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

2014 ◽  
Vol 6 ◽  
pp. 368-375 ◽  
Author(s):  
Raghuram Karthik Desu ◽  
Sharath Chandra Guntuku ◽  
Aditya B ◽  
Amit Kumar Gupta
Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3766 ◽  
Author(s):  
Shin-Hyung Song

In this research, hot deformation experiments of 316L stainless steel were carried out at a temperature range of 800–1000 °C and strain rate of 2 × 10−3–2 × 10−1. The flow stress behavior of 316L stainless steel was found to be highly dependent on the strain rate and temperature. After the experimental study, the flow stress was modeled using the Arrhenius-type constitutive equation, a neural network approach, and the support vector regression algorithm. The present research mainly focused on a comparative study of three algorithms for modeling the characteristics of hot deformation. The results indicated that the neural network approach and the support vector regression algorithm could be used to model the flow stress better than the approach of the Arrhenius-type equation. The modeling efficiency of the support vector regression algorithm was also found to be more efficient than the algorithm for neural networks.


2013 ◽  
Vol 45 ◽  
pp. 616-627 ◽  
Author(s):  
Amit Kumar Gupta ◽  
Hansoge Nitin Krishnamurthy ◽  
Yashjeet Singh ◽  
Kaushik Manga Prasad ◽  
Swadesh Kumar Singh

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