Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

2005 ◽  
Vol 34 (4) ◽  
pp. 335-341 ◽  
Author(s):  
A. Bahrami ◽  
S.H. Mousavi Anijdan ◽  
H.R. Madaah Hosseini ◽  
A. Shafyei ◽  
R. Narimani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Effective Parameters ◽  
Artificial Neural

scholarly journals Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 234 ◽  
Author(s):  
Yuxuan Wang ◽  
Xuebang Wu ◽  
Xiangyan Li ◽  
Zhuoming Xie ◽  
Rui Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Tensile Properties ◽  
Prediction Performance ◽  
Good Prediction ◽  
Relative Significance ◽  
Value Analysis ◽  
Artificial Neural

Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.

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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