A Comparative Study on Modeling of High Temperature Rheological Behavior of AZ80 Magnesium Alloy Using Artificial Neural Network and Inverse Method

2012 ◽  
Vol 522 ◽  
pp. 136-141
Author(s):  
Yan Lou ◽  
Luo Xing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Strain Rate ◽  
Rheological Behavior ◽  
Inverse Method ◽  
Strain Rate Range ◽  
Compression Tests ◽  
Prediction Ability ◽  
Artificial Neural

Artificial neural network (ANN) and inverse method were employed in modeling the rheological behavior 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. Investigation of flow stress curves and microstructure of the compression specimen illustrate occurrence of dynamic recrystallization. The inverse method of non-liner regression was used to determine the parameters of the suggested constitutive equation. The maximum relative errors at different temperatures and different strain rates between experimental and predicted flow stresses by ANN and inverse method were compared. The results show the ANN derives statistical models have better similar prediction ability to those of inverse method, especially at high strain rate. This indicates that ANN can be used as an alternative modeling tool for high temperature rheological behavior studies.

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.

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.

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system

2015 ◽  
Vol 9 ◽  
pp. 60-67 ◽  
Author(s):  
Marziyeh Ramzi ◽  
Mahdi Kashaninejad ◽  
Fakhreddin Salehi ◽  
Ali Reza Sadeghi Mahoonak ◽  
Seyed Mohammad Ali Razavi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Rheological Behavior ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

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.

scholarly journals Estimation of Stress-Strain behavior of polyethylene terephthalate (PET) at different strain rates by Artificial Neural Network under simultaneous stretch scenario

2021 ◽  
Author(s):  
Fei Teng ◽  
Gary Menary ◽  
Savko Malinov ◽  
Shiyong Yan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Strain Rates ◽  
Stress Strain ◽  
Strain Behavior ◽  
Strain And Strain Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
High Degree

In this paper, an Artificial Neural Network (ANN) is used to predict the stress-strain behavior of PET at conditions relevant to Stretch Blow Moulding i.e. Large equibiaxial deformation at elevated temperature and high strain rate. The input vectors considered are temperature, strain, and strain rate with a corresponding output parameter of stress. In the present work, a feed-forward back backpropagation algorithm was used to train the ANN. The ANN is able to approximate the relationship between stress and strain at various strain rates & temperatures to a high degree of accuracy for all conditions tested.

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