Prediction of Flow Stress in Ti-6Al-4V Alloy with Hydrogen at High Temperature Using Artificial Neural Network

2007 ◽  
Vol 539-543 ◽  
pp. 3696-3701
Author(s):  
Qing Wang ◽  
T. Wu ◽  
Dong Li Sun ◽  
J. Lai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Artificial Neural

Predicting Flow Stress of Four Types of Microalloyed Forging Steel at High Temperature by Artificial Neural Network Model

2017 ◽  
Vol 729 ◽  
pp. 75-79
Author(s):  
Hu Sen Jiang ◽  
Jin Wang ◽  
Li Hua Li ◽  
Hai Tao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Constitutive Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Artificial Neural ◽  
Input Variables

Artificial neural network (ANN) gets a lot of applications in predicting flow stress of steels at high temperature. However, few studies have been devoted to simultaneously predict flow stress of several steels by ANN. The purpose of this paper is to determine the effect of ANN on simultaneously predicting flow stress of several steels. Based on the results of previous compression experiments of four types of microalloyed forging steel, using the mass percentage of major chemical composition of the steels, such as as C, Mn, Si and V, and deformation temperature, strain rate and strain as input variables, a three-layers back propagation neural network was established as the constitutive model for them. Standard statistical methods were employed to quantitatively measure the accuracy of predicted results by the model. The calculated correlation coefficient and the average relative error absolute value between the predicted values by the model and experimental values were 0.9982 and 2.4181%, respectively. In addition, the relative error between the two kinds of values was calculated, and for more than 89% samples, the relative error was within ± 5%. The results show that the developed constitutive model can predict the flow stress of the four types of microalloyed forging steel accurately and simultaneously.

Modeling of High Temperature Flow Stress of AZ80 Magnesium Alloy with Support Vector Machines and Artificial Neural Network

2012 ◽  
Vol 486 ◽  
pp. 227-232
Author(s):  
Yan Lou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Support Vector Machines ◽  
Flow Stress ◽  
Strain Rate ◽  
Support Vector ◽  
Prediction Ability ◽  
Vector Machines ◽  
Artificial Neural

Support vector machines (SVM) and artificial neural network (ANN) were employed in modeling the flow stress of the AZ80 magnesium. The hot deformation behavior of extruded AZ80 magnesium was investigated by compression tests in the temperature 350-450 and strain rate range 0.01-50 s-1. The maximum relative errors at different temperatures and different strain rates between experimental and predicted flow stresses by SVM and ANN were compared. The results show the SVM derives statistical models have better similar prediction ability to those of ANN, especially at high strain rate. This indicates that SVM can be used as an alternative modeling tool for high temperature rheological behavior studies.

MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

2013 ◽  
Vol 02 (01) ◽  
pp. 1350004 ◽  
Author(s):  
S. RATH ◽  
P. P. SENGUPTA ◽  
A. P. SINGH ◽  
A. K. MARIK ◽  
P. TALUKDAR
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Mathematical Models ◽  
Hybrid Model ◽  
Data Driven ◽  
Roll Force ◽  
Hot Strip ◽  
Artificial Neural

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

scholarly journals A Comparative Study on Improved Arrhenius-Type and Artificial Neural Network Models to Predict High-Temperature Flow Behaviors in 20MnNiMo Alloy

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-zheng Quan ◽  
Chun-tang Yu ◽  
Ying-ying Liu ◽  
Yu-feng Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Constitutive Model ◽  
Relative Error ◽  
Type Model ◽  
Ann Model ◽  
Data Set ◽  
Average Absolute Relative Error ◽  
Artificial Neural

The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173∼1473 K and strain rate range of 0.01∼10 s−1. Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former,Rand AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of −39.99%∼35.05% and −3.77%∼16.74%. As for the former, only 16.3% of the test data set possessesη-values within±1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.

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