Modeling And Prediction of Mechanical Strength in Electron Beam Welded Dissimilar Metal Joints of Stainless Steel 304 and Copper Using Grey Relation Analysis
-
2018-07-04 https://doi.org/10.14419/ijet.v7i3.6.14969
-
Dissimilar metal joints, electron beam welding, mechanical strength, grey relation analysis. -
Abstract
Aircraft industries witness an extensive variety of utilizations in unique welded joints thinking about the benefit of quality and high corrosion protection. In any case, joining of dissimilar materials is more mind boggling because of the distinction in material properties. In this investigation dissimilar metal joints of pure Copper plates and Stainless Steel 304 plates of 3mm thickness were welded with Electron Beam Welding. The welding input parameters like Welding speed, Beam current and Work distance liable to quality of weld are considered. Plan of analysis has been made utilizing Taguchi strategy with three levels of input values. Ultimate tensile strength and hardness number were found to decide the mechanical quality. Both the yield esteems are consolidated for expectation and optimized using Gray Relation Analysis (GRA). The impacts of the input parameters towards weld quality were analyzed using ANOVA.
Â
-
References
[1] Yang LJ, Bibby MJ & Chandel RS, “Linear Regression Equations for Modeling the Submerged-Arc Welding Processâ€, Journal of Material Processing Technology, vol.39, (1993), pp.33–42.
[2] Ganjigatti JP, Pratihar DK & Roy Choudhury A, “Global Versus Cluster-Wise Regression Analyses for Prediction of Bead geometry in MIG Welding Processâ€, Journal of Material Processing Technology, vol.189, (2007), pp.352–366.
[3] Nagesh DS & Datta GL, “Prediction of weld Bead Geometry and Penetration in Shielded Metal-Arc Welding Using artificial Neural Networksâ€, Journal of Material Processing Technology, vol.123, (2002), pp.303–312.
[4] De A, Jantre J & Ghosh PK, “Prediction of Weld Quality in Pulsed Current GMAW Process Using Artificial Neural Networkâ€, Science and Technology of Welding and Joining, vol.9, no.3, (2004), pp.253–259.
[5] Kim IS, Son JS, Park CE, Lee CW & Prasad YKDV, “A Study on Prediction of Bead Height in Robotic Arc Welding Using a Neural Networkâ€, Journal of Material Processing Technology, (2002), pp.229–234.
[6] Lee JI & Um KW, “A Prediction of Welding Parameters by Prediction of back-Bead Geometryâ€, Journal of Material Processing Technology, vol.108, (2000), pp.106–113.
[7] Tay KM & Butler C, “Modelling and Optimizing of a MIG Welding Process a Case Study Using Experimental Designs and Neural Networksâ€, International Conference on Quality and Reliability Engineering, vol. 13, (1997), pp.61–70.
[8] Benyounis KY, Olabi AG & Hashmi MSJ, “Effect of Laser Welding parameters on the Heat Input and Weld-Bead Profileâ€, Journal of Material Processing Technology, (2005), pp.978–985.
[9] Gunaraj V & Murugan N, “Prediction of heat-affected zone characteristics in submerged arc welding of structural steel pipesâ€, Welding Journal-New York, Vol.81, No.3, (2002).
[10] Taguchi G, Introduction to Quality Engineering, Asian Productivity Organization, Tokyo, (1990).
[11] Tarng YS & Yang WH, “Optimisation of the Weld Bead Geometry in Gas Tungsten Arc Welding by the Taguchi Methodâ€, International Journal of Advanced Manufacturing Technology, vol.14, (1998), pp.549–554.
[12] Tarng YS, Juang SC & Chang CH, “The Use of Grey-Based Taguchi Methods to Determine Submerged Arc Welding Process Parameters in Hard-Facingâ€, Journal of Material Processing Technology, vol.128, (2002), pp.1–6.
[13] Ajith Raj R, Rohith IJ & Dev Anand M, “Mechanical Strength Prediction of TIG Welded Stainless Steel 304 Using Grey RSMâ€, International Journal of Mechanical Engineering and Technology, vol.8, (2017), pp.840–847.
[14] Metzger G & Lison R, “Electron Beam Welding of Dissimilar Metalsâ€, Welding Research Supplement, (1976), pp.230-240.
[15] Rai R, Palmer TA, Elmer JW & Debroy T, “Heat Transfer and Fluid Flow during Electron Beam Welding of 304L Stainless Steel Alloyâ€, Welding Journal, vol.88, (2009).
[16] Miroslav S, Martin S, Milan T & Paulina Z, “Disk Laser Welding of Copper to Stainless Steelâ€, Advanced Materials Research vol. 1077, (2015).
[17] Kanaujia KK, Rout MP, Behera BC, Sahoo SK & Maharana BK, “Optimization of Tensile Strength of AISI304 Stainless Steel and Copper using Nd: YAG Laser Weldingâ€, Proc. of the 5th International Conference on Advances in Mechanical Engineering (ICAME), pp.06-08, (2011).
[18] Mai TA & Spowage AC, “Characterisation of Dissimilar Joints in Laser Welding of Steel–Kovar, Copper–Steel and Copper–Aluminiumâ€, Materials Science and Engineering, vol.374, (2004), pp.224–233.
[19] Rakesh C, Riddhish P & Asha I, “Reliability of Dissimilar Metal Joints Using Fusion Welding: A Reviewâ€, International Conference on Machine Learning, Electrical and Mechanical Engineering, (2014).
[20] Lacki P, Adamus K, Wojsyk K, Zawadzki M & Nitkiewicz Z, “Modeling of Heat Source Based on Parameters of Electron Beam Welding Processâ€, Archives of Metallurgy and Materials, vol.56, no.2, (2011), pp.455-462.
-
Downloads
-
How to Cite
Ajith Raj, R., & Dev Anand, M. (2018). Modeling And Prediction of Mechanical Strength in Electron Beam Welded Dissimilar Metal Joints of Stainless Steel 304 and Copper Using Grey Relation Analysis. International Journal of Engineering & Technology, 7(3.6), 198-201. https://doi.org/10.14419/ijet.v7i3.6.14969Received date: 2018-07-02
Accepted date: 2018-07-02
Published date: 2018-07-04