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

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

Sudipto Chaki, Dipankar Bose
Copyright: © 2021 |Pages: 25
ISBN13: 9781799832386|ISBN10: 1799832384|ISBN13 Softcover: 9781799832393|EISBN13: 9781799832409
DOI: 10.4018/978-1-7998-3238-6.ch009
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MLA

Chaki, Sudipto, and Dipankar Bose. "Application of Artificial Neural Networks to Estimate Tensile Strength of Austenitic Stainless Steel During Metal Inert Gas Welding Process." Artificial Neural Network Applications in Business and Engineering, edited by Quang Hung Do, IGI Global, 2021, pp. 197-221. https://doi.org/10.4018/978-1-7998-3238-6.ch009

APA

Chaki, S. & Bose, D. (2021). Application of Artificial Neural Networks to Estimate Tensile Strength of Austenitic Stainless Steel During Metal Inert Gas Welding Process. In Q. Do (Ed.), Artificial Neural Network Applications in Business and Engineering (pp. 197-221). IGI Global. https://doi.org/10.4018/978-1-7998-3238-6.ch009

Chicago

Chaki, Sudipto, and Dipankar Bose. "Application of Artificial Neural Networks to Estimate Tensile Strength of Austenitic Stainless Steel During Metal Inert Gas Welding Process." In Artificial Neural Network Applications in Business and Engineering, edited by Quang Hung Do, 197-221. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3238-6.ch009

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Abstract

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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