Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm-Reference-Cited by-同舟云学术

Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm

Published:2011-08-20 Issue:3-4 Volume:22 Page:637-649
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Sudhakaran R.,Vel Murugan V.,Sivasakthivel P. S.,Balaji M.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-011-0720-5.pdf

Reference24 articles.

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3. Tarang YS, Yang WH (1998) Optimization of weld bead geometry in GTAW by Taguchi method. Int J Adv Manuf Technol 14:549–554

4. Manikya Kanti K, Srinivasa Rao P (2008) Prediction of bead geometry in pulsed GMAW using back propagation neural network. J Mater Process Technol 200:300–305

5. Li X, Simpson SW, Rados M (2000) Neural networks for online prediction of quality in GMAW. Sci Technol Weld Join 5(2):71–79

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