出版社：SISSA, Scuola Internazionale Superiore di Studi Avanzati
摘要：In this paper, an artificial neural network (ANN) model has been suggested to predict
the constitutive flow behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless
steel under hot deformation. Hot compression tests in the temperature range 850°C-
1250°C and strain rate range 10-3-102 s-1 were carried out. These tests provided the
required data for training the neural network and for subsequent testing. The inputs of
the neural network are strain, log strain rate and temperature while flow stress is
obtained as output. A three layer feed-forward network with ten neurons in a single
hidden layer and back-propagation learning algorithm has been employed. A very good
correlation between experimental and predicted result has been obtained. The effect of
temperature and strain rate on flow behavior has been simulated employing the ANN
model. The results have been found to be consistent with the metallurgical trend.
Finally, a monte carlo analiysis has been carried out to find out the noise sensitivity of
the developed model.
