Impact toughness of C-Mn steel arc welds - Bayesian neural network analysis

H. K. D. H. Bhadeshia, D. J. C. MacKay and L.-E. Svensson

Abstract

Charpy impact toughness data for manual metal arc and submerged arc weld metal samples have been analysed using a neural network technique within a Bayesian framework. In this framework, the roughness can be represented as a general empirical function of variables that are commonly acknowledged to be important in influencing the properties of steel welds. The method has limitations owing to its empirical character; but it is demonstrated in the present paper that it can be used in such a way that the predicted trends make metallurgical sense. The method has been used to examine the relative importance of the numerous variables thought to control the toughness of welds.

Materials Science and Technology, Vol. 11, 1995, 1046-1051.

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	Science and Technology of Welding and Joining A journal founded and edited by S. A. David, T. Debroy and H.K.D.H. Bhadeshia Published by The Institute of Materials, London, since 1996
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	Mathematical Modelling of Weld Phenomena 2 Edited by H. Cerjak Series Editor H.K.D.H. Bhadeshia Institute of Materials, London, 1995
	Mathematical Modelling of Weld Phenomena 3 Edited by H. Cerjack Series Editor H.K.D.H. Bhadeshia Institute of Materials, London, 1997
	Mathematical Modelling of Weld Phenomena 4 Edited by H. Cerjack Series Editor H.K.D.H. Bhadeshia Institute of Materials, London, 1998