a pressure of 5.8 GPa. Post-melt and recrystallized samples were recovered at ambient conditions, and XRD analysis showed retention of B2 and L1 2 phases, although a new σ phase appeared for both EHEA2 and EHEA3 samples due to high-pressure and high-temperature melting and recrystallization experiment. SEM analysis also demonstrated the preservation of the nanolamellar morphology. Nanoindentation studies revealed that recrystallized EHEAs retain their original mechanical property hierarchy, with EHEA3 being 47% harder than EHEA2, largely related to higher content of B2-phase and retention of nanolamellar morphology. We also present Pressure-Volume-Temperature (P-V-T) data for 3-D printed and annealed eutectic high entropy alloys and extract mechanical and thermal properties data.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Influence of the as-built microstructure on the recrystallization of an additively manufactured Inconel939 Ni-based superalloy

This study investigates the influence of the as-built microstructure on the recrystallization (RX) behavior and mechanical properties of the Ni-based superalloy Inconel 939 produced by laser powder bed fusion (PBF-LB/M). Two distinct as-built microstructures were obtained by varying the hatch distance (h d ): a columnar, strongly textured condition (h d =50, termed h d 50) and an equiaxed, weakly textured condition (h d =70, termed h d 70)). Both were subjected to nine solution treatments combining three temperatures (1100, 1150, and 1200 °C) and three holding times (1, 4, and 8 h). Comprehensive microstructural characterization was conducted to assess grain morphology, texture, grain boundary character, dislocation density, and precipitate distribution. Recrystallization was found to be significantly slower than in cast counterparts, requiring higher temperatures and longer times for completion. The initial microstructure plays a decisive role: full RX was achieved only in hd70 specimens after treatment at 1200 °C for 8 h, whereas hd50 samples exhibited delayed and incomplete RX under identical conditions. This behavior is attributed to the finer grain size and higher fraction of high-angle grain boundaries in hd70, which promote recrystallization. Mechanical testing revealed that hd70 samples subjected to a 1200 °C/8 h treatment followed by standard double ageing show higher yield and tensile strengths across the investigated temperature range than both printed and cast Inconel939 processed under conventional conditions, albeit with slightly reduced ductility. The enhanced mechanical performance is attributed to the larger grain size, which limits grain boundary sliding. These results demonstrate the critical importance of controlling the as-built microstructure and tailoring post-processing strategies to optimize high-temperature performance of PBF-LB/M Inconel939.

Inconel939↗

DOE OSTI2025

Effect of LPBF Processing Parameters on Inconel 718 Lattice Structures: Geometrical Characteristics, Surface Morphology, and Mechanical Properties

Laser Powder Bed Fusion (LPBF) enables the additive manufacturing of complex lattice structures. However, the fabrication of lattice structures via LPBF poses challenges in achieving the intended geometrical accuracy due to their inherent complexity. This study investigates the effects of LPBF processing parameters, specifically laser power and scanning speed, on the geometrical characteristics, surface quality, and mechanical behavior of Inconel 718 lattices structures. The results reveal that processing parameters required for the fabrication of near-full dense structures do not translate effectively to lattice configurations, as variations in energy input influence lattice geometry and surface quality. In this work, strut thickness, open-pore size, open-cell porosity, and surface roughness were measured, and the mechanical properties of the lattices were evaluated under shear loading. The findings indicate that lower energy inputs, achieved by reducing laser power and increasing scanning speed, yield porous structures but lead to mechanical degradation. In contrast, high energy inputs lead to lattices with enhanced strength but result in undesirable open-pore blockage and dimensional inaccuracies. These findings provide insights into tailoring LPBF parameters for dimensional accuracy in lattices and correlating the processing parameters to mechanical performance and surface roughness.

36 MATERIALS SCIENCE↗

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 OSTI2025

Microstructure characteristics of LPBF&HIP fabricated graded composite transition joint between ferritic steel and austenitic stainless steel

Graded composite transition joints (GCTJs) offer a promising alternative to conventional dissimilar metal welds (DMWs) by enabling smooth compositional and microstructural transitions. However, GCTJs fabricated solely through additive manufacturing (AM) face challenges such as heat accumulation, complex parameter control, and elemental segregation. In this study, we propose a novel approach that relies on AM to design a spatially graded structure in one alloy and then employs hot isostatic pressing (HIP) as a diffusion bonding method to join it with a second alloy. Here, this method combines the flexibility of AM with the powder net-shaping advantage of HIP. Specifically, a series of closely packed austenitic stainless steel 304 conical structures were printed using laser powder bed fusion (LPBF) and then combined with ferritic steel P91 powder via HIP. By using electron backscatter diffraction (EBSD), electron probe microanalysis (EPMA), and transmission electron microscopy (TEM) techniques, the microstructure characteristics of the GCTJ of 304&P91, especially the interdiffusion zone (IDZ), have been systematically investigated. The microstructure at the interface transitions from austenite-ferrite (A+F) to austenite-martensite-ferrite (A+M+F), and finally to martensite-ferrite (M+F) due to diffusion. Additionally, the diffusion width between 304 and P91 increases with the volume fraction of P91. This unique design also ensures a gradual transition in both hardness and thermal expansion coefficient from 304 to P91, thereby enabling a smooth gradient in functional properties. Overall, this study proposes a novel approach for fabricating GCTJs and contributes to advancing design concepts in the field of dissimilar metal joining.

Additively manufacturing (AM)↗

DOE OSTI2026

Enabling On-Demand Aerospace Component Manufacturing: Topology Optimization of GE Engine Bracket and Fabrication Using Metal FFF

Additive Manufacturing (AM) offers advantages over conventional manufacturing processes, particularly by reducing the number of parts produced through multistage combined technologies, but these often result in low manufacturing yields or require post-processing. AM facilitates the production of complex geometries with fine features, overhangs, and lattice structures. For instance, Laser Powder Bed Fusion (LPBF) technology enables the fabrication of intricate parts that can be easily post-processed by removing residual powder. Laser powder bed AM technologies are widely discussed in the literature due to their design freedom in creating complex geometries, with and without the need for support generation. However, rapid solidification due to a thermal gradient in the build direction, which leads to the formation of columnar grains and warpage, is one of the challenges. To address this challenge, we propose layer-by-layer metal FFF technology, followed by the debinding and sintering process, as an alternative to powder- and laser-based approaches. Furthermore, design for additive manufacturing (DfAM) principles are discussed to minimize the need for support generation and enable easy post-processing, thereby improving surface finish to meet high tolerances in fabricating components for aerospace and healthcare applications.

Singh, Abhishek [University of Michigan, Ann Arbor↗

DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Performance Validation of Pulsed Thermal Imaging System for In-Service Applications

42 ENGINEERING↗

DOE OSTI2025

Additive Manufacturing of Fe-based Alloys for Nuclear Reactor Environments

This report is a Milestone 2 deliverable in FY2025, under work package MT-25AN090211 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program. It provides an update on the work done on the laser based additive manufacturing of steels for nuclear applications, conducted collaboratively by Argonne National Laboratory and Pacific Northwestern National Laboratory. Building upon FY23 and FY24 efforts, the focus of FY25 includes continued optimization of process parameters for the alloys: A709 & G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. The major accomplishments of this work package are summarized below, applicable to both Laser Powder Bed Fusion (LPBF) processes conducted at Argonne National Laboratory and Laser Powder Directed Energy Deposition (LP-DED) processes conducted at Pacific Northwest National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Convergent Manufacturing of Large-Scale Components for Nuclear Applications, via Additive Manufacturing and Powder Metallurgy Hot Isostatic Pressing

Powder metallurgy (PM)–hot isostatic pressing (PM-HIP) has long been recognized as a powerful route for producing fully dense, near net shape metallic components. By consolidating powders under high temperature and pressure, HIP provides isotropic properties, uniform microstructures, and scalability to complex geometries that are vital for sectors such as aerospace, energy, and nuclear power. Yet despite these advantages, the technology has remained constrained by costly trial and error canister fabrication, limitations of conventional forging, and incomplete knowledge about how the canister design influences final part properties. Additive manufacturing (AM), by contrast, thrives on design freedom and geometric flexibility but struggles with speed, scalability, and cost when applied to very large structures. The research presented in this report investigated how a convergent manufacturing approach, combining AM with PM-HIP, can merge the strengths of both technologies, leveraging AM’s flexibility for canister design and HIP’s consolidation capability to deliver reliable, large, and complex parts. The work progressed through three case studies that built on one another in scale and complexity. Small cylindrical canisters fabricated by conventional methods, laser powder bed fusion, and directed energy deposition were filled with stainless steel powders and subjected to HIP. The resulting parts demonstrated near-full density and mechanical properties on par with wrought stainless steel, showing for the first time that AM canisters can be a direct substitute for conventional ones without sacrificing quality. The next step involved a medium-scale, noncentrosymmetric T-valve, which is an enclosed, multibranch geometry that tested the limits of AM + PM-HIP integration. The T-valve achieved predictable shrinkage and uniform densification, confirming feasibility for enclosed designs. However, this study also revealed oxide inclusions and interfacial challenges at the AM + HIP boundary, underscoring the critical importance of controlling interface chemistry and employing robust, in situ strategies, such as melt pool monitoring and thermal monitoring, coupled with nondestructive evaluation techniques such as x-ray computed tomography. Finally, the effort culminated in fabricating a large-scale impeller weighing nearly 2000 lb and spanning 5 ft in diameter. Produced via multirobot wire arc AM and hot isostatic pressed to near-full density, the impeller validated industrial-scale feasibility. Predictive models closely matched experimental shrinkage, tensile properties were spatially uniform across the component, and the AM + PM-HIP interface proved mechanically sound despite the presence of oxide-decorated prior particle boundaries. This large-scale demonstration is a major milestone, showing that hybrid AM + PM‑HIP can reliably deliver components at reactor-relevant scales. Collectively, these studies charted a logical pathway: small-scale work built scientific confidence, medium-scale work highlighted opportunities and challenges, and large-scale work proved industrial impact. The overarching conclusion of this report is that AM + PM-HIP should not be seen as a replacement for forging but as a complementary pathway that provides the US with flexibility, resilience, and new options for manufacturing nuclear-grade components. Looking ahead, several directions emerge as critical to sustaining progress. Predictive modeling must become faster, more accessible, and more accurate, with digital twins and machine learning reducing reliance on trial and error. Powders and alloys must be optimized for HIP, with improved cleanliness, reduced oxides, and tailored chemistries that enhance creep, fatigue, and irradiation resistance. Interfaces between AM and HIP regions must be better engineered through coatings, machining strategies, and surface treatments to mitigate oxide formation and ensure reliable bonding to explore opportunities for HIP of targeted compositional parts, as well as multimaterial HIP cladding applications. Monitoring and nondestructive evaluation need to expand, incorporating multimodal sensors, x-ray computed tomography, and real-time data integration through platforms such as Pelican. At the same time, the pathway to industrial adoption requires techno-economic analysis, machinability studies, and qualification frameworks aligned with industry and regulatory standards. Finally, workforce and academic engagement must be strengthened. Programs that train technicians and engineers for US Navy and US Department of Energy manufacturing challenges should be paired with academic partnerships to support fundamental research, with open sharing of non-export-controlled data to accelerate innovation and build the next generation of experts. In conclusion, this report demonstrates that hybrid AM + PM-HIP is scientifically viable and strategically important. By combining the design agility of AM with the consolidation strength of HIP and embedding modeling, monitoring, and workforce development, this approach provided a transformative new capability for US manufacturing. The path forward is clear: hybrid AM + PM-HIP is not just a promising research direction but is also potentially an industrially relevant pathway that can reshape how nuclear-grade components are designed, qualified, and deployed.

36 MATERIALS SCIENCE↗