Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304

This paper focuses on modelling the relationship between flow stress and strain, strain rate and temperature using Support Vector Regression technique. Data obtained for both the regions (non-Dynamic Strain Aging and Dynamic Strain Aging) is analysed using Support Vector Machine, where a nonlinear model is learned by linear learning machine by mapping it into high dimensional kernel included feature space. A number of semi empirical models based on mathematical relationships and Artificial Intelligence techniques were reported by researchers to predict the flow stress during deformation. This work attempts to show the prowess of Support Vector Regression based modelling applied to flow stress prediction, delineating the flexibility that the user is presented with, while modelling the problem. The model is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. It was found that the correlation coefficient between the predicted and experimental data is 0.9978 for the non- Dynamic Strain Aging regime and 0.9989 for the Dynamic Strain Aging regime showcasing the excellent predictability of this model when compared with other models that are prominently used for flow stress prediction. Data is trained at different values of insensitivity loss function of the Support Vector Regression for showcasing the unique features of this technique. The results produced are encouraging to the researchers for exploring this Artificial Intelligence technique for data modelling.