1Department of Mechanical Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
2Technology Access Centre for Aerospace and Manufacturing, Red River College of Applied Arts, Science and Technology, Winnipeg, Manitoba, Canada
3Standard Aero Limited, Winnipeg, Manitoba, Canada
4Huys Limited, Toronto, Ontario, Canada
American Journal of Modeling and Optimization.
2018,
Vol. 6 No. 1, 18-34
DOI: 10.12691/ajmo-6-1-2
Copyright © 2018 Science and Education PublishingCite this paper: K.M. Oluwasegun, O.A Ojo, O.T Ola, A. Birur, J. Cuddy, K. Chan. Development of Artificial Neural Network Models for Predicting Weld Output Parameters in Advanced Fusion Welding of a Magnesium Alloy.
American Journal of Modeling and Optimization. 2018; 6(1):18-34. doi: 10.12691/ajmo-6-1-2.
Correspondence to: K.M. Oluwasegun, Department of Mechanical Engineering, University of Manitoba, Winnipeg, Manitoba, Canada. Email:
excetom@gmail.comAbstract
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.
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