期刊
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
卷 32, 期 7, 页码 3073-3080出版社
KOREAN SOC MECHANICAL ENGINEERS
DOI: 10.1007/s12206-018-0610-1
关键词
Deep neural network; Drop out; Ultrasonic testing; Weldment flaws classification
资金
- National Research Foundation of Korea (NRF) grant - Korean Government (MEST) [NRF - 2016R1A6A3A11932440]
Ultrasonic signal classification of defects in weldment, in automatic fashion, is an active area of research and many pattern recognition approaches have been developed to classify ultrasonic signals correctly. However, most of the developed algorithms depend on some statistical or signal processing techniques to extract the suitable features for them. In this work, data driven approaches are used to train the neural network for defect classification without extracting any feature from ultrasonic signals. Firstly, the performance of single hidden layer neural network was evaluated as almost all the prior works have applied it for classification then its performance was compared with deep neural network with drop out regularization. The results demonstrate that given deep neural network architecture is more robust and the network can classify defects with high accuracy without extracting any feature from ultrasonic signals.
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