A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304

Abstract

AISI steel 304 is used in nuclear reactors for the cladding of fuel rods. In the literature, various mathematical modelling methods such as support vector regression (SVR), artificial neural network (ANN) and multi-gene genetic programming (MGGP) have been applied to study the properties of this steel. Among these methods, MGGP possesses the ability to evolve the model structure and its coefficients. The model participating in the evolutionary stage of the MGGP algorithm is a weighted linear sum of several genes, with the weights determined by selecting the genes randomly and combining them using the least squares method to form a MGGP model. As a result, there is a possibility that a gene of lower performance can degrade the performance of the model. To counter this, a modified MGGP (M-MGGP) method is proposed and which introduces a new technique of stepwise regression for the selective combination of genes of only higher performance. The M-MGGP method is applied to the true stress value data obtained from tensile tests conducted on austenitic stainless steel 304 subjected to different strain rates and temperatures. The results show that the M-MGGP model is able to extrapolate the values of true stress more satisfactorily than those of the standardized MGGP, ANN and SVR models. The M-MGGP models are also smaller in size than those from MGGP. The results suggest that the M-MGGP method provides compact and accurate models that can be deployed by experts for efficiently studying the properties of the steel at elevated temperatures.