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  4. Crack size measurements on fracture surface images using deep neural networks for semantic segmentation
 
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2023
Conference Paper
Title

Crack size measurements on fracture surface images using deep neural networks for semantic segmentation

Abstract
For the safe evaluation of nuclear-relevant safety components, a precise and reliable analysis of the fracture surfaces after the test procedure is required. Within the scope of the studies a framework for automated crack size measurements based on image segmentation has been developed, capable of accelerating the standardized but tedious measurement procedure. Different known image segmentation architectures have been trained and assessed based on a specially created fracture surface image dataset. The fracture surfaces originate from SE(B) specimens made of the German reactor pressure vessel steel 22NiMoCr3-7 and its weld material. The evaluation of the model performances via the mIoU metric show that the investigated architectures are very well suited for the pixel-fine classification of fracture mechanisms. Based on the obtained prediction masks, the initial crack size a0 could be measured using the so-called area average (AA) method. The results have been compared to manual 5-point average (5PA) measurements. The automated crack length measurements show statistically verifiable very high precision, comparability, and economic efficiency.
Author(s)
Rosenberger, Johannes
RWTH Aachen University  
Tlatlik, Johannes  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Münstermann, Sebastian
RWTH Aachen University  
Mainwork
ASME Pressure Vessels & Piping Conference, PVP 2023. Proceedings. Vol.2: Computer Technology & Bolted Joints; Design & Analysis  
Project(s)
Automated analysis of fracture surfaces using artificial neural networks (ANN) for nuclearrelevant safety components
Funder
Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz  
Conference
Pressure Vessels & Piping Conference 2023  
DOI
10.1115/PVP2023-106773
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • automated crack size measurement

  • area average method

  • image segmentation

  • deep learning

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