Artificial neural network modeling of the tensile properties of indigeneously developed 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel

Abstract

The severe and hostile operating conditions of fast breeder reactors demand the development of new austenitic stainless steels that possess higher resistance to void swelling and irradiation embrittlement. This paper discusses the efforts made in the laboratory and industrial scale development of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel and the evaluation of tensile properties. Melting and casting were carried out in a vacuum induction furnace and the data on recovery of various alloying elements was obtained for charge calculations. Based on the recovery data and decarburisation behavicur under different vacuum levels, a series of alloys with close chemistry variations were prepared. Heat treatment was optimised for these special steels to control the grain size at required level. The ingots were thermo-mechanically processed and tensile properties were evaluated. This experimental data has been used to train and test an artificial neural network. The input parameters of the neural network are chemical compositions and test temperature while the yield strength, ultimate tensile strength and uniform elongation were obtained as output. A multilayer perceptron (MLP) based feed-forward network with back-propagation learning algorithm has been employed. A very good performance of the developed network is obtained. The model can be used as a guideline for new alloy development.