Fault detection and visualization through micron-resolution X-ray imaging

Cicchiani, J.A.; Hartmuller, T.L.; Sell, C.M.; Wright, R.G.

doi:10.1109/autest.2008.4662635

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Further application areas of damage analysis are found in the manufacturing of electronic devices or the construction of buildings, which suffer from similar damage types. For example, faults within printed circuit boards (PCBs) and their components are investigated by Cicchiani et al [CHSW08]. The main goal of their work was to obtain a positive visual confirmation if a specific failure occurred in the PCB.…”

Section: Tasks Of Visual Computing Supported Materials Sciencesmentioning

confidence: 99%

STAR: Visual Computing in Materials Science

Heinzl

Stappen

2017

Computer Graphics Forum

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Visual computing has become highly attractive for boosting research endeavors in the materials science domain. Using visual computing, a multitude of different phenomena may now be studied, at various scales, dimensions, or using different modalities. This was simply impossible before. Visual computing techniques provide novel insights in order to understand complex material systems of interest, which is demonstrated by strongly rising number of new approaches, publishing new techniques for materials analysis and simulation. Outlining the proximity of materials science and visual computing, this state of the art report focuses on the intersection of both domains in order to guide research endeavors in this field. We provide a systematic survey on the close interrelations of both fields as well as how they profit from each other. Analyzing the existing body of literature, we review the domain of visual computing supported materials science, starting with the definition of materials science as well as material systems for which visual computing is frequently used. Major tasks for visual computing, visual analysis and visualization in materials sciences are identified, as well as simulation and testing techniques, which are providing the data for the respective analyses. We reviewed the input data characteristics and the direct and derived outputs, the visualization techniques and visual metaphors used, as well as the interactions and analysis workflows employed. All our findings are finally integrated in a cumulative matrix, giving insights about the different interrelations of both domains. We conclude our report with the identification of open high level and low level challenges for future research.

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Section: Tasks Of Visual Computing Supported Materials Sciencesmentioning

confidence: 99%