@article{ajmo2018612,
author={{Oluwasegun, K.M. and Ojo, O.A and Ola, O.T and Birur, A. and Cuddy, J. and Chan, K.},
title={Development of Artificial Neural Network Models for Predicting Weld Output Parameters in Advanced Fusion Welding of a Magnesium Alloy},
journal={American Journal of Modeling and Optimization},
volume={6},
number={1},
pages={18--34},
year={2018},
url={http://pubs.sciepub.com/ajmo/6/1/2},
issn={2333-1267},
abstract={This paper describes the development of artificial neural network (ANN) models and multi-response optimization technique to predict and select the best welding parameters during Hybrid Laser Arc Welding (HLAW), Hot Wire Cladding (HWC) and Cold Metal Transfer (CMT) of ZE41-T5 alloy. To predict the performance characteristics, namely; weld depth, underfill, percentage defect and total accumulated pore length, artificial neural network models were developed using Levenberg-Marquardt algorithm. ZE41-T5 was selected as the material to be welded with AZ61 alloy as filler material. Experiments were planned using a 3-factor central composite design and were performed under different welding conditions of laser power, travel speed, wire feed rate, current and frequency. The responses were optimized concurrently using ANN Levenberg-Marquardt algorithm. Finally, experimental confirmations were carried out to identify the effectiveness of ANN. A good agreement was obtained between the experimental output data and ANN predicted results.},
doi={10.12691/ajmo-6-1-2}
publisher={Science and Education Publishing}
}