The defect detection and non-destructive evaluation in weld zone of austenitic stainless steel 304 using neural network-ultrasonic wave

1998 ◽  
Vol 12 (6) ◽  
pp. 1150-1161 ◽  
Author(s):  
Won Yi ◽  
In-Sik Yun
2013 ◽  
Vol 45 ◽  
pp. 616-627 ◽  
Author(s):  
Amit Kumar Gupta ◽  
Hansoge Nitin Krishnamurthy ◽  
Yashjeet Singh ◽  
Kaushik Manga Prasad ◽  
Swadesh Kumar Singh

2014 ◽  
Vol 16 (11) ◽  
pp. 1381-1390 ◽  
Author(s):  
Thomas Gietzelt ◽  
Volker Toth ◽  
Andreas Huell ◽  
Florian Messerschmidt ◽  
Roland Dittmeyer

2013 ◽  
Vol 753 ◽  
pp. 83-86
Author(s):  
Wei Guo Wang ◽  
Xiao Ying Fang ◽  
Hong Guo

Though there developed same concentrations of special grain boundaries (SBs) in grain boundary engineered (GBE) austenitic stainless steel (304 stainless steel) and a Pb-Ca based alloy, the makeup of SBs, size distribution of clusters of grains with ∑3n (n=1,2,3) orientation relationships (∑3n CG), and grain orientations (textures) are quite different between the two specimens, suggesting there have two different mechanisms separately governing the evolution of grain boundary character distributions (GBCDs) in the two types of materials during GBE processing.


Sign in / Sign up

Export Citation Format

Share Document