Artificial Neural Network Application on Ti/Al Joint Using Laser Beam Welding – A Review
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Artificial Neural Network Application on Ti/Al Joint Using Laser Beam Welding – A Review

Authors: K. Kalaiselvan, A. Elango, N. M. Nagarajan

Abstract:

Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.

Keywords: Laser Beam Welding (LBW), Artificial Neural Networks (ANN), Optimization, Titanium and Aluminium sheets.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094579

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References:


[1] I. Kim., J. Son., S. Lee. and P.K.D.V. Yarlagadda, "Optimal design of neural networks for control in robotic arc welding”, Robotics and Computer-Integrated Manufacturing, (2004), Vol.20, no.1, pp.57– 63.
[2] S. Pal., S.K. Pal. and A.K. Samantaray, "Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals”, Journal of Materials Processing Technology, (2008), Vol. 202, no. 1–3, pp. 464–474.
[3] Malinov S, Sha W, "Application of artificial neural networks for modelling correlations in titanium alloys". Materials Science and Engineering: A, vol.365 no.1–2, pp.202–211, 2004.
[4] S. K. Dhara., A. S. Kuar. and S. Mitra, "An artificial neural network approach on parametric optimization of laser micromachining of die-steel”, International Journal of Advanced Manufacturing Technology, (2008) Vol. 39, no. 1-2, pp. 39–46.
[5] Mohd Idris Shah Ismail., Yasuhiro Okamoto. and Akira Okada, "Neural Network Modeling for Prediction of Weld Bead Geometry in Laser Microwelding”, Advances in Optical Technologies, (2013), Article ID 415837, 7 pages.
[6] Young whan park, "Genetic algorithms and Neural network for process modeling and parameter optimization of aluminium laser welding Automation”, International Journal of Advanced Manufacturing Technology, (2008).
[7] Vasudevan M., Bhaduri A.K., Baldev R., Rao P.K., "Genetic algorithm based computational methods for optimizing the process parameters of a TIG welding to achieve target bead geometry in type 304 L(N) and 316 L (N) stainless steels”, Materials and Manufacturing processes, 22, 641-649, 2007.
[8] J.M. Vitek., Y.S. Iskander, E.M. Oblow et al, "Neural network modeling of pulsed-laser weld pool shapes in aluminum alloy welds”, proceedings of 5th Inter. Conf. on Trends in Welding Research, Pine Mountain, GA, (1998), June 1-5, ASM Inter, pp.442-448.
[9] H. Park. and S.Rhee, "Estimation of weld bead size in Co2 laser welding by using multiple regression and neural network”, J. of Laser Application, (1999), Vol. 11. n. 3, pp.143-150.
[10] Dhavalkumar. and K. Soni, "A Review of Laser Welding Process for Thin Steel Sheets”, (IJRMEET), (2013), ISSN: 2320-6586, Vol. 1, Issue: 3.
[11] Balasubramanian K.R., Bhuvanasekaran G. and Sankaranarayanaswamy K, "Mathematical & ANN Modeling of ND:YAG Laser welding of Thin SS Sheets”, International Journal for the Joining of Materials, (2006), Volume 18 No. 3/4, pp 99-104.
[12] Bhuvanasekaran G., Balasubramanian K.R. and Sankaranarayanaswamy K, "Analysis of Laser welding parameters using Artificial neural network”, International journal for the joining of Materials, (2006), Volume 18 No.3/4, pp 99-104. ISSN 0905-6866.
[13] Banakar Nagaraj Dr. and V.Venkata Ramana, "Analysis and Optimization of Laser Beam Weld Bead Parameters: A Proposal”, International Journal of Mechanical Engineering Research & Applications (IJMERA), (2013), ISSN: 2347-1719, Vol. 1 Issue 4.
[14] G. Casalion, and F.M.C. Minutolo, "A model for evaluation of laser welding efficiency and quality using an artificial neural network and fuzzy logic”, J of Engineering Manufacture, (2004), Vol. 218, part B, pp. 1-6.
[15] Y.H. Wei., H.K.D.H Bhadeshia, and T. Sourmail, "Modeling mechanical properties of welds in plates of commercial titanium alloys”, Transactions of the Nonferrous Metals Society of China, (2005), Vol. 15, 70--74.
[16] S. Malinov., W.Sha. and J.J. McKeown, "Modeling the correlation between processing parameters and properties in titanium alloys using artificial neural network”, Computational materials science, (2001), 21, 375-394.
[17] Z. Sterjovski., D. Nolan., D. Dunne. and J. Norrish, "Predicting the HAZ hardness of pipeline and tap fitting steels with artificial neural networks”, in Proceedings of the 4th Inter. Conf. on pipeline technology, University of Wollongong, NEW, Australia, (2004), pp. 1233-1245.
[18] Z. Sterjovski., D. Nolan., K.R. Carpenter., D.P. Dune. and J.Norrish, "Artificial neural network for modelling the mechanical properties of steels in various applications”, J. of Materials Processing Technology, (2005), Vol. 170, No.3, pp. 336-544.
[19] M. Murugananth., S.S. Babu. and S.A. David, "Optimization of shielded metal arc weld metal composition for charpy toughness”, Welding Journal, AWS, (2004), pp. 267-s-276-s.
[20] Y. Wei., H.K.D. Bhadeshia. and T. Sourmail, "Mechanical property prediction of commercially pure titanium welds with artificial neural network”, J. of Material Science Technology, (2005) Vol. 21, n. 3, pp. 403-407.