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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
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 OSTI2023

A novel additive manufacturing compression overmolding process for hybrid metal polymer composite structures

Metal polymer composites combining low density, high strength composites with highly ductile and tough metals have gained traction over the last few decades as lightweight and high-performance materials for industrial applications. However, the mechanical properties are limited by the interfacial bonding strength between metals and polymers achieved through adhesives, welding, and surface treatment processes. In this paper, a novel manufacturing process combining additive manufacturing and compression molding to obtain hybrid metal polymer composites with enhanced mechanical properties is presented. Additive manufacturing enabled deposition of polymeric material with fibers in a predetermined pattern to form tailored charge or preform for compression molding. Here, a grade 300 maraging steel triangular lattice is first fabricated using AddUp FormUp350 laser powder bed system and compression overmolded with additively manufactured long carbon fiber-reinforced polyamide-6,6 (40% wt. CF/PA66) preform. The fabricated hybrid metal polymer composites showed high stiffness and tensile strength. The stiffness and failure characteristics determined from the uniaxial tensile tests were correlated to a finite element model within 20% deviation. Fractographic analyses was performed using microscopy to investigate failure mechanisms of the hybrid structures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scaling Up metal additive manufacturing process to fabricate molds for composite manufacturing

Direct Energy Deposition (DED) systems are currently used to repair and maintain existing parts in the aerospace and automotive industries. This paper discusses an effort to scale up the DED technique in order to Additively Manufacture (AM) molds and dies used in the composite manufacturing industry. The US molds and dies market has been in a rapid decline over the last decade due to outsourcing to non-US entities. Oak Ridge National Laboratory (ORNL), Wolf Robotics and Lincoln Electric have developed a Metal Big Area Additive Manufacturing (MBAAM) system that uses a high deposition rate and a low-cost wire feedstock material. In this work we used the MBAAM system with a mild steel wire, ER70S-6, to fabricate a compression molding mold for composite structures used in automotive and mass-transit applications. In addition, the mechanical properties of the AM structure were investigated, and it was found that the MBAAM process delivers parts with high planar isotropic behavior. The paper investigates the microstructure and grain of the printed articles to confirm the roots of the observed planar isotropic properties. Here, the manufactured AM mold was used to fabricate 50 composite parts with no observed mold deformations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Toward large-scale simulation of residual stress and distortion in wire and arc additive manufacturing

In this study, we aim to advance the structural analysis of wire and arc additive manufacturing (WAAM) by considering the thermomechanical features inherent in direct energy deposition. Simulation approaches including the iterative substructure method (ISM), dynamic mesh refining method (DMRM), and graphics processing unit (GPU) based explicit finite element method (FEM) were developed for accelerating additive manufacturing stress analysis that is very time consuming by conventional numerical methods. ISM and DMRM take advantage of the strong nonlinearity phenomenon near the moving heat source by reducing the global iterations and refining the local mesh, respectively. In addition to the use of GPUs, the explicit FEM is accelerated by a time scaling technique based on the inherent strain concept. The residual stress and distortion of two large builds were analyzed, showing very consistent numerical results and good agreement with experiments. Compared with the commercial software Abaqus, the novel approaches reduced the computational cost substantially without compromising accuracy. The GPU code showed the highest computational efficiency (30~70×), while DMRM and ISM had acceleration factors of 9× and 3×, respectively. Finally, such high-fidelity modeling approaches will be very useful for building up a digital twin of WAAM to reduce development time and cost.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Process-Structure-Property Relations for As-Deposited Solid-State Additively Manufactured High-Strength Aluminum Alloy

Solid-state additive manufacturing methods provide innovative solutions to circumvent problems associated with materials susceptible to hot cracking by avoiding liquid solid phase transformations. In this work, the process parameter influence on microstructural evolution and mechanical response of a fully dense aluminum alloy 7050 (AA7050) component manufactured via a rapid, solid-state additive manufacturing process known as Additive Friction Stir Deposition (AFS-D) was quantified for the first time. Three sections (starting dwell, transient, crossover of roads) of the deposition that exhibit differing thermomechanical processing histories were evaluated for the resulting microstructure and mechanical response. The microstructural characterization was performed on the as-deposited AA7050 via Electron Backscatter Diffraction (EBSD), TEM, optical microscopy, and Scanning Electron Microscopy (SEM). The microstructural characterization revealed refined constituent particles and grains throughout the as-deposited AA7050 microstructure. Furthermore, quasi-static tensile experiments were conducted in both the build and transverse directions, in order to quantify the orientation influence on tensile properties of the as-deposited AA7050 build. Spatially dependent tensile properties were observed in the material due to heat input variation coarsening of secondary phases towards the initial layers of the AFS-D build. Post-mortem analysis revealed that voids nucleated and coalesced from the overgrowth of the strengthening precipitates present in the material, resulting in fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase transformation dynamics guided alloy development for additive manufacturing

Fusion-based additive manufacturing technologies enable the fabrication of geometrically and compositionally complex parts unachievable by conventional manufacturing methods. However, the non-uniform and far-from-equilibrium heating/cooling conditions pose a significant challenge to consistently obtaining desirable phases in the as-printed parts. Here, in this study, we report a martensite stainless steel development guided by phase transformation dynamics revealed by in-situ high-speed, high-energy, high-resolution X-ray diffraction. This developed stainless steel consistently forms desired fully martensitic structure across a wide range of cooling rates (10 2 –10 7 °C/s), which enables direct printing of parts with fully martensitic structure. The as-printed material exhibits a yield strength of 1157 ± 23 MPa, comparable to its wrought counterpart after precipitation-hardening heat-treatment. The as-printed property is attributed to the fully martensitic structure and the fine precipitates formed during the intrinsic heat treatment in additive manufacturing. The phase transformation dynamics guided alloy development strategy demonstrated here opens the path for developing reliable, high-performance alloys specific for additive manufacturing.

17–4 PH stainless steel↗
DOE OSTI2022

On the efficacy of post-build thermomechanical treatments to improve properties of Zirconium fabricated using ultrasonic additive manufacturing

Here, hot-isostatic pressing has been applied to Zirconium plate fabricated using the ultrasonic additive manufacturing (UAM) technique to enhance interfacial bond quality. Specimens heated to 800 C for 1 h at 100 MPa pressure showed grain growth across many prior foil-to-foil interfaces, thereby increasing foil adhesion. In addition to the material softening induced by a loss of Hall-Petch strengthening, premature failure of specimens loaded parallel to the build direction was observed. Premature specimen failure was attributed to the local delamination at prior foil-to-foil boundaries where grain growth was pinned by Ti impurities introduced during the UAM process via the interaction between a Ti-alloy buffer foil and the welded Zr-foils underneath. In addition, the presence of Ti along select foil interfaces resulted in the nucleation and growth of secondary (Zr,Ti)(Fe,Cr) laves phases during higher-temperature thermomechanical processing. Using a combination of micro-scale X-ray computed tomography, fractography, and in-situ digital image correlation, the effect of defects along prior foil-to-foil boundaries was revealed as delamination-assisted plasticity accelerated specimen failure in preferred tensile orientations. These findings underscore the importance of impurity control when optimizing weld quality of higher-strength material systems using ultrasonic welding.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predictive binder jet additive manufacturing enabled by clean burn-off binder design

Binder jet additive manufacturing (BJAM) presents an avenue for advanced manufacturing of various high-value materials due to high deposition rates, scalability, and geometric flexibility. However, conventional organic binders in BJAM introduce residual carbon upon pyrolysis, often leading to imprecise alloy composition in the final sintered part. The undesirable residual carbon from binder burn-off limits the application of BJAM for high-performance alloys due to their high sensitivity to carbon addition. In this study, we have designed poly(vinylpyrrolidone-co-vinyl acetate) (PVP-VAc) as a clean burn-off binder for BJAM, where excess oxygen groups in VAc enable cleaner burn-off and reduce residual carbon retention. Compared to a widely used commercial binder, the optimized PVP-VAc binder reduced residual carbon retention by over 90% in H13 tool steel. The significant reduction in residual carbon enables predictable printing and subsequent sintering of complex H13 tool steel geometries, an alloy known to have substantial challenges around distortion due to the sintering window shifting from carbon addition. The design of a clean burnout binder provides a major path forward for BJAM by enabling new AM designs and applications for composition-sensitive high-performance alloys, such as nickel-based superalloys, titanium alloys, and high alloyed steels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Creep deformation and cavitation in an additively manufactured Al-8.6Cu-0.4Mn-0.9Zr (wt%) alloy

Creep deformation and cavitation were investigated at 300 ºC in both tension and compression for an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr (wt%) alloy in the as-fabricated state and after various aging treatments (aging at 300 °C/200 h or 350 °C/24 h and overaging at 400 °C/200 h). Creep mechanisms at 300 °C were determined by relating the measured creep response to corresponding microstructural and X-ray computed tomography observations. In compression, alloys in the as-fabricated and two aging conditions exhibited similarly high creep resistance. Overaging (400 °C/200 h) led to substantial coarsening of intragranular θ-Al2Cu precipitates and an expected drop in their Orowan strengthening contribution. In tension, minimum strain rates comparable to those in compression were obtained at any given stress; however, upon accumulation of some plastic strain in the matrix, creep cavities started to form, leading to accelerated tertiary stage creep deformation and rupture. Cavitation occurred exclusively along melt pool boundaries due to locally enhanced diffusion enabled by (i) large grain-boundary area in adjacent fine-grained zones and (ii) localization of creep strain in nearby heat-affected zones. Although cavity growth was initially diffusion-controlled, its rate was determined by matrix creep rate, consistent with constrained cavity growth mechanisms. This study reveals how microstructural complexities induced by the additive manufacturing process affect the creep and cavitation behavior of Al-Cu-Mn-Zr alloys. The underlying creep and cavitation mechanisms uncovered in this study point to pathways that improve the high-temperature properties of additively manufactured alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding the influence of boron in additively manufactured GammaPrint®-700 CoNi-based superalloy

Boron is commonly added to superalloys in small amounts to enhance creep resistance, but can lead to cracking at high concentrations, especially during the additive manufacturing process. Two variants of CoNi-based GammaPrint®-700 superalloy with different B contents (0.08 at% vs 0.16 at%) were printed via laser powder bed fusion (LPBF) with the same printing parameters, with only the high B alloy exhibiting solidification cracking. Atom probe tomography (APT) revealed stronger segregation behaviors in the high B alloy compared to the low B alloy at both the inter-dendritic regions and grain boundaries (GBs). The segregation behavior at inter-dendritic regions was well captured with Scheil simulation and can correlate with the existing cracking susceptibility index (CSI) on cracking tendencies, although high angle GBs are where cracking occurs according to electron backscatter diffraction (EBSD) measurements. Additionally, the extent of GB segregation was compared between the high B and low B alloy. Higher B additions led to significantly more GB B segregation in the high B alloy compared to the low B alloy. Further, for the high B alloy, the cracked region of one GB exhibited higher levels of B compared to the uncracked region of the same GB. However, much higher B contents were also found in two other uncracked GBs in the high B alloy, which demonstrates that higher GB B concentrations are not fully responsible for the cracking. A much larger variance in GB B segregation content was found in the high B alloy compared to the low B alloy. These phenomena were explained with a solidification model with the GB segregation content expressed explicitly by a modified Langmuir-McLean equation. This model linked the GB segregation content with solidification undercooling, which can be used as quantitative cracking criteria for future builds.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A time-parallel method for scalable heat transfer simulations of additive manufacturing

Here, a major challenge in simulating the thermal behavior in additive manufacturing processes is the disparate length and time scales between transport phenomena occurring in the melt pool and the component. A common simulation approach relies on spatial decomposition for parallel computing, but due to the nature of heat transfer in AM, where most of the computational expenditure is localized near the melt pool, the computational speedup from spatial parallelization saturates quickly. Therefore, additional parallelism by means of time-domain decomposition is needed to fully take advantage of high-performance computing (HPC) resources. This work introduces a time-parallel method to improve the computational scalability of additive manufacturing simulations on HPC systems, while maintaining high temporal resolution of heat transfer near the melt pool. The method, inspired by the nonlinear paraexp formalism, performs an iterative superposition of nonlinear solutions to the initial value problem, integrating the heat equation across overlapping time-parallel intervals. For a single layer of the NIST AMB2018–01 L7 benchmark problem, the method achieves a 38.51x speedup in wall-clock time with a maximum error in the global temperature solution of 0.99%. This reduces the total solution time from 196.72 min to 5.11 min on 128 nodes of the ORNL Frontier supercomputer. The tradeoff between accuracy and total wall-clock time is investigated and recommendations for time-parallel deployment for AM problems are made.

Additive manufacturing↗
DOE OSTI2022

Waste plastic- and coke-derived flash graphene as lubricant additives

Lubricants play an essential role in reducing wear in mechanical systems. Carbon nanomaterial additives, such as graphene, have been found to significantly improve tribological performance when used as lubricant additives. Here, in this study, post-consumer plastic and metallurgical coke are converted into turbostratic flash graphene (FG) through flash Joule heating (FJH). The FG is then added to either poly(alpha olefin) 6 or 9 (PAO 6 or PAO 9). Adding waste plastic-derived FG (WPFG) and metallurgical coke-derived FG (MCFG) to lubricants resulted in a significant decrease in the coefficient of friction (CoF), wear scar diameter (WSD), and roughness during four-ball testing. WPFG and MCFG decrease the CoF in PAO 9 by 6% and 9% at 0.1 mg mL -1 , respectively, and in PAO 6 by 23% and 6% at 0.5 mg mL -1 , respectively. WPFG and MCFG decrease the WSD of steel balls in PAO 9 by 14% and 8% at 0.5 mg mL -1 , respectively, and in PAO 6 by 12% and 14% at 0.5 mg mL -1 , respectively, by forming a coating-like layer between the metal surfaces. Roughness decreased by 38% and 32% for WPFG and MCFG in PAO 6, respectively, and by 35% and 29% for WPFG and MCFG in PAO 9, respectively. Finally, preliminary life cycle analyses demonstrate that production of FG produces up to 99% less greenhouse gas emissions, requires 98% less energy, and consumes 99.9% less water when compared to conventional production techniques of graphene. Hence, metallurgical coke and waste plastic are shown to be ready feedstocks for high-quality FG lubricant additives.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Kinetic Monte Carlo simulations of structural evolution during anneal of additively manufactured materials

Our experiments indicated that upon a post-processing anneal, an additively manufactured 316L stainless steel exhibits cubic grains rather than the conventional equiaxed grains. In this work, we have used kinetic Monte Carlo simulations to explore the origin of these cubic grains. First, we implemented a new kinetic Monte Carlo model in parallel code SPPARKS to simulate grain growth and recrystallization under a residual energy distribution. Our model incorporates physical properties and real-time, as opposed to generic properties and relative time. We further validated that our SPPARKS simulations reproduced the expected kinetic behavior of single-grain evolution. We then used the validated approach to simulate the anneal of an additively manufactured material under the same conditions used in our experiments. We found that the cubic grains can origin from a periodically varying residual energy that may be present in additively manufactured materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatiotemporal parallelization of an analytical heat conduction model for additive manufacturing via a hybrid OpenMP + MPI approach

The ability to do thermal simulations for entire additive manufacturing builds is a key computational problem facing the additive manufacturing community; however, complex numerical models considering multiple physical phenomena currently do not have the capacity for simulations at this scale. To this end, conduction only analytic models offer a viable approach due to the massive drop in computational expense. In this work, we extend an existing implementation which uses a governing equation which can be evaluated at any point in space and time. This implementation already utilizes OpenMP with a spatial decompositions scheme stemming from a melt pool tracking algorithm. Furthermore, we then combine this with a parallel in time (PinT) approach to make the problem highly parallelizable. The new scheme, which uses MPI for internode communication and OpenMP for intranode communication, is shown to scale very well across multiple computational nodes. This approach results in the ability to simulate the 3D solidification conditions for entire layers of additively manufactured parts in minutes making part scale thermal simulations more practical.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Numerical framework for integrated additive manufacturing-compression molding (AM-CM) of thermoplastic composites

Additive manufacturing-compression molding (AM-CM) has emerged as a transformative technology in advanced composite manufacturing. Additive manufacturing (AM) offers high design flexibility and the ability to produce complex geometries with precisely aligned fibers in the preferred orientation. Compression molding (CM) enhances composite materials by providing excellent dimensional stability, reduced porosity, high production rates, and a smooth surface finish. Despite these advantages, extensive integrated analysis is required to optimize processing conditions for improved fiber orientation distribution (FOD) and porosity control. Here, this study develops a comprehensive numerical model to simulate the AM-CM manufacturing process. The model isolates the effects of both the AM and CM phases while also capturing their integration. Additionally, it accounts for heat transfer, temperature-dependent viscosity, and fiber orientation in the extruded fiber-filled polymer, accurately representing material behavior during processing. This approach enables the analysis of interactions between deposited beads of complex strand shapes and their interface regions after full compression. Moreover, the model predicts key parameters such as polymer flowability, fiber orientation, and temperature evolution in AM-CM parts. By optimizing processing conditions, it facilitates a controlled and predictable microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Engineering mechanical and thermomechanical performance in additive manufacturing–Compression molded composites through multiplexed extrusion

Traditional extrusion-based additive manufacturing is limited to single material systems, restricting the multifunctional properties of composites. For this work to overcome this limitation, multiplexed additive manufacturing–compression molding (AM-CM) was employed to fabricate multi-material thermoplastic composites with spatially tailored architectures. Neat acrylonitrile butadiene styrene (ABS) and 20 wt% carbon-fiber reinforced ABS (CF-ABS) were co-extruded through a core–sheath nozzle to produce hybrid composites with neat ABS as sheath (30-50 wt%) and CF-ABS as core (50 – 70 wt%). The results show that the hybrid composites have balance of mechanical and thermomechanical performance. The tensile strength and modulus of hybrid composites exhibited a 61–95% and 173–473% increase compared to neat ABS with increases in CF-ABS content whereas the impact resistance improved by 41% compared to CF-ABS at 50 wt% ABS. Additionally, hybrid composites showed significant reduction (54 - 70%) in creep strain at 100 °C compared to neat ABS. These findings demonstrate that multiplexed AM-CM enables tunable structure–property relationships, reducing CF-ABS usage up to 50 wt% while maintaining balanced stiffness, toughness, and creep resistance.

Additive manufacturing↗