The use of artificial neural networks (backpropagation networks) for p
redicting heat-affected zone hardness, given the 800-500 degrees C coo
ling time and chemical composition, is investigated in this study. The
experimental training data are taken from a database assembled by Yur
ioka ei al. Network predicted hardness values are compared with experi
mental values from the entire Yurioka database and reasonable agreemen
t is found (correlation factor = 0.98). The network results are also c
ompared with values calculated from the regression relationships of Yu
rioka and Suzuki based on the same database. Finally, an optimal netwo
rk architecture (1 hidden layer, 4 hidden nodes and 40 training patter
ns) is suggested.