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At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS2018

Automated Fiber Placement Defect Identity Cards: Cause, Anticipation, Existence, Significance, and Progression

Automated Fiber Placement (AFP), a major composite manufacturing process, can result in many defects during the layup process that often require manual corrective action to produce a part with acceptable quality. These defects are the main limitation of the technology and can be hard to categorize or define in many situations. This paper provides a thorough definition and classification of all AFP defects. This effort constitutes a comprehensive and extensive library relevant to AFP defects. The defects selected and defined in this work are based on understanding and experience from the manufacture and research of advanced composite structure. Proper classification of these defects required an in-depth literature review and consideration of various viewpoints ranging from designers, manufacturers, analysts, and inspection professionals. Collectively, these sources were utilized to develop the most accurate view of each of the individual defect types. The results are presented as identity cards for each defect type, intended to provide researchers and the manufacturing industry a clear understanding of the (1) cause, (2) anticipation, (3) existence, (4) significance, and (5) progression of the defined AFP defects. The link between AFP defects and process planning, layup strategies, and machining was also investigated. Categorization of all important automated fiber placement defects is presented.

Harik, Ramy↗

NASA NTRS2019

Assessment of Automated Fiber Placement for the Fabrication of Composite Wind Tunnel Blades

Composite wind tunnel blades are frequently fabricated by hand layup of prepreg fabrics. Though well proven, this fabrication method is laborious and expensive. The study described in this paper used the Integrated Structural Assembly of Advanced Composites (ISAAC) facility at the NASA Langley Research Center to explore whether automated fiber placement (AFP) could reduce manufacturing time and cost for production of wind tunnel blades. Two blades, taken from two NASA wind tunnels, were investigated as representative geometries. Computer-aided design models of the blade surfaces were created, and AFP process planning and programming were employed to study the manufacturability of the shapes. A placement/cure tool was manufactured for the chosen blade surface from thermoplastic material using an additive manufacturing process. The present study revealed that the AFP head geometry, primarily the heater configuration of the ISAAC system, is the primary constraint that limits the ability to manufacture the selected wind tunnel fan blades using AFP.

Grenoble, Ray↗

DOE OSTI2024

Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels

Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Kim, Yoon G.↗

NASA NTRSDate not supplied

Design of a Manufacturing Demonstration Unit for a Composite Lunar Landing Gear Strut

The goal of the current work was to develop an analytical framework for design of composite struts using various levels of model fidelity. Rapid optimization trade studies were performed using low fidelity two-dimensional (2D) axisymmetric models with smeared composite properties. The optimum 2D model was compared with three-dimensional (3D) models with higher levels of fidelity in material property representation. Good agreement was found with all models. The buckling performance of the highest fidelity 3D model was found to be satisfactory for the intended loading conditions. An additional goal of this work was to perform an initial assessment of using automated fiber placement (AFP) and other advanced manufacturing methods to explore their feasibility for the fabrication of struts for lunar landers, strut-braced wings, and other aerospace components. The designs produced during this study are intended to be used to develop manufacturing demonstration units (MDU) that can be fabricated at the Integrated Structural Assembly of Advanced Composites (ISAAC) facility at Langley Research Center (LaRC) and tested in lab facilities at LaRC.

carbon-epoxy↗

NASA NTRSDate not supplied

Design of a Manufacturing Demonstration Unit for a Composite Lunar Landing Gear Strut

carbon-epoxy↗

NASA NTRS1991

ISyCL technical report

The Air Force Integrated Information Systems Evolution Environment (IISEE) is a program focused on the development of technology for enabling the planning definition development and maintenance of evolutionary integrated information systems. This program has studied the representational needs of this knowledge base from the point of view of the needs of engineering, manufacturing, and logistics Evolutionary Information Systems (EIS). The definition of the concepts for a suite of automated tools and environments to support the pursuit of such engineering, manufacturing, and logistics EIS's has also required examination of languages and representation schemes for the knowledge base of these integrated systems. To accomplish this purpose, the language must be very rich, and must support object orientation, relational orientation, persistent storage, process transaction specification, and first order logic. The task of designing a unified language which supports these paradigms is a challenge. The Information Systems Constraint Language (ISyCL) is described. It was designed to meet the definitional and knowledge representation needs of three perspectives. It supports users from the area expert to the data base designer.

Decker, Louis P.↗

DOE OSTI2024

In‐mold rheology and automated process control for injection molding of recycled polypropylene

Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Krantz, Joshua↗

NASA NTRS2018

Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings.

Sacco, Christopher↗

DOE OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1984

CIM's bridge from CADD to CAM: Data management requirements for manufacturing engineering

Manufacturing engineering represents the crossroads of technical data management in a Computer Integrated Manufacturing (CIM) environment. Process planning, numerical control programming and tool design are the key functions which translate information from as engineered to as assembled. In order to transition data from engineering to manufacturing, it is necessary to introduce a series of product interpretations which contain an interim introduction of technical parameters. The current automation of the product definition and the production process places manufacturing engineering in the center of CAD/CAM with the responsibility of communicating design data to the factory floor via a manufacturing model of the data. A close look at data management requirements for manufacturing engineering is necessary in order to establish the overall specifications for CADD output, CAM input, and CIM integration. The functions and issues associated with the orderly evolution of computer aided engineering and manufacturing are examined.

Ford, S. J.↗

DOE OSTI2026

Automated 4D X-ray Computed Tomography Registration and Flaw Tracking Across Interrupted Creep Testing of Additively Manufactured Steel

Explore the source record for details and available documents.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗

NASA NTRS2019

Machine Learning Based AFP Inspection: A Tool for Characterization and Integration

Automated Fiber Placement (AFP) has become a standard manufacturing technique in the creation of large scale composite structures due to its high production rates. However, the associated rapid layup that accompanies AFP manufacturing has a tendency to induce defects. We forward an inspection system that utilizes machine learning (ML) algorithms to locate and characterize defects from profilometry scans coupled with a data storage system and a user interface (UI) that allows for informed manufacturing. A Keyence LJ-7080 blue light profilometer is used for fast 2D height profiling. After scans are collected, they are process by ML algorithms, displayed to an operator through the UI, and stored in a database. The overall goal of the inspection system is to add an additional tool for AFP manufacturing. Traditional AFP inspection is done manually adding to manufacturing time and being subject to inspector errors or fatigue. For large parts, the inspection process can be cumbersome. The proposed inspection system has the capability of accelerating this process while still keeping a human inspector integrated and in control. This allows for the rapid capability of the automated inspection software and the robustness of a human checking for defects that the system either missed or misclassified.

Sacco, Christopher↗

DOE OSTI2025

Measuring 3D Profilometry of SAVY-4000 Nuclear Material Storage Containers: Novacam TubeInspect Capabilities Report

The SAVY-4000 container series is a general-purpose interim storage container for nuclear materials, developed and maintained by Los Alamos National Laboratory (LANL). It is the first vented, general-use nuclear material container to be demonstrated as meeting the requirements outlined in DOE M 441.1-1, the Nuclear Material Packaging Manual. Due to the challenging radiation, thermal, and corrosive storage conditions that the SAVY containers must endure, continuous surveillance techniques are employed to ensure the containers meet all safety standards and specifications. These inspections are typically performed by human operators, who check for issues such as corrosion, O-ring deterioration, corrosion, filter integrity, and potential manufacturing defects. However, human inspections alone are not sufficient, and automated inspection technologies, such as the ATIS system, as well as other automated systems are also utilized. The MicroCam TubeInspect, developed by Novacam Technologies Inc., is designed to address the challenges of understanding how manufacturing variations in the SAVY-4000 container series may affect performance. It is a 3D profilometry measurement system that enables detailed analysis of surface features, including defects, surface roughness, and manufacturing variations. This advanced tool significantly enhances rapid surveillance techniques for both pristine and used containers. In this study, container properties such as surface roughness, thickness, and geometric attributes like circularity are measured for SAVY-4000 containers. Artificially corroded or dented containers are examined to demonstrate the MicroCam's ability to quantify defects. A sensitivity analysis is also conducted, comparing the MicroCam results to those obtained using more precise instruments such as confocal microscopy. This comparison aims to provide valuable insights into container quality, durability, and potential improvements in manufacturing processes.

42 ENGINEERING↗

NASA NTRS1991

Design and fabrication of an autonomous rendezvous and docking sensor using off-the-shelf hardware

NASA Marshall Space Flight Center (MSFC) has developed and tested an engineering model of an automated rendezvous and docking sensor system composed of a video camera ringed with laser diodes at two wavelengths and a standard remote manipulator system target that has been modified with retro-reflective tape and 830 and 780 mm optical filters. TRW has provided additional engineering analysis, design, and manufacturing support, resulting in a robust, low cost, automated rendezvous and docking sensor design. We have addressed the issue of space qualification using off-the-shelf hardware components. We have also addressed the performance problems of increased signal to noise ratio, increased range, increased frame rate, graceful degradation through component redundancy, and improved range calibration. Next year, we will build a breadboard of this sensor. The phenomenology of the background scene of a target vehicle as viewed against earth and space backgrounds under various lighting conditions will be simulated using the TRW Dynamic Scene Generator Facility (DSGF). Solar illumination angles of the target vehicle and candidate docking target ranging from eclipse to full sun will be explored. The sensor will be transportable for testing at the MSFC Flight Robotics Laboratory (EB24) using the Dynamic Overhead Telerobotic Simulator (DOTS).

Grimm, Gary E.↗

NASA NTRSDate not supplied

NASA Efforts to Explore Additively Manufactured Thermal Protection Systems

Development of the thermal protection system (TPS) needed to protect external and internal surfaces during launch, ascent, cruise, and reentry, is a very specialized field with expertise developed over seven decades and has enabled successful robotic as well as human exploration. Well known examples are: 1) the ablative heatshield that protected Apollo Astronauts during reentry in 1960’s, 2) reusable TPS used on the Space Shuttle Orbiter, which is now sought after by commercial space industry, and 3) hot structures, such as Carbon-Carbon and other high temperature materials needed to operate control surfaces and sharp leading edges during hypervelocity flight. TPS function demands fail-safe design to ensure mission success. One TPS does not fit all, and each application requires a unique material along with manufacturing and integration approach. TPS mass is a key metric. A robust but inefficient TPS reduces payload mass. Mission- and domain-unique TPS development and flight certification efforts have been expensive with long lead times. As a result, there are limited TPS choices with high cost. For future commercial efforts to be successful, the ability to rapidly develop TPS, based on specific needs and at a lower cost, without compromising either mass efficiency or functionality is necessary. NASA is looking into this. Additive manufacturing (AM) techniques have shown their utility in making complex parts and, through automation, the potential to reduce cost and schedule in some applications. The promise of additive manufacturing to TPS, while exciting, is unexplored. In the past several years, NASA has made exploratory investments in internal development as well as in small business and universities through SBIR and STTR programs. In addition, NASA is bringing together the TPS as well as Additive Manufacturing communities to explore approaches to prioritize focus areas for broad benefits. The very first “Additively Manufactured Thermal Protection System Workshop,” to be held at NASA Johnson Space Center in Houston at the end of March of this year, is the culmination of a year-long effort to engage the research, development, and user communities involved in both TPS as well as additive manufacturing. The workshop participants include representatives from large and small commercial space industry, DoD, Federally Funded Research and Development Centers, University Researchers and NASA personnel. The proposed talk will highlight future TPS needs of both emerging commercial and Government interests, NASA’s development efforts in additively manufactured TPS, and the findings and recommendations from the first AM TPS Workshop.

Ethiraj Venkatapathy↗

DOE OSTI2024

Evaluation of Converter Performance Considering Static and Dynamic Device Part-to-Part Variability

This paper presents a methodology to incorporate and analyze the impact of semiconductor device part-to-part variation on power converter performance. By integrating extensive static and dynamic device characterization data with an automated compact model generation process that reflects manufacturing variability, device models with inherent variability features are utilized in converter simulations for a comprehensive assessment of performance impacts. The traditional converter performance evaluation process typically yields fixed efficiency values, often dismissing the inherent part-to-part variability caused by the manufacturing process of semiconductor devices. To address this limitation, a large population of devices was characterized to capture variations in static parameters-such as transfer, output, and capacitance characteristics-as well as dynamic behaviors, including switching losses. This data-driven approach enables the development of individual compact models, which were then integrated into converter simulations to evaluate efficiency ranges rather than single point estimated values. The converter simulation results show that part-to-part component variation can lead to significant efficiency deviations, exceeding several percentage points in high-power conversion applications. By offering a more accurate representation of converter behavior under real-world manufacturing conditions, this methodology enables designers to anticipate performance variability, improving the robustness of power converter designs.

device characterization↗

DOE OSTI2023

Advanced Processing of Rare Earth Elements and Critical Minerals from Acid Mine Drainage Feedstocks

Ongoing work by the project team identified a process for extracting rare earths and critical mineral (REE/CM) concentrates from acid mine drainage from coal mines, a major stream pollutant. The process removes harmful metals and separates REE/CM into economically attractive concentrates. This project explored the processing of those concentrates into individually separated, high purity metals, suitable for domestic manufacturers. The team brought unique experience in working with AMD-based feedstocks since the inception of USDOE’s coal-based REE/CM program in 2016. The project team has pioneered the recovery of rare earth/critical minerals (REE/CM) from acid mine drainage (AMD). This environmentally beneficial strategy will incentivize AMD treatment while recovering materials critical to the Nation’s defense and advanced manufacturing industries. In addition, the project will investigate the co-production of five high purity CMs from AMD, namely Co, Mn, Li, Cr, Ge. This team’s upstream and midstream recovery and processing technology has been demonstrated at the continuous, small scale pilot scale. In conjunction with the West Virginia Department of Environmental Protection, the team is constructing a full-scale, integrated, 500 gpm AMD treatment plant coupled with an REE/CM facility that will produce 21 tons REE/CM per year. Commissioning is scheduled for September 2021. This will provide feedstock for the team’s proprietary downstream processes that met the project objective: production of individually separated, high purity (ISHP) REE/CM oxides and their further processing to elemental metals (REM). To facilitate entry into the U.S. market, we have teamed up with companies that would use our products to strengthen their domestic supply chains and grow REE/CM based manufacturing in the U.S. They include Rockwell Automation, Rivian, Endress+Hauser, Inc., Northrop Grumman and Hela Novel Metals.

01 COAL, LIGNITE, AND PEAT↗