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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Intelligent Manufacturing for Extreme Environments Conference Proceedings

The Intelligent Manufacturing for Extreme Environments (IMEE) workshop was held at the Center for Advanced Energy Studies (CAES) in Idaho Falls, Idaho, May 2–3, 2023, in support of the United States (U.S.) National Science Foundation (NSF) Established Program to Stimulate Competitive Research: Workshop Opportunities (EPSCoR-WO) program. This workshop featured keynote speakers, panels, and breakout sessions with 58 participants. Nuclear reactors need to operate under extreme service conditions, such as high temperatures, corrosive environments, and high-radiation doses. Hence, reactor components must be able to withstand those conditions. The participants envision a future where on demand manufacture of components for small modular reactors (SMRs), microreactors, and other advanced reactor designs are possible. In this future, regulatory bodies accept validated manufacturing processes and standardized feedstocks, thus eliminating the need for individual component testing. However, the necessary technologies and regulatory policies needed for this future do not exist today. Successful innovation would revolutionize the nuclear power sector, enable fast commercial development, create economic opportunities in the U.S., reduce the carbon footprint and associated risks, and promote a skilled and highly competitive workforce. The objective of the workshop was to convene world-class experts, researchers, educators, and students to identify gaps and envision solutions for five interrelated challenges for intelligent manufacturing in extreme environments. The key outcomes of the conference were: (1) to take the opportunity for researchers and educators to network and form collaborations; and (2) to produce a full report to inform policy-makers, industry, and the academic community of various challenges and opportunities in the nuclear energy sector.

36 MATERIALS SCIENCE↗
NASA NTRS1992

SDIO Producibility and Manufacturing Intelligent Processing Programs

SDIO has to fashion a comprehensive strategy to insert the capability of an industrial base into ongoing design tradeoffs. This means that there is not only a need to determine if something can be made to the precision needed to meet system performance, but also what changes need to be made in that industry sector to develop a deterministic approach to fabrication precision components. Developing and introducing advanced production and quality control systems is part of this success. To address this situation, SDIO has developed the MODIL (Manufacturing Operations Development and Integration Labs) program. MODILs were developed into three areas: Survivable Optics, Electronics and Sensors, and Spacecraft Fabrication and Test.

Stottlemyer, Greg↗
DOE OSTI2020

Industry 4.0 and Intelligent Manufacturing Processes: A Review of Modern Sensing Technologies

Digital Manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create a more lean, profitable, and data-driven manufacturing processes. In this paper, we review modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to Cyber-Physical Systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations.

42 ENGINEERING↗
DOE OSTI2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗
DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗
DOE OSTI2022

National Smart Manufacturing Strategic Plan: To Facilitate More Rapid Development, Deployment and Adoption of Smart Manufacturing Technologies

Smart manufacturing technologies provide real-time data and insight to improve the productivity, efficiency, and competitiveness of U.S. manufacturing, creating the potential for new jobs in the manufacturing sector. These technologies can support U.S. manufacturers’ ability to increase throughput and energy efficiency, and decrease waste, defects, and costs. A wide range of manufacturing and industrial subsectors, particularly energy-intensive and energy-dependent industries, have the potential to benefit from smart manufacturing technologies. There are technical improvements still needed to reduce the costs and barriers (trained workforce and upskilling, software-hardware integration, cost, and technical barriers to deployment of advanced sensors, computing, and communication technologies for existing manufacturing assets) to the adoption of smart manufacturing technologies, especially to small and medium-sized manufacturers, and subsequently, increase the overall adoption rate of smart manufacturing technologies. This report outlines the Department of Energy’s (DOE) strategic plan to accelerate the development and implementation of smart manufacturing technologies in the United States and the actions DOE has taken to use these technologies in smart manufacturing for the United States. DOE’s plan to facilitate more rapid development, deployment and widespread adoption of smart manufacturing technologies derive from the 2018 Strategy for American Leadership in Advanced Manufacturing. The strategy outlines a vision for America’s leadership in advanced manufacturing including the development of intelligent manufacturing systems to optimize manufacturing facilities and support the manufacturing of transformative materials. DOE’s Clean Energy Smart Manufacturing Innovation Institute (CESMII), a Manufacturing USA Institute, focuses on accelerating the development and adoption of advanced sensors, controls, platforms, and models needed for smart manufacturing. The objective is to enhance U.S. manufacturing productivity and global competitiveness through the research and development of these technologies. Smart manufacturing has the potential to improve the overall performance, energy productivity, and efficiency of manufacturing, fostering the economic competitiveness of the manufacturing sector.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2005

Rocket Testing and Integrated System Health Management

Integrated System Health Management (ISHM) describes a set of system capabilities that in aggregate perform: determination of condition for each system element, detection of anomalies, diagnosis of causes for anomalies, and prognostics for future anomalies and system behavior. The ISHM should also provide operators with situational awareness of the system by integrating contextual and timely data, information, and knowledge (DIaK) as needed. ISHM capabilities can be implemented using a variety of technologies and tools. This chapter provides an overview of ISHM contributing technologies and describes in further detail a novel implementation architecture along with associated taxonomy, ontology, and standards. The operational ISHM testbed is based on a subsystem of a rocket engine test stand. Such test stands contain many elements that are common to manufacturing systems, and thereby serve to illustrate the potential benefits and methodologies of the ISHM approach for intelligent manufacturing.

Figueroa, Fernando↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Functional Nano-to-Microstructures by Jet Printing and Direct Ink Writing

Jet-based printing techniques and direct ink writing have emerged as complementary, convergent technologies serving as key platforms in additive manufacturing for functional nano- to microscale architectures. This review highlights how these approaches enable fine feature resolution and three-dimensional structures in advanced electronics and biointerfacing applications. The interplay of fluid mechanics, viscoelastic ink rheology, droplet–substrate interactions, and drying dynamics is examined as a critical determinant of printing fidelity. Application-focused case studies, from flexible thin-film transistors to bioprinted artificial tissues, demonstrate how precise structural control via printing translates to enhanced device performance and new functionality in electronic and biological systems. Finally, we discuss the challenges and future opportunities driving the evolution of these printing platforms toward autonomous, adaptive, and intelligent manufacturing systems.

Luo, Junchen [Sichuan Univ. of Arts and Science (C↗
DOE OSTI2026

Carbon-Based Quantum Information Science with Symmetry Protected Topological States (Final Report, DOE-BES award DE-SC0023105)

This research program established the scientific foundation for the rational, bottom-up design, synthesis, isolation, and investigation of symmetry-protected topological (SPT) electron spin qubits embedded in graphene nanoribbons (GNRs). The work focused on integrating atomically precise low-dimensional carbon nanostructures with emerging quantum logic architectures, providing a pathway toward scalable quantum materials for next-generation computing and sensing technologies. A central component of the program was the elucidation of fundamental relationships between real-space molecular architecture, local spin density distributions, electronic band dispersion, and energy level alignment in atomically precise GNR systems. These correlations define key operational parameters of SPT qubits and were systematically investigated to establish quantitative benchmarks against established molecular and solid-state spin qubit platforms. Attention was given to properties critical for quantum device performance, e.g. decoherence times, spectral sharpness of energy transitions, and tunable exchange interactions between spin states. The research demonstrated that these parameters can be engineered with atomic precision through scalable bottom-up synthetic strategies. Theory-guided design played a central role in identifying candidate structures hosting topologically protected spin states. Experimental validation was performed using both ensemble measurements and single-molecule characterization. In addition to advances in quantum materials synthesis, the program developed and applied spin-sensitive scanning probe microscopy techniques capable of directly probing quantum states and dynamic processes with atomic-scale spatial resolution. These capabilities enabled direct observation and characterization of quantum structures at the single-atom level. While the research activities were primarily hypothesis-driven fundamental investigations, the program adopted a comprehensive materials-by-design framework aimed at translating scientific discoveries into technological concepts compatible with scalable and intelligent manufacturing approaches.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Research on conceptual/innovative design for the life cycle

The goal of this research is developing and integrating qualitative and quantitative methods for life cycle design. The definition of the problem includes formal computer-based methods limited to final detailing stages of design; CAD data bases do not capture design intent or design history; and life cycle issues were ignored during early stages of design. Viewgraphs outline research in conceptual design; the SYMON (SYmbolic MONotonicity analyzer) algorithm; multistart vector quantization optimization algorithm; intelligent manufacturing: IDES - Influence Diagram Architecture; and 1st PRINCE (FIRST PRINciple Computational Evaluator).

Cagan, Jonathan↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Artificial intelligence programming languages for computer aided manufacturing

Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL, and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL, LISP, MICROPLANNER, and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.

Rieger, C.↗
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↗