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

Abstract 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.

[1]  D. Bajić,et al.  Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling , 2009 .

[2]  Y. Lin,et al.  A critical review of experimental results and constitutive descriptions for metals and alloys in hot working , 2011 .

[3]  Francesco Parrella.  Online Support Vector Regression , 2007 .

[4]  Gokul Hariharan,et al.  Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network , 2012 .

[5]  Farida Cheriet,et al.  Prediction of anterior scoliotic spinal curve from trunk surface using support vector regression , 2005, Eng. Appl. Artif. Intell..

[6]  Amit Kumar Gupta,et al.  Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression , 2010 .

[7]  D. Agard,et al.  Microtubule nucleation by γ-tubulin complexes , 2011, Nature Reviews Molecular Cell Biology.

[8]  R. Armstrong,et al.  Dislocation-mechanics-based constitutive relations for material dynamics calculations , 1987 .

[9]  Hua He,et al.  Learning to predict ice accretion on electric power lines , 2012, Eng. Appl. Artif. Intell..

[10]  Ivan Bratko,et al.  Learning qualitative models from numerical data , 2011, Artif. Intell..

[11]  H. J. McQueen,et al.  Constitutive analysis in hot working , 2002 .

[12]  J. H. Hollomon,et al.  Effect of Strain Rate Upon Plastic Flow of Steel , 1944 .

[13]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[14]  Dongyang Li,et al.  Mechanical, electrochemical and tribological properties of nano-crystalline surface of 304 stainless steel , 2003 .

[15]  William K. Rule,et al.  A revised form for the Johnson-Cook strength model , 1998 .

[16]  Iris Alvarez-Armas,et al.  Strain aging effects on the cyclic behavior of austenitic stainless steels , 1988 .

[17]  Swadesh Kumar Singh,et al.  Development of constitutive models for dynamic strain aging regime in Austenitic stainless steel 304 , 2013 .