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All sources NASA NTRS DOE OSTI

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats (Integration of Additive Manufacturing and Reinforced Polymer Composite)

Lightweight automotive seats offer multiple benefits to original equipment manufacturers in terms of cost savings from various aspects, including less material usage, more integrated processes, and compliance with Corporate Average Fuel Economy Standards. Original equipment manufacturers have been focusing on innovative ways to produce light weight automotive seats. The commercially available automotive seats are currently made of multiple metal components combined through welding and fasteners. The use of additive manufacturing and composite structures is particularly useful for light weighting the automotive components. Additive manufacturing (AM) offers multiple advantages over traditional manufacturing processes such as freedom of design thereby enabling complex structural geometries, mass customization and waste minimization, and control over the fiber alignment through deposition in a predetermined pattern. Combining metal inserts with polymer composites through a novel manufacturing process allows design of lightweight and high-performance materials for automotive components. However, fabricating these metal polymer composite structures through traditional manufacturing processes limits their mechanical properties due to limited design freedom, lack of control over fiber orientation in composite parts, and poor interfacial bonding between the constituent materials. It is essential to develop a novel manufacturing process to enable high throughput production of lightweight automotive seats using metal and polymer composites. As such it is important to design the automotive seat suitable for manufacturing via this process and perform mechanical characterization on various subcomponents of the seat to ensure that the design and performance requirements provided by the auto manufacturer are met. The aim of this project is to develop a novel manufacturing technique to produce lightweight automotive seat by combining AM with conventional manufacturing processes. The car seat back panel will be designed via topology optimization and numerical simulations to minimize the overall weight while ensuring it meets all the performance requirements. The optimization of the seat back structure will be based on computational stress analysis to maximize the stiffness and minimize the weight. Materials currently used by Ford Motor Company will be adopted for a few subcomponents while the in-house composite materials will be used for the rest of the seat back. The composite and metallic materials will be tested to determine their mechanical properties as these are necessary for simulations. A novel manufacturing process will be developed to integrate AM metal inserts with discontinuous reinforced composite through large scale additive manufacturing and compression overmolding processes. The developed manufacturing technique will be used to fabricated various subcomponents suitable for the seat back design and mechanically tested to determine their properties. The manufacturing of the lightweight seat back design through this process involves integrated AM metal inserts with the composite structure for recliner connection. The manufacturing of the entire seat back which is lightweight through the novel manufacturing process will be discussed. The performance of the designed seat back will be investigated through numerical simulations and shown to meet all the requirements provided by the auto manufacturer. The final goal of developing a novel manufacturing process for lightweight automotive seats is met through design optimization of seat back, manufacturing of subcomponents, mechanical characterization, and validation through numerical simulations. The routes to achieve the final goal of the project and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. The project resulted in the development of a novel manufacturing process to integrate metal inserts with tailored polymer composite preforms through overmolding. Leveraging this proven manufacturing process, a lightweight seat back was designed through topology optimization and numerical simulations. The designed seat back uses AM metal inserts and compression overmolding of tailored polymer composite preforms obtained via large scale additive manufacturing. The metal polymer composite structures fabricated through this process exhibited enhancement in stiffness and improved ductility upon testing. Overall, the project provided an alternative design and manufacturing technique for automotive seat back that enables weight saving while meeting the safety and performance requirements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Lightweight, Flexible Electromagnetic Shielding Composite Films Reinforced with Recycled Carbon Fibers and Carbon Nanofillers

Lightweight, flexible polymer composites are widely adopted in modern electronic devices and systems for high-efficiency electromagnetic interference (EMI) shielding. Reinforcing polymer composites with conductive fillers offers a promising alternative to conventional metal-based shielding material thanks to their low density, tunable properties, and flexibility. Herein, lightweight, flexible ultra-high molecular weight polyethylene composite films reinforced with recycled carbon fibers (rCFs) and carbon-based nanofillers, including graphene nanoplatelets (GNPs) and carbon nanotubes, are reported for EMI shielding applications. The incorporation of these nanofillers significantly reduces the required content of rCFs and improves processability while maintaining comparable shielding effectiveness. The addition of these nanofillers with rCFs enhances the EMI shielding effectiveness by up to 15 dB. Incorporating 1 wt% GNPs can replace 5 wt% rCFs and achieve comparable EMI shielding performance, while 5 wt% of either nanofillers can substitute for 10 wt% rCFs. Numerical modeling of electromagnetic wave transmission reveals that increasing nanofiller concentrations enhances both reflection and absorption losses, with absorption consistently dominating across all levels. Furthermore, this study not only provides insight into the synergistic contributions of rCFs and carbon nanofillers to shielding effectiveness but also paves the way for the design of sustainable, lightweight EMI shielding composite films for applications in electric vehicles, medical equipment, and portable electronics.

carbon nanofiller↗

DOE OSTI2024

Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites

With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. Here, this study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.

36 MATERIALS SCIENCE↗

DOE OSTI2025

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

large convolution kernel↗

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats: Integration of Additive Manufacturing and Reinforced Polymer Composite

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

MAT 329: Cost Efficient, Bio-Derivable, and Lightweight Materials for Vehicle's Thermal Energy Management

Objective: Enable lightweight and multifunctional composites by combining CF composites with encapsulated phase-change materials for temperature regulation. Impacts: The multifunctionality of the composites provides benefits over today's aluminum enclosures which could help drive CF composites adoption; and By implementing CF composites, battery enclosures can be lightweighted leading to a greater vehicle efficiency.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Lattice Structured Lightweight Structural Materials

The development of lightweight structural materials is crucial for enhancing the performance and deployment feasibility of fission batteries. This study aims to produce lightweight structural materials whose strength-to-weight ratios exceed those of current widely used structural materials. To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structured models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The modeling results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison to the solid material; the increase in solid shell thickness and blend radius enhances the mechanical performance; and the effect of unit cell size on macro scale stress is minimal. Tensile testing was conducted on the solid and lattice-structured materials fabricated by laser powder bed fusion (LPBF) additive manufacturing. The experimental results agreed well with the model prediction. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for new material development.

lightweight material↗

DOE OSTI2025

Design of lightweight BCC multi-principal element alloys with enhanced hydrogen storage using a machine learning-driven genetic algorithm

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage. In this work, we first leverage machine learning (ML) models to predict the hydrogen affinity, storage capacity and phase stability of BCC MPEAs, creating a unique hydrogen-to-metal (H/M) predictor for materials with unprecedented performance. We developed a metaheuristic optimizer high-throughput framework by interfacing ML models with a genetic algorithm for the accelerated search of {Mg, Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Nb, Mo} based lightweight BCC MPEAs with improved hydrogen storage characteristics. We report five new MPEAs with a predicted gravimetric hydrogen storage capacity of around 3.5 wt% or more, including Cr 0.09 Mg 0.73 Ti 0.18 (4.25 wt% H) and Cr 0.21 Nb 0.11 Ti 0.35 V 0.33 (3.5 wt% H). The electronic structure of the top-performing composition, Cr 0.09 Mg 0.73 Ti 0.18 , was analyzed using density functional theory (DFT) to understand the reasons for its improved hydrogen storage properties compared to TiFe (1.90 wt% H), LaNi 5 (1.37 wt% H) or BCC MPEAs like TiVNbCr (3.70 wt% H). Temperature-dependent molecular dynamics (MD) studies were further performed on optimized BCC MPEAs to qualitatively study hydrogen mobility and analyze the effect of different elemental composition on bulk hydrogen diffusion. Our findings demonstrate how a ML assisted genetic algorithm framework can be used for efficient search of stable, lightweight and cost-effective MPEAs while minimizing the need for expensive ab initio calculations.

DFT↗

DOE OSTI2024

Enhancing corrosion resistance of lightweight metal alloys through laser shock peening

In this study, we investigated the effects of laser shock peening (LSP) on the corrosion resistance of lightweight metal alloys, specifically AA6061 and AZ31. LSP was performed underwater, using a nanosecond pulse laser and without using a protective coating or layer on the workpiece. The corrosion behaviors of these alloys were analyzed through electrochemical tests, including open circuit potential, electrochemical impedance spectroscopy, and potentiodynamic polarization measurements. The results demonstrated that LSP significantly improved the polarization resistance, and higher laser power intensities led to increased corrosion resistance and reduced corrosion rates. This enhancement in anti-corrosion performance is attributed to the formation of a protective oxide layer on the surface, acting as a barrier against corrosion. In conclusion, the findings underscore the potential of laser surface treatment as a viable technique for enhancing the corrosion resistance of lightweight metal alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Thermal shock resistance of lightweight cements developed for geothermal conditions

Cements are a critical component in well construction, as they act to prevent well fluid and gas escape, prevent corrosion of the casing, and strengthen the wellbore to prevent deformation. Under the high temperature/pressure conditions common in geothermal systems, the injection of cold water for energy production is expected to induce cyclic damage to the borehole cement through the rapid temperature fluctuations. These “thermal shocks” are expected to cause casing shrinkage, annulus formation, and cement tensile stresses. To understand the effect of cold water injection on the wellbore environment, a set of rock-cement-steel samples were created to simulate the structure of a geothermal well. Lightweight thermally-insulating cement blends were tested under thermal shock conditions in this study. In each test, the samples were pressurized to an effective pressure of ~3.5 MPa and placed at high temperatures. Thermal shocks were performed by injecting cold water (~10-15 °C) through the samples at a constant rate while keeping the samples at high temperatures until the sample temperature stopped decreasing and deformation ceased. Eight thermal shock tests were conducted with each sample – two at 100 °C and six at 200 °C. Post-tests analysis was then conducted by cutting open each sample to examine the damage in each component of the simulated wellbore. Experimental results suggest that all samples experienced similar degrees of axial and lateral contraction during cold water injection, but for the most part this contraction is recoverable when injection halts. Post-test analysis revealed that fly ash cenosphere pre-treatment had the best effect on improving thermal shock resistance in the cement blends. Thermomechanical modeling of likely stress paths experienced by the cements during heating/cooling cycles shows that elasto-plastic cement constitutive behavior results in most plastic strain occurring during the initial heating steps, with mostly elastic strain occurring during the thermal shock cycles. In conclusion, this agrees with experimental evidence, suggesting that cement damage from shocking occurs via other mechanisms such as chemical alteration, corrosion, and fatigue.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Smart manufacturing approach to manufacture bulk nanocrystalline aluminum for lightweight applications

In this research, a smart manufacturing approach was used to enhance the mechanical properties of aluminum (Al) for lightweight applications. The smart manufacturing involved cryomilling of Al powders with and without 5 wt.% magnesium (Mg) powders for varying durations followed by a high-pressure cold spray (HPCS) additive manufacturing process to prepare bulk components. The morphological changes, crystallite size, and composition of the cryomilled powders and cold sprayed (CS’ed) components were examined using scanning electron microscopy (SEM), x-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The results showed that the crystallite size reduces with an increase in cryomilling time and the addition of Mg dopant. To test the mechanical properties of the bulk CS’ed components, microhardness tests were performed using a Vickers microhardness tester. Uniaxial tensile tests were also carried out to ascertain the material’s tensile properties. The mechanical testing results showed great improvement in the hardness and tensile strength of CS’ed Al–Mg samples as compared to pure Al samples. Subsequently, fractography analysis of the tensile failed samples was carried out to determine the nature of the failure. Here, the research article also discusses the inherent mechanisms for the improvement in mechanical properties of smart manufactured components as a result of Mg doping and cryomilling.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Doping Effects on the Ductility of a Lightweight Refractory High-Entropy Alloy: Grain Boundary and Bulk Lattice Aspects

Doping elements in small amounts often segregate to grain boundaries (GBs) in alloys and can significantly impact mechanical properties and performance. Refractory high-entropy alloys (RHEAs) are known for their poor ductility, especially at low temperatures. Promoting GB cohesion through segregation can be an effective approach to mitigate embrittlement. Here, in this study, first-principles density functional theory (DFT) calculations were performed to examine the effects of important interstitial dopants (O, B, C, and N) and substitutional dopants (Cr, Y, La, and Ce) on the Σ5(310) [001] tilt GB of a lightweight RHEA Nb 32.5 Ti 27.5 Mo 22.5 Ta 12.5 Hf 2.5 Zr 2.5 . The DFT calculations reveal that certain dopants, such as B, C, Cr, N, and Y, exhibit favorable GB strengthening effects by improving bonding interactions with the bulk alloy. The impact of doping on the ductility parameter of the bulk lattice, defined as the ratio of surface energy to unstable stacking fault energy for the {110} <111> slip system, was also studied; and the results show that doping reduces the intrinsic ductility of the alloy, decreasing the D-parameter from 2.90 to 2.55, depending on the specific dopant. The present findings provide a foundational understanding at atomic level of the effect of representative dopants on mechanical properties of RHEAs and can be used to guide future alloy design for improved mechanical properties.

deformation charge density↗

DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗

DOE OSTI2025

PET-FBA: A lightweight enzyme allocation and thermodynamics-constrained flux analysis approach to explore Escherichia coli metabolic adaptation to intracellular acidification

Escherichia coli employs diverse strategies to adapt to acidic environments that disrupt enzyme activity and the thermodynamic feasibility of essential reactions. To understand the impact of pH stress on cell metabolism, we present the PET-FBA (pH-, Enzyme protein allocation-, and Thermodynamics-constrained Flux Balance Analysis) framework. PET-FBA extends genome-scale modeling by integrating enzyme protein costs and reaction Gibbs free energy changes. Additionally, by incorporating pH-dependent enzyme kinetics in response to intracellular acidification, this framework enables the simulation of E. coli's metabolic adjustments across varying external pH levels. The model's accuracy is validated by comparing in silico growth simulations with experimental measurements under both anaerobic and aerobic conditions, as well as in silico gene knockouts of essential genes. By explicitly incorporating pH effects, our model accurately replicates the metabolic shift towards lactate production as the primary fermentation product at low pH in anaerobic conditions. This shift is only predicted when enzyme kinetics are dynamically adjusted as a function of pH. Further analysis revealed that this shift can be attributed to the reduced protein efficiency of the acetyl-CoA branch compared to lactate dehydrogenase under acidic stress, which then becomes crucial for maintaining NAD regeneration and cell growth at low pH. Furthermore, we identified strategies for enhancing cell growth under acidic anaerobic conditions by improving the enzyme activity of lactate dehydrogenase and pyruvate formate lyase, which increases NAD production efficiency and reduces enzyme protein allocation costs. Designed as a lightweight yet versatile framework, PET-FBA enables efficient genome-scale metabolic analysis. Using E. coli as a model system, our framework provides a systematic approach to understanding metabolic responses to environmental stress, pinpointing key metabolic bottlenecks, and identifying potential targets for strain optimization.

42 ENGINEERING↗

DOE OSTI2024

Lightweight single-phase Al-based complex concentrated alloy with high specific strength

Developing light yet strong aluminum (Al)-based alloys has been attracting unremitting efforts due to the soaring demand for energy-efficient structural materials. However, this endeavor is impeded by the limited solubility of other lighter components in Al. Here, we propose to surmount this challenge by converting multiple brittle phases into a ductile solid solution in Al-based complex concentrated alloys (CCA) by applying high pressure and temperature. We successfully develop a face-centered cubic single-phase Al-based CCA, Al 55 Mg 35 Li 5 Zn 5 , with a low density of 2.40 g/cm 3 and a high specific yield strength of 344×10 3 N·m/kg (typically ~ 200×10 3 N·m/kg in conventional Al-based alloys). Our analysis reveals that formation of the single-phase CCA can be attributed to the decreased difference in atomic size and electronegativity between the solute elements and Al under high pressure, as well as the synergistic high entropy effect caused by high temperature and high pressure. The increase in strength originates mainly from high solid solution and nanoscale chemical fluctuations. Our findings could offer a viable route to explore lightweight single-phase CCAs in a vast composition-temperature-pressure space with enhanced mechanical properties.

42 ENGINEERING↗

DOE OSTI2025

Enabling Next Generation Reaction Injection Molding (RIM) for Lightweight Structures

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this project, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. This optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption. This research has been performed in collaborations with McClarin Composites. The research outcome has been submitted to the Journal of Manufacturing Processes.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Highly Recyclable Thermosets for Lightweight Composites

The objective of this project, Highly Recyclable Thermosets for Lightweight Composites (DOE Award DE-EE0009297), was to develop recyclable carbon fiber–reinforced polymer (CFRP) composites that are more energy efficient to produce than existing technologies while achieving superior mechanical performance and enabling closed-loop material recovery. Specifically, the project targeted vitrimer-based composites with tensile strength at least 20% higher than baseline recyclable polypropylene composites, retention of greater than 95% of tensile strength after multiple recycling and reprocessing cycles, recovery of carbonate monomers through depolymerization, and recovery of greater than 95% of carbon fibers of reusable quality. The project was carried out by The University of Akron in collaboration with Pacific Northwest National Laboratory and Raytheon Technologies Research Center.

36 MATERIALS SCIENCE↗