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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

3D X-ray CT for BGA/CGA Workmanship Defect Detection

Commercial-off-the-shelf (COTS) advanced microelectronic technologies in high-reliability versions are now being considered for use in a number of National Aeronautics and Space Administration (NASA) electronic systems. One of the key drawbacks of advanced electronic packages with hidden solder joint interconnections, such as the column grid array (CGA), is that inspection can be challenging—whether visually or using X-rays. In general, inspection for solder joint integrity is poor, except for identifying shorts. The new, advanced X-ray systems, especially the 3D computer tomography version, may be able to provide the three-dimensional features that are extremely difficult to resolve under the 2D systems.This report presents both 2D and 3D X-ray images along with their representative optical photomicrographs for a number of advanced electronics package assemblies including 3D stack and CGA assemblies before and after thermal cycling.

Ghaffarian, Reza↗
DOE OSTI2022

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.

Ziabari, Amir↗
DOE OSTI2022

Ultrasonic Testing (UT) and Computed Tomography (CT) Comparative Scanning of Proposed Additive Manufacturing Reference Standard

This report presents qualitative results of ultrasonic full matrix capture/total focusing method (FMC/TFM) scanning of two series of blocks, additively manufactured by powder bed fusion, containing a variety of internal features and structures that would not be achievable by conventional manufacturing techniques. The purpose of the first series of additively manufactured (AM) blocks was to explore the possibility of building calibration blocks for FMC/TFM ultrasonic testing (UT). It was confirmed that AM is a suitable candidate for generation of unusual and novel reflector forms, such as rotating slots, purposely embedded voids, and tapering holes, and that FMC/TFM was very capable of characterizing them. The intended use of the second series of AM blocks was as UT reference blocks to characterize the AM process quality or the interrogating UT technique. The larger block in this series was scanned from multiple faces, using multiple UT methods (conventional UT and FMC/TFM) and X-ray computed tomography for comparative purposes. The performance of each method was quantified by a metric corresponding to the detection limit of each feature, and the quantitative results are discussed.

36 MATERIALS SCIENCE↗