Abstract

This paper shows how a convolutional neuronal network can be used to segment multiple features (such as
matrix, fiber bundles and defects) in a single step from X-Ray computed tomography data acquired from carbon
fiber reinforced polymer (CFRP) specimens. The sample analyzed was 5 plies thick plain weave CFRP widely
used in automotive and aerospace application. The specimen was scanned using a GE phoenix X-ray Nanotom
XCT with an voltage of 60kV and a voxel size of (2.5µm)2
. To allow for the prediction of multiple classes, the
standard U-Net architecture was extended to use a softmax (one-hot encoding) as output layer. The trained
network delivers similar results as compared to current state-of-the art methods, with the additional advantage
of reducing the number of required human
OriginalspracheEnglisch
PublikationsstatusVeröffentlicht - 4 Apr. 2022
VeranstaltungSPIE Smart Structures + Nondestructive Evaluation 2022 - Long Beach , Long Beach, USA/Vereinigte Staaten
Dauer: 4 Apr. 20226 Apr. 2022

Konferenz

KonferenzSPIE Smart Structures + Nondestructive Evaluation 2022
Land/GebietUSA/Vereinigte Staaten
OrtLong Beach
Zeitraum04.04.202206.04.2022

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