NDLI: Processing and Characterisation ( ICMPC 2014 ) Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304

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Processing and Characterisation ( ICMPC 2014 ) Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304

Content Provider	Semantic Scholar
Author	Desu, Raghuram Karthik Guntuku, Sharath Chandra Aditya, Basina Gupta, Amit Kumar
Copyright Year	2014
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 nonDynamic 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. © 2014 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the Gokaraju Rangaraju Institute of Engineering and Technology (GRIET).
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://core.ac.uk/download/pdf/82706921.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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