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.

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.

Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel

2011 ◽  
Vol 695 ◽  
pp. 361-364 ◽  
Author(s):  
Ying Han ◽  
Guan Jun Qiao ◽  
Dong Na Yan ◽  
De Ning Zou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Flow Stress ◽  
Strain Rate ◽  
Hot Deformation ◽  
Deformation Behavior ◽  
Martensitic Stainless Steel ◽  
Ann Model ◽  
Hot Deformation Behavior

The hot deformation behavior of super 13Cr martensitic stainless steel was investigated using artificial neural network (ANN). Hot compression tests were carried out at the temperature range of 950°C to 1200°C and strain rate range of 0.1–50s–1at an interval of an order of magnitude. Based on the limited experimental data, the ANN model for the constitutive relationship existed between flow stress and strain, strain rate and deformation temperature was developed by back-propagation (BP) neural network method. A three layer structured network with one hidden layer and ten hidden neurons was trained and the normalization method was employed in training for avoiding over fitting. Modeling results show that the developed ANN model can efficiently predict the flow stress of the steel and reflect the hot deformation behavior in the whole deforming process.

Prediction of the Flow Stress of 00Cr17Ni14Mo2 Steel during Hot Deformation Using a BP Artificial Neural Network

2011 ◽  
Vol 181-182 ◽  
pp. 979-982
Author(s):  
Nan Hai Hao ◽  
Jiu Shi Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Hot Deformation ◽  
Flow Behavior ◽  
Training Set ◽  
Bp Artificial Neural Network ◽  
316L Austenitic Stainless Steel ◽  
Artificial Neural ◽  
Deformation Tests

The knowledge of the flow behavior of metals during hot deformation is of great importance in determining the optimum forming conditions. In this paper, the flow stress of 00Cr17Ni14Mo2 (ANSI 316L) austenitic stainless steel in elevated temperature is measured with compression deformation tests. The temperatures at which the steel is compressed are 800-1100°C with strain rates of 0.01-1s-1. With the experiment result as the training set, the flow stress of 00Cr17Ni14Mo2 steel during hot deformation is predicted using a BP artificial neural network. The architecture of the network includes three input parameters, one output parameter and two hidden layers with 5 neurons in first layer and 6 neurons in second layer. Compared with the regression method, the prediction using the BP artificial neural network has higher efficiency and accuracy.

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.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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.

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