Optimization of the Ductile Properties of an Arc Welded Plate Based on the Yield Strength, Elongation and Modulus of Elasticity.

Authors

Department of Mechanical & Production Engineering, Faculty of Engineering, Delta State University. Nigeria.

10.22094/joie.2021.1903358.1760

Abstract

As a means of controlling the setback associated with the ductile properties of the welded joint, the optimal ductile properties of a mild steel weld were studied based on empirical data generated using the metal inert gas (MIG) welding process with specific references to the yield strength, percentage elongation, poisson ratio and modulus of elasticity using the Response Surface Methodology (RSM) and Genetic Algorithm (GA). The results judging from the remarkable quality of the ductility of the weld reveals the adequacy of the yield strength, poisson ratio, and percentage elongation as key determinant in ascertaining the ductility of a weld as against the tensile strength which have been widely used in previous studies. Further analysis to determine the optimal ductile properties using the optimization techniques generated two different results which were further compared by means of a confirmatory test. The GA results recorded a more accurate optimal responses compared to the RSM having a yield strength of 270.28N/mm2, 31.01% percentage elongation, 0.359 poisson ratio, and modulus of elasticity of 1660.3N/mm2. The results not withstanding their differences reveals that manufacturers can obtain the optimal ductile weld properties using the GA and RSM techniques if the right combination of process parameters is made.

Graphical Abstract

Optimization of the Ductile Properties of an Arc Welded Plate Based on the Yield Strength, Elongation and Modulus of Elasticity.

Highlights

  • The production of strong and durable connections between materials has been made possible by means of the different welding processes. However, the output of the different joining processes is complicated by certain drawbacks such as poor mechanical and ductile properties, which invariably leads to failure of mechanical components, and increased cost of production etc. This study evaluates the significant effect of response parameters such as yield strength, percentage elongation and modulus of elasticity along with the application of two different optimization techniques such as the response surface methodology (RSM) and genetic algorithm (GA)in determining the optimal ductile properties of a gas metal arc welded plate.
  • From the results obtained, the significant input of the bevel angle in designing the weld joint was established. Also from the results obtained, the uniqueness of both techniques as well as the suitability of the GA techniques over the RSM in determining near optimal parameters of the weld responses (yield strength, percentage elongation, Poisson Ratio and modulus of elasticity) was established. However, with the response surface methodology (RSM) the significant contributions and the interactive relationship of model terms were made possible.

Keywords


Abhyankar, A.A, Nikam, D.M, Pawar, S.S. and Shah J (2016) An Experimental Study Of Arc Welding Parameters for Ultimate Strength in Bending and Hardness on DMR 249a and Optimization. International Journal of Mechanical and Production Engineering, ISSN: 2320-2092, Volume- 4, Issue-7.
Amir Fatehi-Kivi, Esmaeil Mehdizadeh, Reza Tavakkoli-Moghaddam, Esmaeil Najafia (2021)Solving a Multi-Item Supply Chain Network Problem by Three Meta-heuristic Algorithms Journal of Optimization in Industrial Engineering Vol.14, Issue 2, Summer & Autumn 2021, 145-151.
Amit Pal (2015) MIG Welding Parametric Optimization Using Taguchi’s Orthogonal Array and Analysis of Variance. International Journal of Research Review in Engineering Science & technology (issn 2278–6643) vol 4 (1), issue-1.
Becker, W.T., and Shipley R.J., (2002) ASM Handbook, vol 11: Failure Analysis and Prevention, ASM International, Materials Park, Ohio.
Björk T., Toivonen J., Nykänen T, 2012. Capacity of fillet welded joints made of ultra-high strength steel. Welding in the world. DOI.10.1007/BF03321337.’
Cemal Meran, (2006) “Prediction of the optimized welding parameters for the joined brass plates using genetic algorithm,” Journal materials & design, vol.27, pp.356-363.
Choudhary S. and Duhan R. (2015), Effect of activated flux on properties of SS 304 using TIG welding. International Journal of Engineering Transactions B: Applications, 28(2), 290–295.
Collin P., Johansson B.,2005, ‘Design of welds in high strength steels,’ Proceedings of the 4th European Conference on Steel and Composite Structures, Maastricht, Volume C. Rafael Picón R., Cañas J.,2009. On strength criteria of fillet welds, International journal of Mechanical Sciences 51 pp 609- 618.doi:10.1016/j.matdes.2012.03.048.
Cornell, J. A. (1990), How to Apply Response Surface Methodology, Volume 8, American Society for Quality Control Press.
Correia, D.S., Gonçalves, C.V., Sebastião S., C., and Ferraresi, V.A., (2004) GMAW Welding Optimization Using Genetic Algorithms. Journal of the Brazilian. Society of Mechanical Science. & Engineering. Vol. 16, No. 1 / 29.
Griffith A.A., The phenomena of rupture and flow in solids, Linear Networks and Systems (Book style) (Wadsworth, Belmont, CA, 1993), pp. 123–135.
Haragopal, G., Reddy, P., V., Reddy, G., C., M. and Subrahmanyam, J., V. (2011) "Parameter design for MIG Welding ofAL-65032alloy using Taguchi technique. Journal of Scientific and Industrial Research, vol 70, pp.844-850.
Javadian Kootanaee, A., Poor Aghajan, A., Hosseini Shirvani, M. (2021). A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements. Journal of Optimization in Industrial Engineering, 14(2), 183-201.
Kadaganchi R., Gankidi, M.R., and Gokhale, H. (2015) Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology. Defence Technology 1-11.
Kumar S. (2010). Experimental investigation on pulsed TIG welding of aluminium plate. Advanced Engineering Technology, 1(2), 200 - 211.
Kathleen, M. C, Natalia, Y. K. and Kamnwa J. R. (2004) Response Surface Methodology. CASOS Technical Report.
Kim, D. and Rhee, S., (2001), “Optimization of Arc Welding Process Parameters using a Genetic Algorithm”, Welding Journal, July, pp. 184-189.
Mahadevi, D. and Manikandan, M. (2014); Optimization of Process parameter of TIG welding of AZ61 Mg Alloy. International Journal of Innovative Research in Science, Engineering and Technology. Volume 3, Special Issue 3.
Montgomery, D. C. (2005). Design and Analysis of Experiments: Response Surface Method and Design. 3rd Edition, Wiley. NY, 55-60.
Myers, R.H. and Montgomery, D.C. (2002). Response Surface Methodology: Process and Product Optimization using Designed Experiments. Wiley-Interscience Publication, NY.
Monika, J., k. and Jagdip Chauhan (2017). A Review Paper on Gas Metal Arc Welding (GMAW) of Mild Steel 1018 by using Taguchi Technique. International Journal of Current Engineering and Scientific Research (IJCESR), Volume-4, Issue-7, 2017 pp 57-62.
Nabendu, G., Pradip Kumar P. and Goutam N. (2017) Parametric Optimization Of Mig Welding On 316l Austenitic Stainless Steel By Taguchi Method International Journal of Mechanical Engineering and Robotics Research Vol. 6, No. 2
Nathan, S.R., Balasubramanian V., Malarvizhi S., and Rao, A.G. (2015) Effect of welding processes on mechanical and microstructural characteristics of high strength low alloy naval grade steel joints. Defence Technology 11; 308-317.
Palani, P., K. and Saju, M., et al, (2013) "Modelling and Optimization of Process Parameters for TIG Welding of Aluminium-65032 Using Response Surface Methodology. "International Journal of Engineering Research and Applications, 3 (2): 230-236.
Rao R. V., (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal Industrial Engineering Computations; 7 (1): 19-34.
Rohit, Jha and Jha. A.K. (2014) Influence of Welding Current and Joint Design on the Tensile Properties of SMAW Welded Mild Steel Joints. Int. Journal of Engineering Research and Applications, Vol. 4, Issue 6 (Version 4), June 2014, pp.106-111
Sada, S. O. (2018).Optimization of weld strength properties of tungsten inert gas mild steel welds using the response surface methodology. Nigeria Journal of Technology, 37 (2), 407–415. https://doi.org/10.4314/njt.v37i2.15
Sada, S. O. | (2020) The use of multi-objective genetic algorithm (MOGA) in optimizing and predicting weld quality, Cogent Engineering, 7:1, 1741310
Sada, S.O. and Achebo, J. (2020): Optimization and Prediction of the Weld Bead Geometry of a Mild Steel Metal Inert Gas Weld. Advances in Materials and Processing Technologies. doi.org/10.1080/2374068X.2020.1860597
Sada, S. O. (2018) Parametric Optimization of Weld Reinforcements using Response Surface Methodology Optimization Process. Journal of Applied Science and Environmental. Management. Vol. 22 (8) 1331 –1335
Satnam, S.S., Navdeep, S.T., Tejwant S.G, and Gurpreet S.T. (2015) Comparative Analysis of Tensile Strength and Percentage Elongation in Submerged Arc Welding Using Different Slag-Flux Ratios. International Journal of Innovative Research in Science, Engineering and Technology Vol. 4, Issue 9.
Sette, S., Boullart, L. and Langenhove, L., 1996, “Optimising a Production Process by a Neural Network/Genetic Algorithm Approach”, Engineering Application of Artificial Intelligence, Vol. 9, No. 6, pp. 681-689.
Sittichai, K., Santirat, N. and Sompong, P. 2012.  “A study of gas metal arc welding affecting mechanical properties of austenitic stainless steel AISI 304,” World Academy of Science, Engineering and Technology, vol. 61, pp. 402–405.
Somers, R., B. and Pense, W., A. (1994) Welding Failure Analysis. Materials Characterization, vol 33(3) pp 295-309.
Stenberg, T., Barsoum, Z., Astrand, E., (2017) Quality control and assurance in fabrication of welded structures subjected to fatigue loading. Weld in the World 61, 1003-1015.
Zuheir Barsoum and Mansoor kurshid (2017) Ultimate Strength Capacity of Welded Joint in High Speed Steel/ Procedia Structural Integrity 5; 1401–1408