Assessing Constitutive Models for Prediction of High-Temperature Flow Behavior with a Perspective of Alloy Development

• DOI: 10.1007/s11665-018-3237-6
• Publication Date: 2018-02-23T07:58:00Z
• Authors: Santosh Kumar; B. Aashranth; M. Arvinth Davinci; Dipti Samantaray; Utpal Borah; A. K. Bhaduri
• Keywords: Experimental data

The utility of different constitutive models describing high-temperature flow behavior has been evaluated from the perspective of alloy development. Strain compensated Arrhenius model, modified Johnson–Cook (MJC) model, model D8A and artificial neural network (ANN) have been used to describe flow behavior of different model alloys. These alloys are four grades of SS 316LN with different nitrogen contents ranging from 0.07 to 0.22%. Grades with 0.07%N and 0.22%N have been used to determine suitable material constants of the constitutive equations and also to train the ANN model. While the ANN model has been developed with chemical composition as a direct input, the MJC and D8A models have been amended to incorporate the effect of nitrogen content on flow behavior. The prediction capabilities of all models have been validated using the experimental data obtained from grades containing 0.11%N and 0.14%N. The comparative analysis demonstrates that ‘N-amended D8A’ and ‘N-amended MJC’ are preferable to the ANN model for predicting flow behavior of different grades of 316LN. The work provides detailed insights into the usual statistical error analysis technique and frames five additional criteria which must be considered when a model is analyzed from the perspective of alloy development.