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At least 19 records

DETERMINATION OF THE SOLID-STATE RESISTANCE-WELDABILITY OF ADDITIVELY MANUFACTURED 304L STAINLESS STEEL

Additive manufacturing of pressure-containing boundaries is of interest to numerous industries, so a project was initiated to develop several different methods to join and weld additively manufactured materials. The approach investigated in this study was a low energy, solid state spot weld. This general approach has been routinely used for conventionally prepared tubing and is now being expanded to additively manufactured components. Additively manufactured 304L stainless steel sheets, semi-tubes, and tubes were resistance spot welded and compared to comparable welds in conventionally manufactured tubing. Radiographic computed tomography and metallography were used to evaluate the suitability of the welds using three criteria: weld closure length, thickness, and width. All additively manufactured test welds closely matched the conventional tubing for these criteria

additive manufacturing

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing

Adaptive control for hybrid manufacturing

The primary objective of the technical collaboration is to validate and implement adaptive feed rate control, tool wear monitoring, and anomaly detection for the finish machining of additive components. Most additively manufactured components need finish machining to achieve surface finish and tolerance requirements. Distortions and surface roughness of additively manufactured components can result in high forces and tool breakage or excessive tool wear during post-additive finish machining, which can result in rework or part scrap. In this collaboration, methods, and models for feed rate control, tool wear monitoring, and anomaly detection via a digital twin will be evaluated and validated.

99 GENERAL AND MISCELLANEOUS

Machining of Thin-Walled Structures From Stiffness-Driven Additively Manufactured Preform Geometry

Additive manufacturing provides the means to build component preforms with reduced excess material to create functional parts. In the case of aero-structural and aero-engine components, additive manufacturing technologies offer the possibility to substantially reduce the volume of material to be removed by machining operations. To achieve this objective, the preform must be built with the minimum material necessary to contain the final geometry and simultaneously provide enough stiffness to withstand the magnitude of the machining forces. This work describes a computationally efficient method to calculate the geometry required from the preform to reliably manufacture typical thin-walled structures via finish machining processes. This is achieved by defining the preform with near constant static stiffness across the width of the preform, in combination with a prescribed magnitude of stiffness at the top edge of the preform. The prescribed static stiffness is the function of the machining force magnitude, a direct consequence of the preselected cutting conditions. In conclusion, this article illustrates the application of the method to a straight single boundary thin-walled structure as an introduction case and for ease of description.

Additive manufacturing

Computational Design to Advance AM Fabrication of High Gamma Prime Alloys for Hot Gas Path Components in Gas Turbine Engines: A Pathway to Enhanced Gas Turbine Efficiency and Energy Saving (Final CRADA Report)

Raising turbine inlet temperature is a key lever for improving industrial gas-turbine efficiency and power output, but it increases thermo-mechanical demands on hot-gas-path components. Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), enables complex internal cooling features in critical components such as turbine tip shoes that are difficult to produce by conventional casting. However, qualification of new high-temperature AM alloys and aggressive geometries is often limited by trial-and-error iteration of build parameters and post-build heat treatments, with cracking during stress relieving or hot isostatic pressing (HIP) being a recurring failure mode.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE

Geometrical effects on the measured neutron residual stress in additively manufactured cold spray AA6061

Aluminum cold spray additive builds were made with varying wall widths and geometries to explore the effects of build geometry on residual stress development in the additively sprayed material. Deposition rates of over 750 g/hr were achieved without any cracking or delamination of the additively manufactured components. Neutron residual stress measurements revealed a maximum tensile residual stress of 41 MPa at the interface of the substrate and a maximum compressive residual stress of −35 MPa in the cold sprayed material. While there was not found to be significant residual stress changes between the cold sprayed walls, the cold sprayed cylinder had varying residual stress throughout the diameter with the outer diameter having a higher tensile residual stress than the interior. The low residual stress and minimal geometrical dependency demonstrate further part size is possible without concerns for component failure in AA6061. The application of common coating residual stress models to full scale additive builds is evaluated and discussed. The lack of residual stress evolution was studied by in-situ coating property measurement that revealed that the deposition stress of the AA6061 sprays reduced with increased layer thickness. In conclusion, this study provides insight into the evolution of residual stress in cold spray additive components and how both the mechanics and material responses differ from coatings.

Additive manufacturing

Real-time tracking and analysis of gas bubble dynamics in laser powder bed fusion using in-situ X-ray characterization and machine learning

Porosity defects remain a significant challenge in the laser powder bed fusion (LPBF) process, adversely affecting the mechanical properties and reliability of additively manufactured components. Here, this study investigates the real-time formation and trajectory of gas bubbles during LPBF of Al6061 alloy using advanced in-situ X-ray characterization and machine learning. The unsupervised Gaussian mixture model and particle tracking algorithm developed are able to precisely track and quantify the properties of gas bubbles and keyhole pores. Our analysis identified five distinct types of gas bubble formation and movement patterns, emphasizing the diverse origins and behaviors of these defects. It enables precise quantification of trajectories, velocities, and morphological changes of gas bubbles, offering a granular view of the subsurface dynamics within the melt pool. Additionally, we explored keyhole-induced pore dynamics, revealing the critical role of keyhole oscillation and collapse for the formation of both large and small gas pores. It defines four different regions of gas bubble movement within the melt pool, providing a clearer understanding of how local fluid dynamics affect pore behavior. The results underscore the importance of integrating in-situ experimental observation and automated machine learning to develop a more robust predictive model for defect formation in LPBF.

In-situ X-ray imaging

Fabrication and Evaluation of Large Alumina Crucibles by Vat Photopolymerization Additive Manufacturing for High-Temperature Actinide Chemistry

Additive manufacturing (AM) offers opportunities to advance the design and function of ceramic tooling in high temperature actinide pyrochemistry. In technical ceramics such as alumina, conventional forming techniques often restrict design flexibility and can limit experimental progress. In this study, we investigate the use of vat photopolymerization (VP) with commercial resins to fabricate large-scale alumina crucibles, reaching dimensions up to 125 mm, which is significantly larger than typically reported for dense VP ceramics. Notably, these additively manufactured components are produced using consumer-grade hardware, which limits process control, but offers significant upside in scalability and accessibility. Using microscopy and X-ray computed tomography, the VP alumina parts have high bulk densities above 95%, but also the prevalence of AM-induced artifacts and surface defects. Mechanical testing showed these defects to significantly reduce flexural strength and compromise part reliability. Electrorefining trials under sustained exposure to molten salts and metals reveal mixed results, with the AM material exhibiting high chemical compatibility, but mechanical failures due to the reduced strength were prevalent. Our findings illustrate both the promise and current limitations of AM ceramics for actinide chemistry, and point toward future improvements in process optimization, design strategies, and part screening to enhance performance and reliability.

Materials science

Pitfalls in parameters: practical process development in chemical vapor processing of SiC

Vapor processing of high-value materials, such as silicon carbide (SiC), is of interest for many industries, including aerospace and energy production. Chemical vapor infiltration (CVI) of additively manufactured components is an especially attractive manufacturing process currently in development. Here, a novel development workflow is demonstrated with the H 2 –CH 3 SiCl 3 gas system for SiC to accelerate the process optimization of CVI SiC. A combination of calculated thermodynamics and high-throughput experimental chemical vapor deposition (CVD) coatings substantially reduced the experiments required with slow CVI processes. The computational results accurately predicted changes in the thermodynamic conditions tested, while CVD coatings – characterized by Raman spectroscopy – addressed changes in kinetic parameters. This workflow is also applicable to other vapor-processing systems, such as pyrolytic carbon, ZrC, or Si 3 N 4 .

Lamm, Benjamin W. [Oak Ridge National Laboratory (

Determination of the Solid-State Resistance-Weldability of Additively Manufactured 304L Stainless Steel

• A scoping study was undertaken at Savannah River National Laboratory (SRNL) to determine the solidstate resistance-weldability of additively manufactured 304L stainless steel. • Additive manufacturing of pressure-containing boundaries is of interest to numerous industries. • The approach investigated in this study was a low energy, solid state spot weld (pinch weld). • This general approach has been routinely used for conventionally prepared tubing and is now being expanded to additively manufactured components. • This work aimed to understand the impact of AM on the bond quality and weld geometry of pinch welds.

Rogers, Jeremy K. [Savannah River National Laborat

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

FY 2025 Multidimensional Data Correlation Platform: Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing

The Advanced Materials and Manufacturing Technologies (AMMT) program continues to advance a data-driven approach to demonstrate the utility of additive manufacturing for fabricating components for nuclear applications. A key scientific goal is to leverage data to better understand manufacturing outcomes and thereby improve the performance, reliability, and lifespan of nuclear components. Ultimately, this effort supports the development of standards for certification and qualification of additively manufactured components, enabling broader industry adoption. In support of this objective, the AMMT program is building and deploying a data management platform to record, index, analyze, and make available the manufacturing data generated across the AMMT program. In FY 2023, the team conceptualized the architecture of the platform and, in FY 2024, deployed the first functional version at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF). In FY 2025, the platform was officially opened to all AMMT members. To enable this expansion, core modifications and enhancements were developed, including improvements to the user interface and workflows for data entry and retrieval. Most notably, robust security and access control mechanisms were implemented to protect data and manage information sharing. This effort featured a logging system, protected views, and controlled access mechanisms. This report documents these enhancements and the transition of the platform into program-wide use.

36 MATERIALS SCIENCE

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