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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SEEMS: A Single Event Effects and Muon Spectroscopy facility at the Spallation Neutron Source

This study outlines a concept that would leverage the existing proton accelerator at the Spallation Neutron Source (SNS) of Oak Ridge National Laboratory to enable transformative science via one world-class facility serving two missions: Single Event Effects (SEE) and Muon Spectroscopy (μSR). The μSR portion would deliver the world’s highest flux and highest resolution pulsed muon beams for material characterization purposes, with precision and capabilities well beyond comparable facilities. The SEE capabilities deliver neutron, proton, and muon beams for aerospace industries that are facing an impending challenge to certify equipment for safe and reliable behavior under bombardment from atmospheric radiation originating from cosmic and solar rays. With negligible impact on the primary neutron scattering mission of the SNS, the proposed facility will have enormous benefits for both science and industry. Herein, we have designated this facility “SEEMS.”

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Mild hybridisation of turboprop engine with high-power-density integrated electric drives

Here this paper shares with the aerospace community a case study of turboprop mild hybridisation using a recently developed integrated drive system in the University of Nottingham, UK, within the ACHIEVE project under EU H2020 CleanSky 2 program (project No. 737814). The developed drive system enables green taxiing of a turboprop aircraft while on the ground with its engine off, and as an electrical generator when the turboprop is in the air. The entire system is designed to be able to integrate within the power auxiliary gear box (PAGB) of a turboprop aircraft. Some of the key features of the developed system include a high-speed permanent magnet machine (up to 14,200rpm) with dual three-phase design, SiC-based high power density (11.8kW/L for the power converter, 35.3kW/L and 7.2kW/kg for the machine active parts), integrated cooling design for high-temperature operation (>130°C ambient temperature), fault tolerance consideration with dual channel operation capabilities and sensorless control for entire operational conditions. This paper is giving an overview of the design process of the electrical machine, power converters, and its cooling of the entire drive. Numerical analysis (FEM and CFD) and some experimental results are presented to demonstrate the effectiveness and the desired performance of the developed integrated drive system.

42 ENGINEERING↗
DOE OSTI2021

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, we report an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

42 ENGINEERING↗
DOE OSTI2021

Development of a Castable High Strength Secondary Aluminum Alloy from Recycled Wrought Aluminum Scrap

A new process for recycling scrap AA7075 aerospace alloys directly into a high strength castable secondary alloy is demonstrated. The process involves enhancing the castability of the utilized primary wrought aluminum scrap feedstock by scrap alloy chemistry optimization and zirconium additions in order to mitigate the inherent hot tearing tendency of 7075 alloys. By utilizing the new process, the hot tearing index of scrap AA7075 was reduced from 26 to 4. Defect-free castings were obtained in pilot-scale castings trials. The developed secondary 7075 alloy also satisfied preset target mechanical properties with strength levels exceeding 250 MPa and elongation levels higher than 3%. The validity of the new approach and its readiness for transition to commercial practice was demonstrated by casting cylinder heads directly from the modified 7075 scrap.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Efficient Silicon Carbide (SiC) Fiber Manufacture: Continuous Processing of Novel Precursors via Modified Material Handling

General Atomics Electromagnetic Systems (GA-EMS) together with The Nonwovens Institute (NWI) and Harper International have executed on a development project to improve the production efficiency and reduce the production cost of silicon carbide (SiC) fiber – a critical material that advances US energy security and aerospace leadership. In the first budget period (BP1), the modified tow throughput (MTT) apparatus was developed, enabling fiber spinning in the form of loose coils rather than tightly packed spools (Figure 1) for batch processing of SiC fiber. This coil-based approach was proven to be capable of uniform fiber crosslinking and ceramic conversion, as well as demonstrating key process efficiencies while meeting the program fiber property targets.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Design, Fabrication and On-Sun Performance Evaluation of SiC Receiver Feature Specimens for CST Applications Using Additively Manufactured SiC Materials

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Successful development of CST systems to generate heat for industrial applications requires significant increase in temperature capability and techno-economically viability of the receiver systems. Supported by an award from the Solar Technology Office (SETO), US Department of Energy (DOE), GE Aerospace Research in collaboration with Heliogen Holdings Inc and Sandia National Lab, is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. The program objective is to demonstrate SiC receiver with air exit temperatures up to 1100 oC and high thermal efficiencies. We report design, fabrication and on-sun test results of SiC components of a prototype 50kW (thermal) HOTSSTAR module. The receiver module architecture is based on a radial airflow design and consists of a series of radial SiC receiver sectors organized around a SiC center absorber. The SiC components are fabricated using binder-jet printed SiC followed by melt-infiltration reaction bonding process. To enhance the reliability and to minimize the risk of cracking damage of SiC test articles in thermal gradient and thermal shock environment of the application, the components were laminated with GE’s MI SiC-SiC CMC. Following extensive design and lab test analyses, selected SiC component designs are tested at Heliogen Lancaster Solar field under highly concentrated solar fluxes around 2000 suns to assess the thermal performance characteristics under realistic field conditions. We report on the test results and compare the thermal performances of different HOTSSTAR SiC absorbers. Finally, we discuss fabrication and initial assembly of a 50kW prototype test module in preparation of on-sun field tests to assess the performance of our final design.

CST, CSP, high temperature, air receiver, SiC, add↗
DOE OSTI2026

Performance Evaluation of an Additively Manufactured ultra-High Operating Temperature SiC Solar-Thermal Air Receiver (HOTSSTAR) Test Module

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal and solar power systems. GE Aerospace Research in collaboration with Heliogen Inc and Sandia National Laboratories (SNL) is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. HOTSSTAR goal is to demonstrate SiC receiver with air exit temperatures up to 1100oC. We discuss fabrication and on-sun test results of a prototype 50kWth test module. The receiver architecture is based on a radial airflow design and consists of a series of radial SiC receiver sectors organized around central absorber. These components were fabricated using binder-jet printed SiC followed by melt-infiltration reaction bonding process. To enhance the thermo-mechanical reliability of SiC test articles in thermal gradient/ shock environment of the application, the components were laminated with GE’s MI SiC CMC. A dedicated test facility was constructed at SNL Solar Tower to evaluate the operational performance of HOTSSTAR module under solar fluxes >200 W/cm2. We report on our final 50kW test module fabrication, integration at the test facility at Sandia, and discuss on-sun test results. We compare the performance of HOTSSTAR module relative to our model predictions.

14 SOLAR ENERGY↗
DOE OSTI2020

Aerodynamic Data Fusion Toward the Digital Twin Paradigm

This paper considers the fusion of two aerodynamic data sets originating from differing types of physical or computer experiments. This paper specifically addresses the fusion of 1) noisy and in-complete fields from wind-tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fused in order to estimate the true field that best matches measured quantities that serve as the ground truth. For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment. A fundamental challenge in this problem is that the true field is unknown and cannot be estimated with 100% certainty. A Bayesian framework is employed to infer the true fields conditioned on measured quantities of interest; essentially a statistical correction to the data is performed. The fused data may then be used to construct more accurate surrogate models suitable for early stages of aerospace design. An extension of the proper orthogonal decomposition with constraints is also introduced to solve the same problem. In this work, both methods are demonstrated on fusing the pressure distributions for flow past the RAE2822 airfoil and the Common Research Model wing at transonic conditions. Comparison of both methods reveals that the Bayesian method is more robust when data are scarce and capable of also accounting for uncertainties in the data. Furthermore, given adequate data, the proper-orthogonal-decomposition-based and Bayesian approaches lead to surprisingly similar results.

42 ENGINEERING↗
DOE OSTI2020

Determining Compression Characteristics of Honeycomb Material - 19674

The objective is to determine compression test characteristics of stainless steel honeycomb material to be able to represent honeycomb structures accurately in analytical models used to simulate hypothetical accident scenarios of shipping packages. Honeycomb is a material used primary in the aerospace industry due to its high strength-to-weight ratio. Because it doesn't have a shelf life and can withstand high heats, it is an excellent candidate for a structural material in package designs. Honeycomb has orthotropic material properties. The honeycomb currently being evaluated is made of metal ribbons spot welded together to form hexagon pattern between metal plates. The hexagon ribbons are brazed to the metal plates. The complexity of the honeycomb significantly lengthens the simulation time, which is further complicated when the brazing, welding, and imperfections of the material is considered. This means to affectively represent the honeycomb in simulation programs, such as Abaqus, the material needs to be approximated as a uniform orthotropic material. This requires material properties in each of the three directions, T, W, and L. The T direction is defined as the 'strong' direction, perpendicular to the honeycomb sheet. The L direction is parallel the ribbon and the W direction is perpendicular to the ribbon. Honeycomb has 3 different compression stages. Stage 1 is the initial compression where the honeycomb maintains its structural integrity and does not permanently deform from loads from normal operations. Stage 2 is when the initial buckling causes irreversible damage to the honeycomb. Stage 2 is the one we are most interested in because it absorbs the most energy from a hypothetical accident scenario. During Stage 2, the honeycomb fails layer-by-layer, indicating that the more layers, the longer material crushing is sustained, and thus the more energy that is absorbed. Stage 3 is final compression, similar to compressing solid metal. During stage 3 the stress increases with a diminishing rate of elongation and the honeycomb is completely failed where the plates between the honeycomb core sandwich the crushed honeycomb ribbon. The stress-strain graph bellow shows compression test of 3 different samples in the T direction. The red lines divide the three stages. The far left is Stage 1, the middle stage 2, and the right stage 3. The data collected is displayed in the table below. The expressions in the table represent the regression formula that represents each stage of the compression on a stress-strain graph. Stage 1 is assumed to intersect with the origin; data for stage 3 for the crush in W and L directions where unable to be gathered due to the nature of failure for those directions. Stage 1 and 2 are linear regressions while stage 3 is a degree 2, to best match the curve. Linear regression is used on stage 2 because ultimately that represents the energy absorbed. With more data, a degree n x 2 regression would be a more appropriate regression for stage 2, where n is the number of layers. The table below is the approximation of each scenario and stage. Each stage starts and ends at the intersection of the next stage. There where two main objectives for these tests: firstly to understand how honeycomb performs under extreme compression, and secondly to be able to numerically represent the honeycomb structure. Both of these where completed to varying degrees. It is important understood how the honeycomb fails. If it is crushed in the T direction it retains its integrity even after being crushed, however when crushed in the L and W crush direction, if it fails, it disintegrates, and loses all integrity. Also, the more layers the more time the material spends in stage 2. Failure is started by buckling, thus if there is any imperfection, the stress will not spike but transition straight into stage 2. The data collected and aggregated can be used for initial simulation of honeycomb used in packages. The initial testing has set the ground work for more data to be collected in order to verify results and to allow more confidence in the simulation results. This is only the initial data collected. The next steps is to continue to collect more data to verify results and to test more variants of honeycomb, with different brazing, layers, and shape.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chapter 8: Life-Cycle Testing and Analysis

Prior to a spacecraft launch, program mission assurance standards dictate that the flight battery power system should comply with mission requirements under the intended operating conditions. Ground life cycle testing (LCT) combined with an analysis on the electrical power system (EPS) battery is an empirical method used to demonstrate compliance to satellite service life requirements. The LCT compliance method adopted by the aerospace industry is based on demonstrating a space battery's life expectancy as a part of battery qualification. Real-time cell and lithium-ion battery (LIB) LCT data are commonly used for model inputs to EPS power and energy balance analyses, in LIB reliability analysis estimates, and to support on-orbit spacecraft mission life extension opportunities. This chapter describes the LCT planning steps, process approach, and analysis techniques commonly used to qualify space LIB power systems.

accelerated aging↗
DOE OSTI2023

Aerosol-Jet Printed Sensors for Environmental, Safety, and Health Monitoring: A Review

An emergent direct-write approach, aerosol-jet printing (AJP), is gaining attention for the deployment of rapid and affordable microadditively manufactured energy-efficient sensors and printed electronics. AJP enables a broad range of ink viscosities (0.001–1 Pa s) for printing diverse materials ranging from ceramics and metals to polymers and biological matter. Reproducible, high-spatial-resolution features (≈10 µm), and wide standoff distances (1–11 mm) between the nozzle and the substrate facilitate conformal printing of complex geometrical designs on nonplanar—e.g., stepped or curved—surfaces. Here this paper aims to provide a comprehensive overview of state-of-the-art AJP-based sensors (e.g., strain and temperature gauges, biosensors, photosensors, humidity and surface acoustic wave sensors, dielectric elastomer actuators, and motion, smoke, and hazardous gas detectors) and to discuss prospective applications. The drive toward cost-effective devices that are smaller, lighter, and better-performing remains a frontier challenge in the field of printed electronics. Consequently, as AJP becomes increasingly utilized in the high-volume manufacturing of miniaturized active and passive sensors, it opens a pathway for facile large-scale fabrication of devices for a wide range of consumer and industrial applications, including transportation, agriculture, infrastructure, aerospace, national defense, and healthcare.

3-dimensional↗
DOE OSTI2025

Estimating the State of Charge in Lithium Primary Batteries: Recent Advances and Critical Insights

Lithium primary batteries (LPBs) remain essential in critical applications such as military, aerospace, medical and emergency devices, and portable electronics. Their superior energy density over lithium-ion batteries offers a significant advantage for long-duration use. Therefore, accurate estimation of the state of charge (SoC) is essential for ensuring the reliable and safe operation of these batteries. While extensive research has been conducted on SoC estimation techniques for lithium-ion secondary batteries, LPBs present unique challenges that complicate accurate SoC estimation. Moreover, research on nondestructive testing techniques for SoC estimation in LPBs is significantly lacking. In this review article, it is aimed to provide a comprehensive overview of recent advancements in SoC estimation for LPBs and generates new insights and directions for future research. Herein, existing methods are discussed and their effectiveness and mechanisms are identified, and areas for further optimization are outlined. More theoretical/experimental efforts to advance SoC detection in LPBs is recommended due to challenges identified with existing techniques.

25 ENERGY STORAGE↗
DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data

Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An Ultra-microporous Metal-Organic Framework with Exceptional Xe Capacity

Molecular confinement plays a significant effect on trapped gas and solvent molecules. A fundamental understanding of gas adsorption within the porous confinement provides information necessary to design a material with improved selectivity. In this regard, metal organic frameworks (MOFs) adsorbents are ideal candidate materials to study confinement effects for weakly interacting gas molecules such as noble gases. Among the noble gases, Xenon (Xe) has practical applications in the medical, automotive and aerospace industries. In this communication, we report an ultra-microporous nickel-isonicotinate MOF with exceptional Xe uptake and selectivity compared to all benchmark MOF and porous organic cage materials. The selectivity arises because of the near perfect fit of the atomic Xe inside the porous confinement. Notably, at low partial pressure, the Ni-MOF interacts very strongly with Xe compared to the closely related Krypton gas (Kr) and more polarizable CO2. Further 129Xe NMR suggests a broad isotropic chemical shift due to the reduced motion as a result of confinement.

Confinement effects, atomically precise materials,↗
DOE OSTI2025

Solventless Dual‐Cure Liquid Resins Via Circular Use of Phthalic Anhydride for Recyclable Composite Applications

Abstract Fiber‐reinforced composites (FRCs) possess a remarkable strength‐to‐weight ratio, making them ideal light‐weighing alternative materials of metals used in automotive, aerospace, and outdoor equipment applications, but their recycling is challenging. Chemically recyclable thermoset polymers can enable fiber recovery and reuse; however, challenges remain in the separation and purification of depolymerized small molecules for efficient polymer recycling. To this end, a series of liquid resins for chemically recyclable polymer networks is designed based on phthalic anhydride, a widely produced and inexpensive chemical. The straightforward sublimation of phthalic anhydride is leveraged to enable a simple and efficient separation process for polymer recycling. To liquefy phthalic anhydride, five mono‐acryloyl‐phthalates are synthesized to obtain stable liquid resins together with phthalic diglycidyl ester. These liquid resins undergo dual‐cure reactions that comprise photopolymerization of acrylate and, subsequently, heat‐mediated epoxy‐acid polymerization reactions. These liquid resins exhibit moderate viscosities (2600–6400 cP @ 22 °C), fast curing, and robust thermomechanical properties (T g s from 71 to 116 °C). It is demonstrated that hydrolysis of the dual‐cured polymers completes within 2 h at 80 °C, and direct sublimation produces phthalic anhydride with 82% yield. This resin system is expected to provide a cost‐competitive, highly efficient platform for recyclable FRCs.

Polymer Science↗
DOE OSTI2021

Improve durability and surface quality of additively manufactured molds using carbon fiber prepreg

Hybrid tooling is an emerging concept introduced in the aerospace industries to reduce weight and cost of traditional tools. A hybrid tool features a skin, which provides desired surface quality, durability, and a low-density substrate to reduce the weight of the mold. The big area additive manufacturing (BAAM) technology permits rapid production of thermoplastic polymer intensive large-scale structures. The present study features a carbon fiber reinforced polyphenylene sulfide (CF-PPS) substrate fabricated using Oak Ridge National Laboratory's BAAM system. Carbon fiber-bismaleimide (CF-BMI) prepreg skin was then bonded to the BAAM tool through high-pressure autoclave molding. Process optimization was conducted to improve the bonding between CF-PPS and CF-BMI (transverse tensile strength increased from 0.54 to 4.8 MPa). Durability of the mold was demonstrated from fabricating seven (7) carbon fiber-Huntsman epoxy hand lay-up parts utilizing the mold.

36 MATERIALS SCIENCE↗