Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

2012 ◽  
Vol 23 (7-8) ◽  
pp. 2301-2308 ◽  
Author(s):  
Gholamreza Khalaj ◽  
Tohid Azimzadegan ◽  
Mahdi Khoeini ◽  
Moslem Etaat
Keyword(s):  
Neural Networks ◽  
Tensile Strength ◽  
Artificial Neural Networks ◽  
Ultimate Tensile Strength ◽  
Pipeline Steels ◽  
Artificial Neural

Modeling tensile strength and suture retention of polycaprolactone electrospun nanofibrous scaffolds by artificial neural networks

2021 ◽  
Vol 26 ◽  
pp. 102115
Author(s):  
B.S. Reddy ◽  
Kim Hong In ◽  
Bharat B. Panigrahi ◽  
Uma Maheswera Reddy Paturi ◽  
K.K. Cho ◽  
...  
Keyword(s):  
Neural Networks ◽  
Tensile Strength ◽  
Artificial Neural Networks ◽  
Nanofibrous Scaffolds ◽  
Artificial Neural

Application of Artificial Neural Networks to Estimate Tensile Strength of Austenitic Stainless Steel During Metal Inert Gas Welding Process

2021 ◽  
pp. 197-221
Author(s):  
Sudipto Chaki ◽  
Dipankar Bose
Keyword(s):  
Neural Networks ◽  
Tensile Strength ◽  
Stainless Steel ◽  
Artificial Neural Networks ◽  
Austenitic Stainless Steel ◽  
Welding Current ◽  
Inert Gas ◽  
Mig Welding ◽  
Artificial Neural ◽  
Full Factorial Experimental Design

In the present work, artificial neural networks (ANN) have been used to model the complex relationship between input-output parameters of metal inert gas (MIG) welding processes. Four ANN training algorithms such as back propagation neural network (BPNN) with gradient descent momentum (GDM), BPNN with Levenberg Marquardt (LM) algorithm, BPNN with Bayesian regularization (BR), and radial basis function networks (RBFN) method have been used for prediction modelling. An experimentation based on full factorial experimental design has been conducted on MIG welding of austenitic stainless steel of grade-304 where welding current, welding speed, and voltage have been considered as input parameters, and tensile strength has been considered as measurable output parameter. The dataset so constituted is used for ANN modelling. Altogether, 40 different ANN architectures have been trained and tested using the above-mentioned algorithms, and 3-11-1 ANN architecture trained using BPNN with BR has been considered to show best prediction capability with mean % absolute error of 0.354%.

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