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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Frequency domain assurance criterion for defect detection in additively manufactured parts

Additive manufacturing (AM) is a very desirable manufacturing method for industries such as aerospace and automotive due to its ability to create internal features and complex geometries, however, these characteristics also present challenges for nondestructive evaluation (NDE) and defect detection. Dynamic analysis and modal analysis have shown promise for NDE and identifying flaws in AM parts. Frequency Domain Assurance Criterion (FDAC) is a method of dynamic evaluation that represents the degree of correlation between two sets of operational deflection shapes (ODS) at each spectral line. Using colormaps to display the results allows for quick and easy interpretation of parts’ similarities and differences. This method appears to show promise in identifying parts with minor defects and can be used to differentiate nominal and defective parts. This paper explores the use of FDAC on various AM parts with and without intentional defects.

Deonarain, G↗
DOE OSTI2022

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗
DOE OSTI2026

Product Defect Detection System: SYSM- 5620 Final Project

Retail sales is a growing market estimated to up to seven percent year over year. With this growing market there is also a trend in growing rate of retail returns, estimated just last year at $\$$850 billion. Retail stores must ensure that products available for purchase remain safe, undamaged, and acceptable to customers throughout their time in the store. This job exists regardless of the specific solution used because stores are always responsible for preventing damaged or defective products from reaching customers and when they fail to this is categorized under operation inefficiencies which accounts for an estimated $\$$12 billion in returns. When defective items remain on the sales floor, stores may experience increased returns, reduced customer satisfaction, loss of customer trust, and potential safety concerns depending on the product type. As a result, the core job to be done is to identify defective products quickly, remove them from the sales floor before they are purchased, and preserve useful information about the defect so that the store can improve its handling, stocking, and supplier coordination over time. The need for a more reliable process is especially important in high volume retail environments where employees manage large numbers of products across many aisles, shelves, and storage areas. In these settings, manual inspection alone can be inconsistent and difficult to sustain at the individual item level. At the same time, broader retail trends continue to emphasize operational efficiency, product visibility, and improved customer experience, creating an opportunity for more automated and data driven defect detection methods.

42 ENGINEERING↗
DOE OSTI2024

Post Irradiation Examination Dislocation Defect Detection Software

This software provides dislocation-type defect identification and segmentation using a standard open source computer vision model, YOLOv8, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of expert annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on multiple alloys. It includes multiple layers of frozen layers used for transfer learning from multidisciplinary data and is extensible to alloys that are not included in the training dataset.

Anderson, MatthewW↗
DOE OSTI2023

Internal defect detection and characterization of samarium-cobalt sintered magnets by ultrasonic testing technique

Excessive quantities of samarium-cobalt (Sm-Co) magnet material are being scrapped needlessly due to a lack of understanding of inhomogeneity distribution and unacceptable internal defects. If there is a way to identify, locate, characterize and when needed separate the defective portions of magnet material, utilization can be increased and product quality improved. Further, the magnets’ magnetic and mechanical performance can be improved by reducing the occurrence of internal defects. This paper reports on a cost-effective and efficient nondestructive evaluation method based on an ultrasonic testing (UT) technique applied for detecting and characterizing internal defects in Sm-Co sintered magnets. Applying the UT technique will allow users to comprehensively analyze internal defects, such as inclusions, porosity, microcracks, and other structural irregularities, check for homogeneity and anomalous regions and give the locations of these internal anomalies and defects within the Sm-Co sintered magnets. The UT technique can also be applied to other rare-earth permanent magnets, such as sintered or die-upset neodymium-iron-boron (Nd-Fe-B) magnets. The UT technique can effectively guide quality control and acceptable product selection, in addition to optimizing the magnet alloy design and production processes. Therefore, it can facilitate the improvement of magnet manufacturing efficiency and machinability, reduce scrap, prolong service life, increase the use of what would be post-production waste, and enhance product reuse and recycling at end-of-life disposition.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-Defect Detection in Additively Manufactured Lattice Structures Using 3D Electrical Resistance Tomography

Cellular lattice structures possess high strength-to-weight ratios suitable for advanced lightweight engineering applications. However, their quality and mechanical performance can degrade because of defects introduced during manufacturing or in-service. Their complexity and small length scale features make defects difficult to detect using conventional nondestructive evaluation methods. Here we propose a current injection-based method, electrical resistance tomography (ERT), that can be used to detect damaged struts in conductive cellular lattice structures with their intrinsic electromechanical properties. The reconstructed conductivity distributions from ERT can reveal the severity and location of damaged struts without having to probe each strut. However, the low central sensitivity of ERT may result in image artifacts and inaccurate localization of damaged struts. To address this issue, this study introduces an absolute, high throughput, conductivity reconstruction algorithm for 3D ERT. The algorithm incorporates a strut-based normalized sensitivity map to compensate for lower interior sensitivity and suppresses reconstruction artifacts. Numerical simulations and experiments on fabricated representative cellular lattice structures were performed to verify the ability of ERT to quantitatively identify single and multiple damaged struts. The improved performance of this method compared with classical ERT was observed, based on greatly decreased imaging and reconstructed value errors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experiences Detecting Defective Hardware in Exascale Supercomputers

In May 2022, the newest supercomputer to top the TOP 500 list was Frontier at Oak Ridge National Laboratory, demonstrating the capability of computing more than 1.1 quintillion (1018) floating-point calculations every second. Driving this ground-breaking rate of computing is Frontier’s more than 37,000 graphics processing units (GPUs) and 9,408 central processing units (CPUs). In total, Frontier contains more than 60 million parts. At this scale, the smallest margin of error may generate hundreds of hardware errors across the system. These errors are capable of directly hindering world-class science performed on Frontier if not found. In this work, we describe and evaluate two strategies for finding hardware-level faults in Frontier’s 9,408 compute nodes. There are two strategies developed: the first uses the Slurm scheduler to scavenge available compute time to run the node screen, the second builds upon the lessons learned in the first strategy and enforces a weekly screen of each node. Using June 2023 as a case study, we find that the first scheduling strategy consumed more than ten times the resources as the second scheduling strategy, but successfully detected five hardware defects in Frontier. We summarize the lessons learned while developing and running a node screen on the world’s first exascale supercomputer.

Hagerty, Nick↗
DOE OSTI2025

Effective Defect Detection Using Instance Segmentation for NDI

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask- RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs.

computer vision techniques↗
DOE OSTI2025

Defect Detection Model Development for Large Scale Thermoplastic Printing

Large-format additive manufacturing (LFAM) offers several advantages, including high throughput, cost-effective pellet-fed extrusion, and the capability to produce large-scale structures. The main pain points of LFAM include start and stops during the printing process, warpage, long layer times that lead to bead freezing, and bead separation due to shrinkage. These issues can lead to overfill, underfill and buildup of material in different sections of a print. This can lead to hidden defects embedded within the printed layers, or even ultimate failure of the printed structure. This ensures these defects can only be identified through nondestructive testing (NDT) inspection methods after printing, which can be timely and costly. Aligned Vision work specializes in 2D projectors with visual inspection systems and machine learning. Traditionally system is used for composite layup and layup inspections. In this work we used the LFAM system at Oak Ridge National Laboratory to create defect rich samples. The Aligned Vision inspection system then performed in-situ monitoring of the print process after each part was printed. This in-situ vision inspection system was used to develop a layer-by-layer inspection model that looks for overfill, underfill, and the buildup of defects using only a camera-based vision system. This leads to the assurance of high-quality production components.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Detecting defects that reduce breakdown voltage using machine learning and optical profilometry

Abstract Semiconductor wafer manufacturing relies on the precise control of various performance metrics to ensure the quality and reliability of integrated circuits. In particular, GaN has properties that are advantageous for high voltage and high frequency power devices; however, defects in the substrate growth and manufacturing are preventing vertical devices from performing optimally. This paper explores the application of machine learning techniques utilizing data obtained from optical profilometry as input variables to predict the probability of a wafer meeting performance metrics, specifically the breakdown voltage (V bk ). By incorporating machine learning techniques, it is possible to reliably predict performance metrics that cause devices to fail at low voltage. For diodes that fail at a higher (but still below theoretical) breakdown voltage, alternative inspection methods or a combination of several experimental techniques may be necessary.

42 ENGINEERING↗
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗