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Neural network based automated defect detection using induction thermography for surface cracks of forged parts

: Müller, David; Netzelmann, Udo; Ehlen, Andreas; Finckbohner, Michael; Valeske, Bernd

Fulltext (PDF; )

Maldague, X.P.V.:
QIRT 2020, 15th Quantitative InfraRed Thermography Conference. Online resource : Porto (Portugal), 21-30 September 2020, virtual conference
Online im WWW, 2020
ISSN: 2371-4085
QIRT-2020-015, 3 pp.
Quantitative InfraRed Thermography Conference (QIRT) <15, 2020, Online>
Conference Paper, Electronic Publication
Fraunhofer IZFP ()
neural network; defect detection; induction thermography; cracks

A fully convolutional neural network was set up for the detection of crack-type defects and for the defect shape prediction of thermography datasets. The method uses a supervised neural network for sematic segmentation (U-Net). For these tasks, training datasets of forged parts were acquired through induction thermography. The approach provides a significant improvement over conventional methods of thermal signal and image processing used in active thermography. Furthermore, the results may lead to new procedures for a quantitative evaluation of flaws and defects in non-destructive testing using infrared thermography.