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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Solidification dynamics in metal additive manufacturing: analysis of model assumptions

Solidification characteristics in the meltpool drive the process-microstructure relationship which helps dictate the material properties of as-built parts in additive manufacturing; therefore, being able to accurately and quickly simulate the size, shape, and solidification characteristics in the melt pool is of great interest to the field. In this study, we investigate various important physical phenomena (dynamic material properties, fluid-flow, radiation and vaporization) which can either be included or neglected in a continuum finite volume model (FVM) and their effect on the solidification conditions. Additionally, since the simplest form of such a model (conduction only) has an analytic solution which is much faster, its viability is also considered. Since the inclusion of some of these physical phenomena will inherently change the net energy input as well as the amount of energy needed to achieve melting of a control volume, each set of included phenomena had an effective absorption efficiency which was calibrated to closely match the dimensions of the melt pool to that of the ground truth data. The ground truth data for this study was defined to be the output of the FVM which included all the physical phenomena (OF). This study then goes on to compare the effects on solidification conditions each of these calibrated models has. It was found that most of the change in solidification conditions comes from the inclusion of latent heat. A posterior correlation factor (PCF) is then introduced to enable an analytic model to predict similar solidification conditions to OF model.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A functional modeling approach for quality assurance in metal additive manufacturing

Purpose: Due to the complexity of and variations in additive manufacturing (AM) processes, there is a level of uncertainty that creates critical issues in quality assurance (QA), which must be addressed by time-consuming and cost-intensive tasks. This deteriorates the process repeatability, reliability and part reproducibility. So far, many AM efforts have been performed in an isolated and scattered way over several decades. Here, a systematically integrated holistic view is proposed to achieve QA for AM. Design/methodology/approach: A systematically integrated view is presented to ensure the predefined part properties before/during/after the AM process. It consists of four stages, namely, QA plan, prospective validation, concurrent validation and retrospective validation. As a foundation for QA planning, a functional workflow and the required information flows are proposed by using functional design models: Icam DEFinition for Function Modeling. Findings: The functional design model of the QA plan provides the systematically integrated view that can be the basis for inspection of AM processes for the repeatability and qualification of AM parts for reproducibility. Research limitations/implications: A powder bed fusion process was used to validate the feasibility of this QA plan. Feasibility was demonstrated under many assumptions; real validation is not included in this study. Social implications: This study provides an innovative and transformative methodology that can lead to greater productivity and improved quality of AM parts across industries. Furthermore, the QA guidelines and functional design models provide the foundation for the development of a QA architecture and management system. Originality/value: This systematically integrated view and the corresponding QA plan can pose fundamental questions to the AM community and initiate new research efforts in the in-situ digital inspection of AM processes and parts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of a Method for Shape Optimization for a Gas Turbine Fuel Injector Design Using Metal-Additive Manufacturing

Adjoint shape optimization has enabled physics-based optimal designs for aerodynamic surfaces. Additive manufacturing (AM) makes it possible to manufacture complex shapes. However, there has been a gap between optimal and manufacturable surfaces due to the inherent limitations of commercial computational fluid dynamics (CFD) codes to implement geometric constraints during adjoint computation. In such cases, the design sensitivities are exported and used to perform constrained shape modifications using parametric information stored in computer aided design (CAD) files to satisfy manufacturability constraints. However, modifying the design using adjoint methods in CFD solvers and performing constrained shape modification in CAD can lead to inconsistencies due to different shape parameterization schemes. This paper describes a method to enable the simultaneous optimization of the fluid domain and impose AM manufacturability constraints, resolving one of the key issues of geometry definition for isogeometric analysis. Similar to a grid convergence study, the proposed method verifies the consistencies between shape parameterization techniques present within commercial CAD and CFD software during mesh movement as a part of the adjoint shape optimization routine. By identifying the appropriate parameters essential to a shape optimization study, the error metric between the different parameterization techniques converges to demonstrate sufficient consistencies for justifiable exchange of data between CAD and CFD. For the identified shape optimization parameters, the error metric to measure the deviation between the two parameterization schemes lies within the AM laser-powder bed fusion (L-PBF) process tolerance. Additionally, comparison for subsequent objective function calculations between iterations of the optimization loop showed acceptable differences within 1% variation between the modified geometries obtained using the two parameterization schemes. This method provides justification for the use of multiphysics guided adjoint design sensitivities computed in CFD software to perform shape modifications in CAD to incorporate AM manufacturability constraints during the shape optimization loop such that optimal designs are also additively manufacturable.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

SRF E - Beam Accelerator for Metal Additive Manufacturing [Patent]

A system and apparatus for electron beam melting comprises a superconducting radio frequency accelerator configured to produce an electron beam, a conduction cooling system configured to cool the superconducting radio frequency accelerator, and an electron beam melting system wherein the electron beam melts power in a build chamber of the electron beam melting apparatus.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Electrical-Discharge-Machining Contamination Removal from Metal Additively Manufactured Components

The use of an electrochemical dissolution process is shown to remove the recast layer contamination from the surfaces of electrical-discharge-machining cut components, as well as the interior exposed surfaces of the structure. The solution chemistry, cell potential, and exposure time are all relevant interdependent variables. Optimization of the electrode geometry should be made for each type of component. For the case of Cu-Zn recast contamination of 300-series alloy components, surface composition analysis indicates that complete electrochemical dissolution is achieved using a dilute solution of nitric acid (HNO 3 ). For example, electrochemical dissolution of the Cu-Zn recast is accomplished at 1.2 V cell potential using a 20% nitric solution and an exposure time of 4 h. The use of a nitric acid bath was specifically chosen since it’s chemically compatible and will not degrade the host alloy or the component. In sum, an electrochemically driven dissolution process can be tailored to remove of the recast contamination without affecting the integrity of the host component structure and its dimensional tolerances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Large Scale Metal Additive Manufacturing for Stamping Dies

Oak Ridge National Laboratory (ORNL) worked with Dienamic Tooling Systems (DTS) in a follow-on Phase 2 effort to develop and demonstrate additively manufactured conformal cooling for hot stamping dies, providing a new industry capability that did not previously exist. Current approaches to die cooling are nonoptimal and based on what can be machined with traditional technologies. The printed approach to hot stamping cooling channels allows an analytical needs-based size and placement approach to cooling. Cooling is optimized and parts count is reduced producing a simpler more reliable faster to manufacture stamping tool. An additively manufactured hot stamping die with conformal cooling channels was manufactured and successfully tested in a real-world environment.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Project Fringe, Testing and Validation on Metal Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Additive Monitoring Systems (AMS) to demonstrate the real-time inspection of additive manufacturing (AM) binder jet fusion 3D printing (BJF). Test coupons on the ExOne Innovent were printed and monitored using AMS’s Project Fringe structured light system. Anomalies were detected automatically and classified post build as recoater powder layer non-uniformities, binder powder interaction, depressions, and sub optimal print area. These anomalies were not apparent in the visual spectrum, which suggests that the structured light system has advanced the monitoring techniques state-of-the-art for BJF.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Model-based quantification of margins and uncertainties in metal additive manufacturing for process design and qualification

Laser powder bed fusion (LPBF) Additive Manufacturing (AM) has the potential to enable the production of components with novel designs and material properties unachievable otherwise. However, process repeatability is a challenge, making qualification ill-defined and greatly reducing the utility of what could be an important manufacturing technology. In this work, a combination of modeling, uncertainty quantification (UQ), and experimentation are used in an effort to predict and bound the range of possible outcomes of the LPBF process. Quantities of interest predicted are melt pool dimensions, microstructure features, and mechanical distortions. A combination of high fidelity thermal-fluid models, microstructure growth models, and reduced fidelity, rapid thermal and mechanical models are used. Uncertainty propagation techniques are used to predict probability distributions of quantities of interest from estimates of process uncertainties. Repeated experiments are done to quantify observed probability distributions and compared to predicted distributions to determine if predictions are precise and accurate. Novel modeling methods are microstrucutre characterization techniques are also discussed. It is found that high fidelity models do a generally good job bounding experimentally observed melt pool morphologies for both bead-on-plate and powder bed cases. Microstructure models are able to bound a number of experimentally observed microstructure statistics, but with low precision due to challenges with calibrating the microstructure growth model parameters. A developed modified inherent strain distortion model does not accurately predict observed distortions. A lumped laser distortion model shows promise in being both accurately and precisely bounding observed outcomes from the deflection comb build, but requires further evaluation on more builds and geometries.

36 MATERIALS SCIENCE↗
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↗