Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions.

Abstract:

:Ultrasonic flaw classification in weldment is an active area of research and many artificial intelligence approaches have been applied to automate this process. However, in the industrial applications, the ultrasonic flaw signals are not noise free and automatic intelligent defect classification algorithms show relatively low classification performance. In addition, most of the algorithms require some statistical or signal processing techniques to extract some features from signals in order to make classification easier. In this article, the convolutional neural network (CNN) is applied to noisy ultrasonic signatures to improve classification performance of weldment defects and applicability. The result shows that CNN is robust, does not require specific feature extraction methods and give considerable high defect classification accuracies even for noisy signals.

journal_name

Ultrasonics

journal_title

Ultrasonics

authors

Munir N,Kim HJ,Park J,Song SJ,Kang SS

doi

10.1016/j.ultras.2018.12.001

subject

Has Abstract

pub_date

2019-04-01 00:00:00

pages

74-81

eissn

0041-624X

issn

1874-9968

pii

S0041-624X(18)30575-4

journal_volume

94

pub_type

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