The Effect of Different Flaw Data to Machine Learning Powered Ultrasonic Inspection

Previous research (Li et al., Understanding the disharmony between dropout and batch normalization by variance shift. CoRR abs/1801.05134 (2018). http://arxiv.org/abs/1801.05134 arXiv:1801.05134 ) has shown the plausibility of using a modern deep convolutional neural network to detect flaws from pha...

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Veröffentlicht in:	Journal of nondestructive evaluation 2021, Vol.40 (1), Article 24
Hauptverfasser:	Koskinen, Tuomas, Virkkunen, Iikka, Siljama, Oskar, Jessen-Juhler, Oskari
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Application Arrays Artificial neural networks Characterization and Evaluation of Materials Classical Mechanics Control Dynamical Systems Engineering Flaw detection Inspection Machine learning Neural networks Notches Object recognition Size distribution Solid Mechanics Solidification System effectiveness Technology Training Trends in NDE 4.0: Purpose Ultrasonic testing Vibration
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