Analysis and interpretation of weld flaws using ANN
by Vijay R. Rathod; R.S. Anand; Alaknanda Ashok
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 6, No. 3, 2013

Abstract: The paper presents a novel approach for the detection and classification of flaws in weld images is presented. This method has been applied for detecting and discriminating flaws in welds that may correspond to false alarms or possibly all nine types of weld defects (slag inclusion, wormhole, porosity, incomplete penetration, under cuts, cracks, lack of fusion, weaving fault slag line), after being successfully tested on more than 180 radiographic images. The procedure to detect all the types of flaws and feature extraction is implemented by the segmentation algorithm. Classification is carried out by the counter-propagation neural network.

Online publication date: Fri, 13-Sep-2013

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com