DOI: 10.5176/2251-1857_M312.71

Authors: Yashjeet Singh, Nitin Krishnamurthy, Amit Kumar Gupta, Swadesh Kumar Singh

Abstract: In this paper, a comparative study has been made on the capability of Johnson Cook (JC) model, modified Zerilli-Armstrong (m-ZA) model, modified Arrhenius type equations (m-Arr) and Artificial Neural Networks (ANN) model for predicting the flow stress of Austenitic Stainless Steel (ASS) 316 in Dynamic Strain Aging (DSA) regime. Data from isothermal tensile tests conducted over a wide range of temperatures (623K-923K at an interval of 50K) and at different strain rates (0.0001, 0.001, 0.01,0.1s-1) were employed to identify the DSA regime and to calculate the material constants of above mentioned models. Aptness of these models was then evaluated by comparing their correlation coefficient, average absolute error and standard deviation. The resultant value of the correlation coefficient for JC, m-ZA, m-Arr and ANN model were found to be 0.8339, 0.9001, 0.8925and 0.9985 respectively. Hence it was concluded that JC, m-ZA and m-Arr model could not capture DSA phenomenon effectively, while the ANN model predicted the deformation behaviour of ASS 316 in DSA regime very accurately.
Keywords: Austenitic stainless steel; dynamic strain aging; Johnson Cook model; modified Zerilli-Armstrong model; modified Arrhenius type equation; artificial neural network.


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