Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments
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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.
KeywordsDeep neural network Drop out Ultrasonic testing Weldment flaws classification
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- A. Géron, Hands on machine learning with scikit-learn and tensorflow, O’Reilly, Media (2017).Google Scholar
- V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010).Google Scholar
- A. L. Maas, A. Y. Hannun and A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models, Proc. ICML, June (2013).Google Scholar
- A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (2012).Google Scholar
- T. Rashid, Make your own neural network, CreateSpace Independent Publishing Platform (2016).Google Scholar