Effect of topology changes of neural network in classification of weld defects. (2020)

• Record Type: Journal Article
• Authors: Lalithakumari, S.; Pandian, R.
• Is Part Of: Materials today. Volume 33:Part 7(2020)
• Page Start: 2656
• Page End: 2659
• Publication Date: 2020
• Subjects: TOFD -- Classification accuracy -- MSE -- SSE -- MSEREG; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.01.222
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Non destructive testing play vital role in weld assessments. Time of Flight Diffraction testing is one of the reliable ultrasonic testing and evaluation method, followed globally. In this paper, TOFD testing based automatic weld classification is proposed. Artificial neural Network based algorithm is proposed and architecture, network topology of the feed forward network, Cascade forward network, Elman recurrent network and Radial basis function network are discussed. The classification of weld defects are also analyzed, based on their performance metric, classification accuracy. It also focuses on model development, neural network paradigms, training and testing of the proposed model for the time scale feature set.