Modified arrhenius-type constitutive model and artificial neural network-based model for constitutive relationship of 316LN stainless steel during hot deformation

2015 ◽  
Vol 22 (8) ◽  
pp. 721-729 ◽  
Author(s):  
An He ◽  
Xi-tao Wang ◽  
Gan-lin Xie ◽  
Xiao-ya Yang ◽  
Hai-long Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Constitutive Model ◽  
Hot Deformation ◽  
Constitutive Relationship ◽  
316Ln Stainless Steel ◽  
Artificial Neural ◽  
Relationship Of

Capability of a Feed-Forward Artificial Neural Network to Predict the Constitutive Flow Behavior of As Cast 304 Stainless Steel Under Hot Deformation

2006 ◽  
Vol 129 (2) ◽  
pp. 242-247 ◽  
Author(s):  
Sumantra Mandal ◽  
P. V. Sivaprasad ◽  
S. Venugopal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Strain Rate ◽  
Hot Deformation ◽  
Flow Behavior ◽  
304 Stainless Steel ◽  
Compression Tests ◽  
Feed Forward ◽  
Artificial Neural

A model is developed to predict the constitutive flow behavior of as cast 304 stainless steel during hot deformation using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from hot compression tests in the temperature range 1023-1523K, strain range 0.1-0.5, and strain rate range 10−3-102s−1 are employed to develop the model. A three-layer feed-forward ANN is trained with standard back propagation and some upgraded algorithms like resilient propagation (Rprop) and superSAB. The performances of these algorithms are evaluated using a wide variety of standard statistical indices. The results of this study show that Rprop algorithm performs better as compared to others and thereby considered as the most efficient algorithm for the present study. It has been shown that the developed ANN model can efficiently and accurately predict the hot deformation behavior of as cast 304 stainless steel. Finally, an attempt has been made to quantify the extrapolation ability of the developed network.

Modeling constitutive relationship of Ti40 alloy using artificial neural network

2011 ◽  
Vol 32 (3) ◽  
pp. 1537-1541 ◽  
Author(s):  
Y. Sun ◽  
W.D. Zeng ◽  
Y.Q. Zhao ◽  
X.M. Zhang ◽  
Y. Shu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Constitutive Relationship ◽  
Artificial Neural ◽  
Relationship Of

Prediction of the Flow Stress of a High Alloyed Austenitic Stainless Steel Using Artificial Neural Network

2012 ◽  
Vol 724 ◽  
pp. 351-354 ◽  
Author(s):  
Zhao Hui Zhang ◽  
Dong Na Yan ◽  
Jian Tao Ju ◽  
Ying Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Flow Stress ◽  
Austenitic Stainless Steel ◽  
Strain Rate ◽  
Flow Behavior ◽  
Constitutive Relationship ◽  
Ann Model ◽  
Artificial Neural

The high temperature flow behavior of as-cast 904L austenitic stainless steel was studied using artificial neural network (ANN). Isothermal compression tests were carried out at the temperature range of 1000°C to 1200°C and strain rate range of 0.01 to 10s1. Based on the experimental flow stress data, an ANN model for the constitutive relationship between flow stress and strain, strain rate and deformation temperature was constructed by back-propagation (BP) method. Three layer structured network with one hidden layer and nine hidden neurons was trained and the normalization method was employed in training process to avoid over fitting. Modeling results show that the developed ANN model exhibits good performance for predicting the flow stresses of the 904L steel. Therefore, it can be used to reflect the hot deformation behavior in a wide working window.

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