Impact toughness of C–Mn steel arc welds – Bayesian neural network analysis
Impact toughness of C–Mn steel arc welds – Bayesian neural network analysis
复制标题
C-Mn钢电弧焊缝的冲击韧性——贝叶斯神经网络分析
DOI：
10.1179/mst.1995.11.10.1046
复制
发表时间：
1995
影响因子：
1.8
通讯作者：
Dr Svensson
中科院分区：
文献类型：
--
作者：
H. Bhadeshia;D. Mackay;L. Svensson;D. Mackay;Dr Svensson
采用贝叶斯框架内的神经网络技术，对手工电弧焊和埋弧焊金属试样的夏比冲击韧性数据进行了分析。在这个框架中，韧性可以被表示为一个一般的经验函数的变量，通常被认为是重要的影响钢焊缝的性能。该方法由于其经验性质而具有局限性，但本文件表明，它可以以预测趋势具有冶金意义的方式使用。该方法已被用来检查的相对重要性的许多变量被认为是控制焊缝的韧性。MST/3115
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 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 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. MST/3115