Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography

: Wei, Ziang; Fernandes, Henrique Coelho; Herrmann, Hans-Georg; Tarpani, Jose Ricardo; Osman, Ahmad

Fulltext urn:nbn:de:0011-n-6213148 (7.3 MByte PDF)
MD5 Fingerprint: c74bfed907f0150286417780812527eb
(CC) by
Created on: 14.1.2021

Sensors. Online journal 21 (2021), No.2, Art. 395, 18 pp.
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Journal Article, Electronic Publication
Fraunhofer IZFP ()
composite materials; infrared thermography; deep learning; damage segmentation; curve shaped laminates

Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP)laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weld ability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.