Abd Elsalam, A., ElAtriby, S., Barakat, A., Abdel Ghany, M. (2018). MODELING OF THE WELD BEAD SHAPE AT DIFFERENT WELDING PARAMETERS. test, 18(18th International Conference on Applied Mechanics and Mechanical Engineering.), 1-11. doi: 10.21608/amme.2018.34960
A. A. Abd Elsalam; S. ElAtriby; A. F. Barakat; M. Abdel Ghany. "MODELING OF THE WELD BEAD SHAPE AT DIFFERENT WELDING PARAMETERS". test, 18, 18th International Conference on Applied Mechanics and Mechanical Engineering., 2018, 1-11. doi: 10.21608/amme.2018.34960
Abd Elsalam, A., ElAtriby, S., Barakat, A., Abdel Ghany, M. (2018). 'MODELING OF THE WELD BEAD SHAPE AT DIFFERENT WELDING PARAMETERS', test, 18(18th International Conference on Applied Mechanics and Mechanical Engineering.), pp. 1-11. doi: 10.21608/amme.2018.34960
Abd Elsalam, A., ElAtriby, S., Barakat, A., Abdel Ghany, M. MODELING OF THE WELD BEAD SHAPE AT DIFFERENT WELDING PARAMETERS. test, 2018; 18(18th International Conference on Applied Mechanics and Mechanical Engineering.): 1-11. doi: 10.21608/amme.2018.34960

MODELING OF THE WELD BEAD SHAPE AT DIFFERENT WELDING PARAMETERS

The International Conference on Applied Mechanics and Mechanical Engineering
Article 24, Volume 18, 18th International Conference on Applied Mechanics and Mechanical Engineering., April 2018, Pages 1-11    PDF (513.69 K)
Document Type: Original Article
DOI: 10.21608/amme.2018.34960
Authors
A. A. Abd Elsalam1; S. ElAtriby2; A. F. Barakat3; M. Abdel Ghany4
1Assistant Lecturer at Mechanical Engineering Dept., Faculty of Engineering, Helwan University, Cairo, Egypt.
2Lecturer at Mechanical Engineering Dept., Faculty of Engineering, Helwan University, Cairo, Egypt.
3Professor at Mechanical Engineering Dept., Faculty of Engineering, Helwan University, Cairo, Egypt.
4Professor at Electrical Engineering Dept., Faculty of Engineering Helwan University, Cairo, Egypt.
Abstract
ABSTRACT
This paper tends to discuss the relation between welding parameters (Voltage, Arc
Current and Travel Speed) and the deposited lines shape (width, height and
penetration) in metal deposition process. Due to the high nonlinearity of the process
ANN (artificial neural network) was found to be the best chose for representing it.
ANN is trained off-line under different operating conditions then used for prediction of
the system model for further optimization of the process. The results show the
capability of the developed ANNs to represent the process properly. Supervised
learning with back propagation technique was used to make the networks. Best
network for width consisted of tansig functions with an output layer of linear function.
Traingd function gave the best result for width. Best network for penetration consisted
of tansig functions with an output layer of linear function. Traingd function gave the
best result for penetration. Best network for height consisted of mixture of radial
basis, tansig functions with an output layer of linear function. Traingd function gave
the best result for height.
Keywords
ANN; MIG; GMAW; Shape of weld line
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