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Hybrid additive featured plates used to form an overall microchannel heat exchanger and corresponding method of manufacture are disclosed. Various additive manufacturing (AM) techniques may be used to form walls defining microchannel features on a plate substrate. The manufacturing method is a hybrid process in that leverages both additive and conventional manufacturing techniques to minimize both cost and fabrication time.
Solution-processed semiconducting main-group chalcogenides (MMCs) have attracted increasing research interest for next-generation device technologies owing to their unique nanostructures and superior properties. To achieve the full potential of MMCs, the development of highly universal, scalable, and sustainable synthesis and processing methods of chalcogenide particles is thus becoming progressively more important. Here we studied scalable factors for the synthesis of two-dimensional (2D) V–VI chalcogenide nanoplates (M 2 Q 3 : M = Sb, Bi; Q = Se, Te) and systematically investigated their colloidal behaviour and chemical stability. Based on a solvent engineering technique, we demonstrated scale-up syntheses of MMCs up to a 900% increase of batch size compared with conventional hydrazine-based gram-level syntheses, and such a scalable approach is highly applicable to various binary and ternary MMCs. Furthermore, we studied the stability of printable chalcogenide nanoparticle inks with several formulation factors including solvents, additives, and pH values, resulting in inks with high chemical stability (>4 months). As a proof of concept, we applied our solution-processed chalcogenide particles to multiple additive manufacturing methods, confirming the high printability and processability of MMC inks. Furthermore, the ability to combine the top-down designing freedom of additive manufacturing with bottom-up scalable synthesis of chalcogenide particles promises great opportunities for large-scale design and manufacturing of chalcogenide-based functional devices for broad application.
The US Department of Energy’s (DOE’s) Advanced Manufacturing Office held the virtual workshop entitled “Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy” in November and December 2020. The workshop brought together participants from universities/laboratories, industries, equipment manufacturers, technology vendors, nongovernmental organizations, and subject-matter experts to discuss transformative technologies and strategies to substantially improve the performance (e.g., energy productivity, thermal efficiency, reduced greenhouse gas [GHG] emissions, reduced number of process steps) of thermal processing systems in the industrial sector. The US industrial sector accounts for 32% of the nation’s primary energy use (including feedstocks), and refining, chemicals, pulp and paper, iron and steel, and food products represent the top energy-consuming sectors. Thermal processing or process heating represents the largest energy use category; it accounts for 63% of all energy use in manufacturing. Additionally, thermal processing is the largest contributor of carbon dioxide (CO2) generation, resulting from combustion of fuels and process related chemical reactions, such as in the case of cement and lime production. The challenge of achieving net-zero industrial GHG emissions is colossal considering the established industrial base that depends mainly on carbon-based processes and energy sources; the time frame and cost to replace carbon-based energy sources and feedstocks; and the long-term outlook for development with large-scale adaptation of alternative non carbon–based technologies. The goals of the DOE Advanced Manufacturing Office Thermal Process Intensification Workshop were as follows: (1) Identify R&D gaps and opportunities to facilitate transformative improvement in industrial thermal processes beyond current technologies and allow for entirely new methods for processing materials; (2) Gain insight into new and innovative approaches to thermally intensify processes, reduce heat demand, harness waste heat, and use fuels and hydrocarbon feedstocks more efficiently; (3) Identify the R&D pathways to thermal process intensification (TPI) with the highest potential for impact and adoption by the industrial sector; (4) Define areas of research, development, and demonstration (RD&D) activities to accelerate development and application of emerging and transformative technologies to intensify thermal processes in industry. The scope and focus of the workshop were defined to meet these goals. Based on the available data for energy use and GHG emissions, the industries that collectively use more than 80% of the total process heating energy consumption were selected as primary focus areas. The chosen industries were combined into the following four groups based on similarities in their thermal processes: high-temperature metal processing (iron and steel industry, alumina-aluminum industry); high-temperature nonmetal and mineral processing (cement and glass industry); medium- to low-temperature thermal processing (food processing and pulp and paper industry as part of forest products sector); and hydrocarbon processing (petroleum refining and chemical industry). Furthermore, all potential TPI technologies associated with the processes defined were considered as part of the workshop. Different types of TPI technologies possible in industrial in the document.
This Laboratory Directed Research and Development project developed and applied closely coupled experimental and computational tools to investigate powder compaction across multiple length scales. The primary motivation for this work is to provide connections between powder feedstock characteristics, processing conditions, and powder pellet properties in the context of powder-based energetic components manufacturing. We have focused our efforts on multicrystalline cellulose, a molecular crystalline surrogate material that is mechanically similar to several energetic materials of interest, but provides several advantages for fundamental investigations. We report extensive experimental characterization ranging in length scale from nanometers to macroscopic, bulk behavior. Experiments included nanoindentation of well-controlled, micron-scale pillar geometries milled into the surface of individual particles, single-particle crushing experiments, in-situ optical and computed tomography imaging of the compaction of multiple particles in different geometries, and bulk powder compaction. In order to capture the large plastic deformation and fracture of particles in computational models, we have advanced two distinct meshfree Lagrangian simulation techniques: 1.) bonded particle methods, which extend existing discrete element method capabilities in the Sandia-developed , open-source LAMMPS code to capture particle deformation and fracture and 2.) extensions of peridynamics for application to mesoscale powder compaction, including a novel material model that includes plasticity and creep. We have demonstrated both methods for simulations of single-particle crushing as well as mesoscale multi-particle compaction, with favorable comparisons to experimental data. We have used small-scale, mechanical characterization data to inform material models, and in-situ imaging of mesoscale particle structures to provide initial conditions for simulations. Both mesostructure porosity characteristics and overall stress-strain behavior were found to be in good agreement between simulations and experiments. We have thus demonstrated a novel multi-scale, closely coupled experimental and computational approach to the study of powder compaction. This enables a wide range of possible investigations into feedstock-process-structure relationships in powder-based materials, with immediate applications in energetic component manufacturing, as well as other particle-based components and processes.
Abstract Determination of permeability of thick-section glass fabric preforms with fabric layers of different architectures is critical for manufacturing large, thick composite structures with complex geometry, such as wind turbine blades. The thick-section reinforcement permeability is inherently three-dimensional and needs to be obtained for accurate composite processing modeling and analysis. Numerical simulation of the liquid stage of vacuum-assisted resin infusion molding (VARIM) is important to advance the composite manufacturing process and reduce processing-induced defects. In this research, the 3D permeability of thick-section E-glass fabric reinforcement preforms is determined, and the results are validated by a comparison between flow front progressions from experiments and from numerical simulations using ansys fluent software. The orientation of the principal permeability axes were unknown prior to experiments. The approach used in this research differs from those in literature in that the through-thickness permeability is determined as a function of flow front positions along the principal axes and the in-plane permeabilities and is not dependent on the inlet radius. The approach was tested on reinforcements with fabric architectures which vary through-the-thickness direction, such as those in a spar cap of a wind turbine blade. The computational simulations of the flow-front progression through-the-thickness were consistent with experimental observations.
Hydride-dehydride Ti-6Al-4V powder with particle size distribution of 75–175 μm were used to manufacture fatigue samples. Optimized processing parameters were used to maintain a relative density of >99.5% in the samples. Fatigue tests were carried out in R = σ min /σ max = -1 condition under stresses ranging from 150 to 500 MPa and the results were compared to samples that were additively manufactured using spherical powders. The fatigue lives were similar between the two types of powder; i.e., powder morphology has no effect on fatigue performance. However, the high surface roughness and martensitic microstructure resulting from the laser powder bed fusion process result in poor performance compared to samples machined from conventionally produced materials. In conclusion, crack initiation occurred consistently from surface defects because of high surface roughness of the additively manufactured parts.
Sandwich structured (SS) composites demonstrate considerable flexural stiffness and high strength-to-weight ratios and can be tailored as functional materials. Historically they have been constrained to specific material types and geometry due to limitations in manufacturing methods. However, employing additive manufacturing (AM), specifically direct ink writing (DIW), can provide an alternative method for making SS composites with complex and controllable micro and mesostructures with multifunctionality targeted at desired mechanical, thermal, and electrical properties. DIW, an extrusion-based AM technique, uses a viscous and thixotropic ink with desired components that, once printed, is cured to obtain the final complex net shape parts. In this paper, a novel hybrid AM technique is employed to manufacture SS composite materials containing bisphenol A-based epoxy core and carbon fiber reinforced polymer (CFRP) face sheets that are fabricated via DIW and vacuum infusion process (VIP), respectfully. We demonstrate that the fabrication of these SS composites can be tailored from a thermosetting material, from which additives and/or various lattice structures can be manufactured to achieve enhanced and desirable mechanical integrity with functional properties. Surface topology and mechanical testing techniques are used to characterize the fabricated hybrid SS composites to study and assess mechanical stability. A rheo-kinetic cure model was developed for the core material to allow for additive manufacturing process requirements while ensuring complete cross-linking for the thermoset-based core material. Because of the ability to obtain relatively small core-thickness and controlled architecture, this method now allows for fabricating layered micro-sandwich structures for realizing further light-weighting in relevant applications.
Here, in this paper, a novel hybrid wire arc additive manufacturing (WAAM) and ultrasonic nanocrystal surface modification (UNSM) on porosity manipulation and surface properties of aluminum 5356 alloys was studied. The goal is to improve the quality of the WAAM-built part by eliminating bigger pores and reducing its size, reducing surface roughness, and increasing surface hardness. The as-built WAAM and WAAM-UNSM-treated samples were quantitatively studied for porosity using an X-ray micro-computed tomography (μ-CT). The surface roughness was measured on the surface profile of the same samples before and after UNSM treatment. Followed by the Vickers micro-hardness tests to evaluate the hardness modified by the influence of the UNSM treatment. It was found that the bigger pores in the as-built WAAM samples were eliminated and the medium-sized pores were shrunk to almost half the size after the UNSM treatment. Further, the UNSM treatment showed a significant improvement in both surface roughness and hardness on the WAAM Al5356 samples. This experimental work demonstrates the critical advantages of hybrid WAAM-UNSM in improving the qualities of the WAAM processed parts.
This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).
Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.
This article outlines the current state-of-the-art binder jetting (BJT) additive manufacturing of functional ceramics. The impact of printing parameters, heat treatment processing, and testing conditions on the observed performance of these ceramics is discussed. Additionally, this article discusses the impact of physical properties such as density and mechanical strength on the overall performance of these functional ceramics. Although printing parameters and initial feedstock are crucial for the printability of the desired parts, other factors play an important role in the performance of the ceramic. Thermal post-processing is crucial to achieve optimized functional properties, while the testing orientation is key to obtaining the maximum output from the part. Finally, future research directions for this field are also discussed.
The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing rapid qualification of new materials for fabrication of nuclear relevant components using advanced manufacturing techniques. Particular interest is placed on code-qualifying stainless steel (SS) 316H processed by laser powder bed fusion (LPBF) additive manufacturing. A paradigm that incorporates data from in-situ sensing during the printing, ex-situ characterization, and advanced artificial intelligence–based models was established under the Transformation Challenge Reactor (TCR) program to develop a pedigree for each fabricated component that could be tracked from the feedstock to the component’s release for application. Under the TCR program, the Peregrine software was developed as a tool for incorporating the vast amounts of in-situ and ex-situ characterization data collected; all data stored on a rapidly growing digital platform. The digital platform allows for users to link site-specific process anomalies to the macro- and microstructure. The platform will eventually be able to predict component performance, which will be crucial to qualifying materials and components in risk-averse industries such as those supporting and building nuclear reactors. Current in-situ process monitoring techniques that are already integrated with software like Peregrine are advantageous for identifying process anomalies including powder spatter, component edge swelling, recoating-build interactions, and so on. However, additional data are required to fully predict the resulting microstructures needed for identifying relationships to component performance. The rapid cooling rates observed in LPBF are some of the highest of any bulk manufacturing process, resulting in heterogenous microstructures and typically causing anisotropy in mechanical properties. Moreover, evolved residual thermal stresses are high, which can cause severe defects such as delamination or cracking. Therefore, other in-situ monitoring methods are warranted for exploration to measure and map the thermal history, and potentially the stress state, of each build. This report summarizes different in-situ monitoring strategies proposed for LPBF with a focus on the more developed sensor systems. Novel capabilities for measuring melt pool temperatures are also addressed to better inform modeling efforts.
Multilayer dielectric (MLD) gratings with high diffraction efficiency and a high laser-induced damage (LID) threshold for pulse compressors are key to scaling the peak and average power of chirped pulse amplification lasers. However, surface defects introduced by manufacturing, storage, and handling processes can reduce the LID resistance of MLD gratings and impact the laser output. The underlying mechanisms of such defect-initiated LID remain unclear, especially in the femtosecond regime. In this Letter, we model dynamic processes in interactions of a 20-fs near-infrared (NIR) laser pulse and a MLD grating design in the presence of cylindrically symmetrical nodules and particle contaminants and cracks at the surface. Utilizing a dynamic model based on a 2D finite difference in time domain (FDTD) field solver coupled with photoionization, electron collision, and refractive index modification, we study the simulation results for the damage site distribution initiated by defects of various types and sizes and its impact on the LID threshold of the grating design.
The CACTUS project aimed to develop a novel solid sorbent and sorption module prototype for direct air capture (DAC) of carbon dioxide, by a moisture-swing adsorption (MSA) mechanism. Because MSA uses changes in humidity, not temperature to switch the sorbent from capturing CO 2 to releasing concentrated CO 2 it is > 4X more energy-efficient than the thermal swing process. The goal was to develop a sorbent with a CO 2 capture capacity of 1 mmol CO 2 /g sorbent (or 0.75 mmol CO 2 /g structured sorbent), at a projected scaled cost of < $\$$15/kg sorbent and demonstration of a path to < $\$$100/ton CO 2 . At the end of the project, we achieved 0.8 mmol CO 2 /g sorbent powders, 0.37 mmol CO 2 /g structured sorbent (50% of target), at a projected cost of $\$$29/kg sorbent and an estimated cost of $\$$160/ton CO 2 . The key innovation was SRI’s patented polymer aerogel synthesis platform, which was adapted to produce a nanoporous aerogel with a high density of CO 2 -adsorbing quaternary ammonium groups. This research discovered a new ammonium polymer sorbent and identified a chemical path for its fabrication. The new process solved the monomer immiscibility challenge encountered with the initial method (ammonium is hydrophilic and the crosslinker is hydrophobic). The team fabricated structured sorbent sheets consisting of a non-woven porous substrate impregnated with ammonium polymer and demonstrated its operation in custom made breakthrough test setup MSA DAC built at SRI, and which operates similarly to the envisioned large-scale CO 2 capture plant, to provide data for techno-economic analysis (TEA). TEA sensitivity analysis indicated that ∼$\$$100/ton CO 2 can be achieved if the target capacity of 0.75 mmol CO 2 /g structured sorbent is met. Also identified routes to decrease sorbent manufacturing cost to ∼$\$$19/kg by reducing amounts and recycling the organic solvents. More work is needed on transitioning process manufacturing from powders (which showed a capacity of up to 0.8 mmol/g sorbent) to structured sheets which showed a capacity of 0.37 mmol CO 2 /g structured sorbent (or 0.44 mmol/g sorbent if one excludes the inert porous substrate). This is likely due to different micro/nano-structure of the sorbent and material processing constraints at the laboratory scale. Sorbent cycling studies (> 100 cycles) are needed to investigate its performance stability over time.
This report describes the current state of modeling and simulation techniques for predicting the properties of materials fabricated with advanced manufacturing techniques, given the initial microstructure of the material. The report includes a literature survey and a gap analysis outlining and prioritizing key issues in applying these modeling and simulation techniques to nuclear reactor structural materials. The discussion covers both physics-based and data-driven modeling techniques and includes a broad range of manufacturing techniques and materials that may have future nuclear applications. This report is the second in a two-part series, with the first report covering modeling and simulation methods for predicting the initial, as-manufactured structure of advanced manufacturing materials, given a description of the process. Both reports focus on a set of manufacturing technologies likely to be applied to reactor structural components. Taken together, the two reports provide a complete summary of the current state of processing-structure-properties models for advanced manufacturing as well as a survey of applications to reactor structural materials
Multivariate multiple regression models are applied to simplified pyrotechnic igniters for the first time to understand how changes in manufactured parameters can affect the output gas dynamic response and the timing of ignition events. The statistical modeling technique is applied to demonstrate quantification of the effects of a set of independent variables measured in the as-fabricated igniters on a set of responses experimentally measured from the functioned igniters. Two independent process variables were intentionally varied following a full factorial experimental design while several other independent variables varied within their normal manufacturing variability range. The four igniter performance responses consisted of the timing of sequential events during igniter function and visual gas dynamic output in the form of shock wave strength observed with high-speed schlieren imaging. Linear regression models built using the measurements throughout the manufacturing processes and the output variance provide insight into the critical device parameters that dominate performance
Fe-3.8wt%Si transformer steels were processed using two different additive manufacturing (AM) techniques, laser powder bed fusion (LPBF) and directed energy deposition (DED). While the LPBF processed samples exhibited a strong <001> orientation of the BCC grains along the build axis, the DED processed samples exhibited a randomized texture along the build axis. DED processed samples showed substantially coarser columnar grains as compared to their LPBF counterparts. Here, the columnar grains exhibited a substantial number of low-angle sub-grain boundaries. All samples exhibited very good soft magnetic properties, with saturation magnetization (M s ) values ranging from 205 - 232 emu/gm, and coercivity (H c ) values ranging from 1.2 – 4.2 Oe. The Coercivity (H c ) values were significantly lower when the magnetic field was applied parallel to the build axis, as compared to being perpendicular, which can be rationalized based on the columnar nature of the grains, resulting in a higher number density of grain boundaries in case of the field applied perpendicular to the build axis.
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.