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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of fiber orientation in injection molding: Comparison of glass fiber, carbon fiber and their hybrid composites

Fiber orientation distribution (FOD) in injection-molded panels with respect to distance from the gate was analyzed using X-ray computed tomography (X-CT) for glass fiber (GF), carbon fiber (CF), and hybrid CF/GF (CGF) reinforced nylon 66. To understand the reason behind the FOD with different fiber types, computational fluid dynamics (CFD) and rheology were performed. Samples were extracted at three locations: near the gate, center, and opposite end. Thickness of the layers of typical skin-shell-core type FOD varied with fiber type and location. GF achieved flow direction alignment (in shell) earlier than viscous CF and CGF near the gate, whereas CF showed the highest flow-direction alignment at the center due to shear induced orientation. At the opposite end, GF experienced more backflow than others indicating faster mold filling owing to its lower viscosity. Hybrid CGF exhibited GF-dominated center and CF-dominated end region. The numerical model used to obtain FOD and rheological predictions for the CF and GF composites served to corroborate the trends observed in the experimental trials. The FOD responses across fiber types and location were reflected in their longitudinal and transverse properties. Only GF showed higher longitudinal modulus over transverse modulus near the gate attributed to rapid alignment, whereas CF and CGF exhibited opposite trend. However, fountain flow enhanced the longitudinal modulus over transverse modulus with the distance for all, particularly for CF. This study offers insights into mold filling behavior of different fibers which are critical in optimizing injection molding conditions for tailored final properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Macro-level mechanical interlocking: A rapid joining approach for additively manufactured compression molded composite panels

Composite joining typically involves multiple steps, such as drilling and surface treatment, as part of the manufacturing process, which leads to low throughput and long cycle times. In the present study, we demonstrated a macro-level mechanical interlocking (MI) based, rapid joining technique to assemble additively manufactured compression molded (AMCM) panels, enabling the production of parts larger than the mold dimensions. Composite panels made of 20 wt% short carbon fiber reinforced acrylonitrile butadiene styrene (CF/ABS) were joined using MI features of various geometries, namely tree (TR), dovetail (Dov), rectangle 2 (Rect2), and rectangle 1 (Rect1), and their in-plane strength was evaluated. The resultant strength of the tested MI joints reached up to 74 % of the baseline tensile strength (i.e., the ‘no joint’ case). Observations from optical and scanning electron microscopy revealed inadequate polymer diffusion between the adherends, indicating that the joint strength was primarily derived from mechanical interlocking. Additionally, the fracture surfaces exhibited stress-whitening marks, which were characterized using differential scanning calorimetry (DSC). The increase in melting enthalpy suggested local stretching of polymer chains due to MI. Finite element analysis (FEA) indicated that the Rect1 MI feature, which generated the lowest stress concentration, outperformed the others in terms of joint strength, achieving 42 MPa. As a demonstration of the MI joining method, a battery box tray measuring 108 cm × 34 cm using a mold with an effective dimension of 36 cm × 34 cm successfully manufactured, resulting in a part with an area three times larger than the mold. In conclusion, this study presents a promising approach to improving composite joining techniques while minimizing production complexities.

In-plane joining↗
DOE OSTI2025

Genesis of a novel high-rate composite manufacturing process using large-scale additive manufacturing – compression molding (AM-CM) system: Possibilities and limitations

Oak Ridge National Laboratory (ORNL) has developed a highly automated manufacturing process for thermoplastic composites that combines the benefits of Additive Manufacturing and Compression Molding (AM-CM) to produce high-performance functional composite structures at automotive production rates. Here, the AM-CM process creates highly precise preforms by additively placing extruded fiber-filled polymers (with controlled fiber orientations and multi-material configurations) in the desired mold location before undergoing a secondary compression molding process immediately before the preform cools down. Preforms can be in the form of short, long-chopped, or continuous fiber-filled thermoplastic polymers (e.g., CF/GF-filled ABS, PC, LM-PAEK, etc.). The AM-CM process combines the benefits of controlled fiber alignment, that is only achievable in AM-printed parts with the classical CM process, which eliminates porosity and good surface finish. A preform created using AM-CM can integrate various materials to enable additional architectural functionalities, including over-molding, selective stiffening, and the incorporation of electrically or thermally conductive channels. All these advantages come with a fast part production cycle time. The AM-CM process can manufacture multi-material, multi-functional parts in under 3 min, starting from raw material (pellets) to the final product. The novel AM-CM process offers superior microstructural control and enhanced multi-functionality previously unattainable with any other traditional high-rate thermoplastic composite manufacturing method. This work covers the manufacturing concept, system development, materials and applications of AM-CM process in detail.

Kumar, Vipin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2023

Investigation of pressure-controlled injection molding on the mechanical properties and embodied energy of recycled high-density polyethylene

Manufacturing with secondary feedstock has been identified as an effective strategy to improve plastic circularity. However, product quality inconsistencies arise due to variations in molecular weight, rheology, and mechanical properties. This work evaluates the processing of recycled high-density polyethylene using pressure-controlled injection molding with a focus on processing behavior and energy consumption. Further, the effects of injection velocity, packing pressure, and transfer position are benchmarked against a conventional velocity-controlled process. The experimental results show that the novel process control strategy significantly affects the mechanical properties, in-mold rheology, and energy consumption. Parts fabricated using pressure-controlled injection molding showed higher tensile properties due to increased macromolecular orientation. Additionally, reduced energy was used due to lower melt pressures required to completely replicate the cavity geometry. The results demonstrate the potential of the technology to support increased utilization of secondary feedstock and reduced carbon footprint.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analysis and Design for Sustainable Circularity of Barrier Films Used in Sheet Molding Composites Production

Sheet molding composites (SMC) are thermoset based molding compounds with a global market size of USD 2.1 billion in 2020. SMC is usually processed with multilayer barrier/carrier films to prevent emissions from volatile organic compounds (VOC). The plastic barriers are separated from SMC after the curing process and discarded. To illustrate the high consumption volume of SMC barrier/carrier films, Kohler Co., a company which uses SMC in their bath and showering product lines, alone generates over 1M lb/year of SMC barrier/carrier film waste. Therefore, high amount of SMC barrier/carrier film plastic waste creates a great opportunity for reusing, recycling, or valorization at its end of life. This can be achieved through transition from a linear to a circular supply chain design. However, this transition will not be feasible without overcoming the technical and economic barriers. This project aims to tackle this by making the film circular. Different end of life alternatives will be evaluated experimentally. The reusability of the film will be studied through applying different mechanical and solvent cleaning techniques. The operability concerns regarding the utilization of the cleaned film for long run operations will be addressed by thermally stitching the film. Pelletizing of the cleaned film will also be investigated as a viable technique for recycling or downcycling of the film as molded products. In addition to technical barriers, designing a sustainable circular supply chain for SMC carrier film products requires uncovering the collaboration opportunities between suppliers and users at different stages of the value chain to expand its use to other products. These opportunities will be investigated by finding companies willing to buy or treat the plastic film waste. Finally, the economic viability and environmental impacts of these circular design alternatives will be evaluated through techno-economic assessment (TEA) and life cycle analysis (LCA). Techno-economic and life cycle inventory data for various pathways will also be made available as one of the main outcomes of this project. Preliminary LCA results indicate that the current SMC film supply chain (base case) emits 5.81 tonne CO2 eq. and consumes 104 GJ energy per tonne of SMC film. It is estimated that alternative circular pathways result in 14-20% less CO2 emissions and 34-44% reduction in embodied energy. This project will develop a tool to analyze the circularity and sustainability of hundreds of combinations of alternatives in different life cycle stages. Users of this tool will be able to choose the best combination among these alternatives with respect to the selected economic, environmental and circularity objectives.

circular economy↗
DOE OSTI2024

Modulated Thermomechanical Analysis of Compression-Molded High-Density Polyethylene

Thermomechanical analysis (TMA) experiments conducted on high-density polyethylene (HDPE) show both reversible and irreversible dimensional changes. To further explore these reversible and irreversible processes, modulated thermomechanical analysis (MTMA) was used. Before reliable data on compression-molded HDPE was collected, a parameter optimization was performed to obtain a suitable MTMA method. Once a suitable method was obtained, several MTMA experiments were conducted on compression-molded HDPE. This work highlights the steps taken during the MTMA parameter optimization and the results obtained from MTMA experiments conducted on pristine compression-molded HDPE samples.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterization of Polyhydroxybutyrate-Based Composites Prepared by Injection Molding

The waste generated by single-use plastics is often non-recyclable and non-biodegradable, inevitably ending up in our landfills, ecosystems, and food chain. Through the introduction of biodegradable polymers as substitutes for common plastics, we can decrease our impact on the planet. In this study, we evaluate the changes in mechanical and thermal properties of polyhydroxybutyrate-based composites with various additives: Microspheres, carbon fibers or polyethylene glycol (2000, 10,000, and 20,000 MW). The mixtures were injection molded using an in-house mold attached to a commercial extruder. The resulting samples were characterized using microscopy and a series of spectroscopic, thermal, and mechanical techniques. We have shown that the addition of carbon fibers and microspheres had minimal impact on thermal stability, whereas polyethylene glycol showed slight improvements at higher molecular weights. All of the composite samples showed a decrease in hardness and compressibility. The findings described in this study will improve our understanding of polyhydroxybutyrate-based composites prepared by injection molding, enabling advancements in integrating biodegradable plastics into everyday products.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. Further, the MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10%. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11%. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

42 ENGINEERING↗
DOE OSTI2024

Characterization of spent nuclear fuel canister surface roughness using surface replicating molds

In this study we present a replication method to determine surface roughness and to identify surface features when a sample cannot be directly analyzed by conventional techniques. As a demonstration, this method was applied to an unused spent nuclear fuel dry storage canister to determine variation across different surface features. In this study, an initial material down-selection was performed to determine the best molding agent and determined that non-modified Polytek PlatSil23-75 provided the most accurate representation of the surface while providing good usability. Other materials that were considered include Polygel Brush-On 35 polyurethane rubber (with and without Pol-ease 2300 release agent), Polytek PlatSil73-25 silicone rubber (with and without PlatThix thickening agent and Pol-ease 2300 release agent), and Express STD vinylpolysiloxane impression putty. The ability of PlatSil73-25 to create an accurate surface replica was evaluated by creating surface molds of several locations on surface roughness standards representing ISO grade surfaces N 3 , N 5 , N 7 , and N 8 . Overall, the molds were able to accurately reproduce the expected roughness average (R a ) values, but systematically over-estimated the peak-valley maximum roughness (R z ) values. Using a 3D printed sample cell, several locations across the stainless steel spent nuclear fuel canister were sampled to determine the surface roughness. These measurements provided information regarding variability in normal surface roughness across the canister as well as a detailed evaluation on specific surface features (e.g., welds, grind marks, etc.). The results of these measurements can support development of dry storage canister ageing management programs, as surface roughness is an important factor for surface dust deposition and accumulation. This method can be applied more broadly to different surfaces beyond stainless steel to provide rapid, accurate surface replications for analytical evaluation by profilometry.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Effect of fiber reinforcement on the process parameters of injection molding manufacturing process

Use of fillers with the resin is common in injection molding manufacturing technique to enhance the stiffness and strength of the manufactured components. However, the addition of fillers changes the rheological and mechanical properties of the material, required adjustments in the process parameters used for the manufacturing. The present study aims to investigate this impact of fillers on the process parameters of the injection molding manufacturing process. Neat Acrylonitrile Butadiene Styrene (ABS) and ABS reinforced with short glass fibers are used as the materials to perform the comprehensive numerical analysis including complete process sequence, namely filling, packing, cooling and warpage. Parametric studies are conducted to optimize the manufacturing process for minimal warpage (or shrinkage) in the produced part. The selected process parameters for the parametric study include cooling time, packing pressure, and mold temperature, all of which directly impact the quality of the manufactured parts. The optimal values obtained for these parameters are compared to examine the effect fiber reinforcement has on the manufacturing process.

Garg, Nikhil↗
DOE OSTI2022

High Pressure and Two-Sided Mold Surface Solutions (CRADA Final Report)

This project provided surface solutions for highly loaded precision tooling relevant to the automotive industry. Previous development work in coatings for Big Area Additive Manufacturing (BAAM) has produced commercialized coatings for low temperature Acrylonitrile Butadiene Styrene (ABS) tools (TD Coat RT) as well as a soft coating for high temperature applications (TD Seal HT). The team developed these coatings and commercialized them during a prior Manufacturing Demonstration Facility (MDF) technical collaboration (NFE-15-05793). During the prior collaboration, commercialization testing was successfully performed by the Boeing and NAVAIR and resulted in an Awards for Composites Excellence (ACE), award at CAMX – The Composites and Advanced Materials Expo 2016. While this work was clearly successful, gaps in the technology were identified, that prevent widespread adoption of large-scale additive manufacturing in high pressure tooling applications prevalent in the automotive industry. In this project we focused on development of surface solutions for two-sided, compression molding tooling printed on a large-scale polymer AM system in an arbitrary build orientation. We have printed a single cavity extrusion compression molding mold using Polyphenylene sulfide/carbon fiber (PPS/CF) materials, 50% by weight CF samples. These processes are commonly used by Original Equipment Manufacturers (OEM) and Tier 1 suppliers for high volume automotive applications. Several OEMs have identified the need for such technology, indicating significant time and monetary savings could be possible if commercially available surface solutions for high pressure Additive Manufacturing (AM) tooling existed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mining the Penicillium expansum Genome for Virulence Genes: A Functional-Based Approach to Discover Novel Loci Mediating Blue Mold Decay of Apple Fruit

Blue mold, a postharvest disease of pome fruits, is caused by the filamentous fungus Penicillium expansum. In addition to the economic losses caused by P. expansum, food safety can be compromised, as this pathogen is mycotoxigenic. In this study, forward and reverse genetic approaches were used to identify genes involved in blue mold infection in apple fruits. For this, we generated a random T-DNA insertional mutant library. A total of 448 transformants were generated and screened for the reduced decay phenotype on apples. Of these mutants, six (T-193, T-275, T-434, T-588, T-625, and T-711) were selected for continued studies and five unique genes were identified of interest. In addition, two deletion mutants (Δt-625 and Δt-588) and a knockdown strain (t-434 KD ) were generated for three loci. Data show that the Δt-588 mutant phenocopied the T-DNA insertion mutant and had virulence penalties during apple fruit decay. We hypothesize that this locus encodes a glyoxalase due to bioinformatic predictions, thus contributing to reduced colony diameter when grown in methylglyoxal (MG). This work presents novel members of signaling networks and additional genetic factors that regulate fungal virulence in the blue mold fungus during apple fruit decay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2022

MODELLING FIBER ORIENTATION DURING ADDITIVE MANUFACTURING COMPRESSION MOLDING PROCESSES

The production of high-performance thermoplastic composites reinforced with short carbon fibers can be achieved by a novel “additive manufacturing-compression molding” technique. An advantage of such combination is two-fold: controlled fiber orientation in additive manufacturing and less void content by compression molding. In this study, a computational fluid dynamics model has been developed to predict the behavior of printed layers during fiber-reinforced thermoplastic extrusion and subsequent compression molding. The fiber orientation was modelled with a simple quadratic closure model. The interaction between the fibers is included using a rotary diffusion coefficient which becomes significant in concentrated regimes. Finally, the second order orientation tensor is coupled with the momentum equation as an anisotropic part of the stress term. The effect of processing parameters on the behavior of printed layers was investigated to determine the favorable printing scenarios. The developed numerical model enables design of high-performance composites with tunable mechanical properties.

Seta, Berin↗
DOE OSTI2022

MODELLING OF ADDITIVE MANUFACTURING – COMPRESSION MOLDING PROCESS USING COMPUTATIONAL FLUID DYNAMICS

A computational fluid dynamics model has been developed to predict the behavior of a printed strands during a novel material extrusion additive manufacturing and compression molding process. While the traditional additive manufacturing process enables control over the fiber orientation within the part, it would also result in high void content. On the other hand, compression molding produces parts with low porosity levels at rapid processing cycle time but lacks control over the microstructure. The novel additive manufacturing – compression molding (AM-CM) integrating both these processes offers control over both the microstructure and porosity to manufacturing high performance composite parts. The numerical model developed here enables to analyze the effect of processing parameters on the behaviour of printed layer that is subsequently compressed and to determine the optimal printing parameters for high-performance composite part design.

Seta, Berin↗
DOE OSTI2022

Long fiber injection molding

An improved method for obtaining high fiber volume fraction, long fiber injection molded articles is provided. According to one embodiment, the method includes forming an injection molding feedstock by cutting pre-impregnated fiber-reinforced tape into platelets. The platelets can be coated with a thin layer of polymer to allow sliding of the platelets with respect to each other at the early stages of plastication, rather than forcing relative motion of fibers with respect to each other. The method can further include the dispersion of material only in the final stages of the injection molding screw to promote gentle motion of the feedstock at the earlier stages of the plastication process. The method allows improvement of mechanical properties of articles manufactured with equipment and techniques that are prevalent in high volume automotive and consumer industries.

Kunc, Vlastimil↗
DOE OSTI2023

Process Variability Effects on Tensile Response in Injection Molded, Fluorinated Thermoplastics

The mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight-averaged molecular weight. Parallel plate rheology was used to obtain the zero-shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero-shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not presented for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens' sides as a flash due to higher mobility with CTFE segments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Material extrusion with integrated compression molding of NdFeB/SmFeN nylon bonded magnets using small- and large-scale pellet-based 3D-printers

High-density bonded rare-earth magnets are manufactured using pellet-fed additive manufacturing (AM)/material extrusion and an integrated additive manufacturing-compression molding (AM-CM) process. Neodymium iron boron – samarium iron nitride in polyamide 12 (NdFeB-SmFeN/PA12) of 93 % weight fraction (65 % volume fraction) are used for the study. The mechanical properties (tensile strength and modulus), magnetic properties (maximum energy density, coercivity, remanence) are reported. Manufacturing parameters such as layer height, barrel temperatures, screw speed and gantry feed rate are optimized to obtain the highest possible density of the magnets using a small-scale desktop material extrusion printer. Large scale integrated additive manufacturing-compression molding (AM-CM) is then utilized to increase the density of the magnets by reducing porosity defects common in the material extrusion process. The density of as-printed magnets was 5.2 g/cm 3 with a BH max value of 124.14 kJ/m 3 , tensile strength of 20 MPa and a modulus of 2 GPa. AM-CM increased the density of the compound by 5.5 % (5.49 g/cm 3 ). The reduction in porosity was confirmed using X-ray tomography (XCT). Improvement in mechanical strength of the material was also observed, with an increase in tensile strength of 25 % (25.09 MPa) and increase in tensile modulus of 275 % (5.49 GPa). Scanning electron microscopy showed increased particle-matrix adhesion with the integrated AM-CM process.

36 MATERIALS SCIENCE↗