Paper Title:
Neural Network Based Toughness Prediction in CGHAZ of Low-Alloy Steel Produced by Temper Bead Welding
  Abstract

In temper bead welding, toughness is one of the key criteria to evaluate the tempering effect. A neural network-based method for toughness prediction in the coarse grained heat affected zone (CGHAZ) of low-alloy steel has been investigated in the present study to evaluate the tempering effect in temper bead welding. Based on the experimentally obtained toughness database, the prediction systems of the toughness of CGHAZ have been constructed using RBF-neural network. The predicted toughness of the synthetic CGHAZ subjected to arbitrary thermal cycles was in good accordance with the experimental results. It follows that our new prediction system is effective for estimating the tempering effect in CGHAZ during temper bead welding and hence enables us to assess the effectiveness of temper bead welding.

  Info
Periodical
Chapter
Chapter 9: Welding and Mechanical Connections and Fracture
Edited by
Huixuan Zhang, Ye Han, Fuxiao Chen and Jiuba Wen
Pages
1880-1887
DOI
10.4028/www.scientific.net/AMM.117-119.1880
Citation
L. N. Yu, M. Sasa, K. Ohnishi, M. Kameyama, S. Hirano, N. Chigusa, K. Saida, M. Mochizuki, K. Nishimoto, "Neural Network Based Toughness Prediction in CGHAZ of Low-Alloy Steel Produced by Temper Bead Welding", Applied Mechanics and Materials, Vols. 117-119, pp. 1880-1887, 2012
Online since
October 2011
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Price
$32.00
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