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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, the toughness 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 due to its empirical character, but it is demonstrated here 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.
Bhadeshia et al. (Sun,) studied this question.
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