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, 10461051.
Download postscript file
Download TeX file
Download review of Neural Networks in Materials Science
Science and Technology of Welding and JoiningA journal founded and edited by 

Mathematical Modelling of Weld PhenomenaEds. H. Cerjak and K. E. Eastering 

Mathematical Modelling of Weld Phenomena 2Edited by H. Cerjak 

Mathematical Modelling of Weld Phenomena 3Edited by H. Cerjack 

Mathematical Modelling of Weld Phenomena 4Edited by H. Cerjack 
Superalloys  Titanium  Bainite  Martensite  Widmanstätten ferrite 
Cast iron  Welding  Allotriomorphic ferrite  Movies  Slides 
Neural Networks  Creep  Mechanicallly Alloyed  Theses  Retained Austenite 
PT Group Home  Materials Algorithms 