Joint strength prediction in a pulsed MIG welding process using hybrid neuro ant colony-optimized model

In this work, a pulsed metal inert gas welding (PMIGW) process is modeled by using a hybrid soft computing technique. Ant colony optimization (ACO) and back-propagation neural network (BPNN) models are combined to predict the ultimate tensile strength of butt-welded joints. A large number of experim...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 41; no. 7-8; pp. 694 - 705
Main Authors Raghavendra, N., Koranne, Rakshit, Pal, Sukhomay, Pal, Surjya K., Samantaray, Arun K.
Format Journal Article
LanguageEnglish
Published London Springer-Verlag 01.04.2009
Springer Nature B.V
Subjects
Ant colony optimization
Artificial neural networks
Back propagation networks
Butt joints
Butt welding
CAE) and Design
Comparative studies
Computer-Aided Engineering (CAD
Engineering
Gas metal arc welding
Gas welding
Industrial and Production Engineering
Inert gas welding
Mechanical Engineering
Media Management
Neural networks
Original Article
Rare gases
Soft computing
Ultimate tensile strength
Welded joints
Back-propagation neural network
Ant colony optimization
PMIGW
Weld strength
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Summary:In this work, a pulsed metal inert gas welding (PMIGW) process is modeled by using a hybrid soft computing technique. Ant colony optimization (ACO) and back-propagation neural network (BPNN) models are combined to predict the ultimate tensile strength of butt-welded joints. A large number of experiments have been conducted, and comparative study shows that the hybrid neuro ant colony-optimized model produces faster and also better weld-joint strength prediction than the conventional back propagation model.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-008-1517-2