Influence of Welding Parameters on Strength of Metal Inert Gas Welded Mild Steel Joints

Authors

  • Washington Odhiambo Obura * Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Multimedia University of Kenya, Nairobi, Kenya. https://orcid.org/0009-0007-5162-0567
  • Abel N. Mayaka Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Multimedia University of Kenya, Nairobi, Kenya.
  • Charles Ondieki Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Multimedia University of Kenya, Nairobi, Kenya.

https://doi.org/10.48314/apem.vi.45

Abstract

Metal Inert Gas (MIG) welding is a type of arc welding that uses a continuous solid wire electrode and a shielding gas to join two metals by heating them with an electric arc. We studied process parameters including current, voltage, preheat temperature, and post-weld heat treatment. We optimize process parameters of experiments done in previous work using a Taguchi Orthogonal Array (L27) design. A grey-based Taguchi method is used to optimize the process parameters. The Analysis of Variance (ANOVA) is applied to assess the significance of the input parameters on the response parameters. A mathematical model is developed using Multiple Linear Regression (MLR) equations. The results of this research show that it is possible to achieve higher strengths in weld joints using Taguchi design. We also find that increasing current (I) and Post-Weld heat Treatment (PWT) temperature increases the strength of the studied welded joints, and vice versa. Future research should validate the findings of the current study through experimental investigations.

Keywords:

Weld parameters, Optimization, Taguchi relational analysis, Metal inert gas welding

Author Biography

  • Abel N. Mayaka, Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Multimedia University of Kenya, Nairobi, Kenya.

     

     

References

  1. [1] Karayel, E., & Bozkurt, Y. (2020). Additive manufacturing method and different welding applications. Journal of materials research and technology, 9(5), 11424–11438. https://doi.org/10.1016/j.jmrt.2020.08.039
  2. [2] Okai, V., Chahul, H. F., Wuana, R. A., Barnabas, I. A., & Tolufashe, G. F. (2021). Corrosion inhibition potentials of Cucurbita polyesteramide urethane on mild steel in hydrochloric acid medium: Experimental and computational studies. Scientific african, 12, e00776. https://doi.org/10.1016/j.sciaf.2021.e00776
  3. [3] Kumar, S., & Singh, R. (2019). Optimization of process parameters of metal inert gas welding with preheating on AISI 1018 mild steel using grey based Taguchi method. Measurement, 148, 106924. https://doi.org/10.1016/j.measurement.2019.106924
  4. [4] Tabatabaeipour, S. M., & Honarvar, F. (2010). A comparative evaluation of ultrasonic testing of AISI 316L welds made by shielded metal arc welding and gas tungsten arc welding processes. Journal of materials processing technology, 210(8), 1043–1050. https://doi.org/10.1016/j.jmatprotec.2010.02.013
  5. [5] Norrish, J., Polden, J., & Richardson, I. (2021). A review of wire arc additive manufacturing: Development, principles, process physics, implementation and current status. Journal of physics d: Applied physics, 54(47), 473001. https://dx.doi.org/10.1088/1361-6463/ac1e4a
  6. [6] Gyasi, E. A. (2019). Welding processes of metals for offshore environment: Underwater welding. https://lutpub.lut.fi/handle/10024/160103
  7. [7] Kumar, N., Sherlock, R., & Tormey, D. (2019). Prediction of weld interface depth and width at optimum laser welding temperature for Polypropylene. Procedia cirp, 81, 1272–1277. https://doi.org/10.1016/j.procir.2019.03.306
  8. [8] Yalamanchili, V. K., Galindo, D. A., & Mach, J. C. (2018). Robust virtual welding process optimization. Procedia computer science, 140, 342–350. https://doi.org/10.1016/j.procs.2018.10.305
  9. [9] Arumugam, A., & Pramanik, A. (2020). Review of experimental and finite element analyses of spot weld failures in automotive metal joints. Jordan journal of mechanical & industrial engineering, 14(3).
  10. [10] Das, D., Jaypuria, S., Pratihar, D. K., & Roy, G. G. (2021). Weld optimisation. Science and technology of welding and joining, 26(3), 181–195. https://doi.org/10.1080/13621718.2021.1872856
  11. [11] Zhu, C., Tang, X., He, Y., Lu, F., & Cui, H. (2018). Effect of preheating on the defects and microstructure in NG-GMA welding of 5083 Al-alloy. Journal of materials processing technology, 251, 214–224. https://doi.org/10.1016/j.jmatprotec.2017.08.037
  12. [12] Arunkumar, S. P., Prabha, C., Saminathan, R., Khamaj, J. A., Viswanath, M., Paul Ivan, C. K., … ., & Kumar, P. M. (2022). Taguchi optimization of metal inert gas (MIG) welding parameters to withstand high impact load for dissimilar weld joints. Materials today: Proceedings, 56, 1411–1417. https://doi.org/10.1016/j.matpr.2021.11.619
  13. [13] Jawad, M., Jahanzaib, M., Ali, M. A., Farooq, M. U., Mufti, N. A., Pruncu, C. I., … ., & Wasim, A. (2021). Revealing the microstructure and mechanical attributes of pre-heated conditions for gas tungsten arc welded AISI 1045 steel joints. International journal of pressure vessels and piping, 192, 104440. https://doi.org/10.1016/j.ijpvp.2021.104440
  14. [14] Pradhan, R., Joshi, A. P., Sunny, M. R., & Sarkar, A. (2022). Performance of predictive models to determine weld bead shape parameters for shielded gas metal arc welded T-joints. Marine structures, 86, 103290. https://doi.org/10.1016/j.marstruc.2022.103290
  15. [15] Tafarroj, M. M., Moghaddam, M. A., Dalir, H., & Kolahan, F. (2021). Using hybrid artificial neural network and particle swarm optimization algorithm for modeling and optimization of welding process. Journal of advanced manufacturing systems, 20(04), 783–799. https://doi.org/10.1142/S0219686721500384
  16. [16] Srinivas, K., Vundavilli, P. R., & Manzoor Hussain, M. (2020). Weld quality prediction of paw by using pso trained RBFNN. Advances in materials and manufacturing engineering (pp. 433–439). Springer Singapore. https://doi.org/10.1007/978-981-15-1307-7_48
  17. [17] Abima, C. S., Akinlabi, S. A., Madushele, N., Fatoba, O. S., & Akinlabi, E. T. (2022). Multi-objective optimization of process parameters in TIG-MIG welded AISI 1008 steel for improved structural integrity. The international journal of advanced manufacturing technology, 118(11), 3601–3615. https://doi.org/10.1007/s00170-021-08181-1
  18. [18] Aftab, B., & Mishra, Y. (2020). Optimisation of process parameters for MIG welding byusing grey relational analysis. International journal of scientific research & engineering trends, 6(4), 2245–2250.
  19. [19] Osman, M. H., Nasrudin, N. F., Shariff, A. S., Wahid, M. K., Ahmad, M. N., Maidin, N. A., … ., & Rahman, M. H. A. (2021). Experimental study of single pass welding parameter using robotic metal inert gas (MIG) welding process. Advances in mechatronics, manufacturing, and mechanical engineering (pp. 10–21). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-7309-5_2
  20. [20] Gidde, R. R. (2020). Design optimization of micromixer with circular mixing chambers (M-CMC) using Taguchi-based grey relational analysis. International journal of chemical reactor engineering, 18(9), 20200057. https://doi.org/10.1515/ijcre-2020-0057
  21. [21] Rana, M., Singh, T., Sharma, V. K., Saini, A., & Singh, J. (2022). Optimization of surface integrity in face milling of AISI 52,100 alloy steel using Taguchi based grey relational analysis. Materials today: Proceedings, 50, 2105–2110. https://doi.org/10.1016/j.matpr.2021.09.430
  22. [22] Yadav, P., & Khanna, P. (2022). Effect of input parameters on weld bead geometry and weld dilution for weld surfacing of flux cored 308L stainless steel on low carbon steel. Materials today: Proceedings, 62, 3608–3616. https://doi.org/10.1016/j.matpr.2022.04.412
  23. [23] Meena, S. L., Butola, R., Khan, M. A., Walia, R. S., & Murtaza, Q. (2023). Influence of process parameters in synergic MIG welding of 304L stainless steel using response surface methodology. Advances in materials and processing technologies, 9(1), 196–205. https://doi.org/10.1080/2374068X.2022.2091090
  24. [24] Ogbeide, O. O., Akeredolu, K., & Omotehinse, S. A. (2022). Optimization of tensile strength of butt joint weldment on mild steel plate using response surface methodology. Journal of applied research on industrial engineering, 9(1), 50–58. https://doi.org/10.22105/jarie.2021.297079.1362
  25. [25] Adak, D. K., Senapati, D., & Dutta, P. (2022). Parameters optimisation for submerged arc welding of mild steel weld bead geometry using response surface methodology. Journal of mechanics of continua and mathematical sciences, 17(8), 34–47.
  26. [26] Pathak, D., Kumar, D., Singh, R. P., & Balu, V. (2022). Optimization of process variables for prediction of penetration depth of HSLA steel welds using response surface methodology. Key engineering materials, 934, 119–128. https://www.scientific.net/KEM.934.119
  27. [27] M. T. A. Al-basheer. (2019). Design and development of an automated metal inert gas/metal active gas welding machine [Thesis].
  28. [28] Assefa, A. T., Ahmed, G. M. S., Alamri, S., Edacherian, A., Jiru, M. G., Pandey, V., & Hossain, N. (2022). Experimental investigation and parametric optimization of the tungsten inert gas welding process parameters of dissimilar metals. Materials, 15(13), 4426. https://doi.org/10.3390/ma15134426
  29. [29] Meseguer-Valdenebro, J. L., Portoles, A., & Matínez-Conesa, E. (2018). Electrical parameters optimisation on welding geometry in the 6063-T alloy using the Taguchi methods. The international journal of advanced manufacturing technology, 98(9), 2449–2460. https://doi.org/10.1007/s00170-018-2395-x
  30. [30] Mallick, B., Hameed, A. S., Al Suaidy, M. S. J., & Halder, K. (1986). Experimental investigation for multi characteristics optimization of MIG welding on 304 stainless steel using desirability function analysis. List of special numbers published (1986-2019), 1986(3), 144.
  31. [31] Zhao, D. Q., Pan, S. P., Zhang, Y., Liaw, P. K., & Qiao, J. W. (2021). Structure prediction in high-entropy alloys with machine learning. Applied physics letters, 118(23). https://doi.org/10.1063/5.0051307

Downloads

Published

2025-10-23

Issue

Vol. 2 No. 4 (2025): Annals of Process Engineering and Management

Section

Articles

How to Cite

Obura, W. O., Mayaka, A. N., & Ondieki, C. (2025). Influence of Welding Parameters on Strength of Metal Inert Gas Welded Mild Steel Joints. Annals of Process Engineering and Management, 2(4), 211-225. https://doi.org/10.48314/apem.vi.45