Research Article
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Year 2025, Volume: 9 Issue: 2, 103 - 113, 20.06.2025
https://doi.org/10.26701/ems.1621888

Abstract

References

  • Baskoro, A. S., Hidayat, R., Widyianto, A., Amat, M. A., & Putra, D. U. (2020). Optimization of Gas Metal Arc Welding (GMAW) Parameters for Minimum Distortion of T Welded Joints of A36 Mild Steel by Taguchi Method. Materials Science and Engineering, 1000, 356–363. doi: 10.4028/www.scientific.net/msf.1000.356
  • Ramarao, M., King, M. F., Sivakumar, A., Manikandan, V., Vijayakumar, M., & Subbiah, R. (2022). Optimizing GMAW parameters to achieve high impact strength of the dissimilar weld joints using Taguchi approach. Materials Today: Proceedings, 50, 861–866.
  • Adin, M. Ş., & İşcan, B. (2022). Optimization of process parameters of medium carbon steel joints joined by MIG welding using Taguchi method. European Mechanical Science, 6(1), 17–26.
  • Madavi, K. R., Jogi, B. F., & Lohar, G. S. (2022). Metal inert gas (MIG) welding process: A study of effect of welding parameters. Materials Today: Proceedings, 51, 690–698.
  • Titinan, M., Masaya, S., Manabu, T., Rinsei, I., Muneo, M., & Tokihiko, K. (2017). Diagnostic of heat source characteristics in gas metal arc welding using CO2 shielding gas. 溶接学会論文集, 35(2), 103s–107s.
  • Narwadkar, A., & Bhosle, S. (2016). Optimization of MIG welding parameters to control the angular distortion in Fe410WA steel. Materials and Manufacturing Processes, 31(16), 2158–2164.
  • Sankar, B. V., Lawrence, I. D., & Jayabal, S. (2018). Experimental study and analysis of weld parameters by GRA on MIG welding. Materials Today: Proceedings, 5(6), 14309–14316.
  • Pal, A. (2015). MIG welding parametric optimisation using Taguchi’s orthogonal array and analysis of variance. International Journal of Research Review in Engineering Science and Technology, 4(1), 211–217.
  • Kumar, P., & Roy, B. K. (2013). Parameters Optimization for Gas Metal Arc Welding of Austenitic Stainless Steel (AISI 304) & Low Carbon Steel using Taguchi’s Technique. International Journal of Engineering and Management Research, 3(4), 18–22.
  • Palani, P. K., Murugan, N., & Karthikeyan, B. (2006). Process parameter selection for optimising weld bead geometry in stainless steel cladding using Taguchi’s approach. Materials Science and Technology, 22(10), 1193–1200.
  • Montgomery, D. C. (2006). Design and analysis of experiments (4. baskı). New York: John Wiley & Sons.
  • Ross, P. J. (1988). Taguchi techniques for quality engineering. New York: Tata McGraw Hill.
  • Park, S. H. (1996). Robust design and analysis for quality engineering (1. baskı). Londra: Chapman & Hall.
  • Ross, P. J. (1996). Taguchi techniques for quality engineering. New York: McGraw-Hill.
  • Belavendran, N. (1995). Quality by design. Londra: Prentice Hall.
  • Yang, W. H., & Tarng, Y. S. (1998). Design optimization of cutting parameters for turning operations based on the Taguchi method. Journal of Materials Processing Technology, 84, 122–129.
  • Songa, Y. A., Parka, S., & Chaeb, S. W. (2005). Optimization of the injection molding process for part warpage using Taguchi method. International Journal of Machine Tools & Manufacture, 45(1), 1–7.
  • Syrcos, G. P. (2003). Die casting process optimization using Taguchi methods. Journal of Materials Processing Technology, 135, 68–74.
  • Gadakh, V. S., Shinde, V. B., & Khemnar, N. S. (2013). Optimization of welding process parameters using MOORA method. The International Journal of Advanced Manufacturing Technology, 69, 2031–2039.
  • Brauers, W. K. M. (2004). Optimization methods for a stakeholder society: A revolution in economic thinking by multiobjective optimization. Boston: Kluwer Academic.
  • Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi-objective decision-making for road design. Transport, 23, 183–193.
  • Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi-objective optimization of road design alternatives with an application of the MOORA method. Proceedings of the 25th International Symposium on Automation and Robotics in Construction, Lithuania, 541–548.
  • Brauers, W. K. M. (2008). Multi-objective contractor’s ranking by applying the MOORA method. Journal of Business Economics and Management, 4, 245–255.
  • Brauers, W. K. M., & Zavadskas, E. K. (2009). Robustness of the multiobjective MOORA method with a test for the facilities sector. Technological and Economic Development of Economy, 15(2), 352–375.
  • Kalibatas, D., & Turskis, Z. (2008). Multicriteria evaluation of inner climate by using MOORA method. Information Technology and Control, 37, 79–83.
  • Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24.
  • Lootsma, F. A. (1999). Multi-criteria decision analysis via ratio and difference judgement. Londra: Springer.
  • Qazi, M. I., Akhtar, R., Abas, M., Khalid, Q. S., Babar, A. R., & Pruncu, C. I. (2020). An integrated approach of GRA coupled with principal component analysis for multi-optimization of shielded metal arc welding (SMAW) process. Materials, 13(16), 3457.
  • Stephens, M. A. (1974). EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69, 730–737.
  • Seong, W.-J. (2019). Prediction and characteristics of angular distortion in multi-layer butt welding. Materials, 12, 1435.
  • Armentani, E., Esposito, R., & Sepe, R. (2007). The influence of thermal properties and preheating on residual stresses in welding. International Journal of Computational Materials Science and Surface Engineering, 1, 146–162.
  • Ramasamy, N., Jeyasimman, D., Kathiravan, R., & Raju, N. (2019). Influence of Welding Sequence on Residual Stresses Induced in As-Welded Plug Weld of Low-Carbon Steel Plate. Transactions of the Indian Institute of Metals, 72, 1361–1369.
  • Chate, G. R., Patel, G. M., Kulkarni, R. M., Vernekar, P., Deshpande, A. S., & Parappagoudar, M. B. (2018). Study of the effect of nano-silica particles on resin-bonded moulding sand properties and quality of casting. Silicon, 10, 1921–1936.

Data-driven optimization of MIG welding: A synergistic approach for superior joint quality

Year 2025, Volume: 9 Issue: 2, 103 - 113, 20.06.2025
https://doi.org/10.26701/ems.1621888

Abstract

A data-driven approach was applied in this research to determine input parameters for producing high-quality welds in mild steel sheets. By utilizing an L16 orthogonal array, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) techniques were utilized to optimize weld characteristics. The Multi-Objective Optimization based on Ratio Analysis (MOORA) method was used to rank these conflicting objectives according to their importance in different scenarios. From principal component analysis (PCA), setting the voltage at 42V, welding current at 250A, wire feed rate at 8 mm/min, and gas flow rate at 15 L/min results in ideal characteristics: penetration of 2.961 mm, reinforcement of 5.658 mm, bead width of 12.753 mm, and dilution percentage of 4.183%. Through the MOORA method, it was determined that a voltage of 40V, welding current of 175A, wire feed rate of 4 mm/min, and gas flow rate of 10 L/min would yield optimal weld bead geometry with penetration of 0.884 mm, reinforcement of 6.489 mm, bead width of 11.715 mm, and dilution percentage of 1.218%. This study effectively optimized welding parameters for superior welding in sheet metal fabrication for small and medium-sized enterprises.

References

  • Baskoro, A. S., Hidayat, R., Widyianto, A., Amat, M. A., & Putra, D. U. (2020). Optimization of Gas Metal Arc Welding (GMAW) Parameters for Minimum Distortion of T Welded Joints of A36 Mild Steel by Taguchi Method. Materials Science and Engineering, 1000, 356–363. doi: 10.4028/www.scientific.net/msf.1000.356
  • Ramarao, M., King, M. F., Sivakumar, A., Manikandan, V., Vijayakumar, M., & Subbiah, R. (2022). Optimizing GMAW parameters to achieve high impact strength of the dissimilar weld joints using Taguchi approach. Materials Today: Proceedings, 50, 861–866.
  • Adin, M. Ş., & İşcan, B. (2022). Optimization of process parameters of medium carbon steel joints joined by MIG welding using Taguchi method. European Mechanical Science, 6(1), 17–26.
  • Madavi, K. R., Jogi, B. F., & Lohar, G. S. (2022). Metal inert gas (MIG) welding process: A study of effect of welding parameters. Materials Today: Proceedings, 51, 690–698.
  • Titinan, M., Masaya, S., Manabu, T., Rinsei, I., Muneo, M., & Tokihiko, K. (2017). Diagnostic of heat source characteristics in gas metal arc welding using CO2 shielding gas. 溶接学会論文集, 35(2), 103s–107s.
  • Narwadkar, A., & Bhosle, S. (2016). Optimization of MIG welding parameters to control the angular distortion in Fe410WA steel. Materials and Manufacturing Processes, 31(16), 2158–2164.
  • Sankar, B. V., Lawrence, I. D., & Jayabal, S. (2018). Experimental study and analysis of weld parameters by GRA on MIG welding. Materials Today: Proceedings, 5(6), 14309–14316.
  • Pal, A. (2015). MIG welding parametric optimisation using Taguchi’s orthogonal array and analysis of variance. International Journal of Research Review in Engineering Science and Technology, 4(1), 211–217.
  • Kumar, P., & Roy, B. K. (2013). Parameters Optimization for Gas Metal Arc Welding of Austenitic Stainless Steel (AISI 304) & Low Carbon Steel using Taguchi’s Technique. International Journal of Engineering and Management Research, 3(4), 18–22.
  • Palani, P. K., Murugan, N., & Karthikeyan, B. (2006). Process parameter selection for optimising weld bead geometry in stainless steel cladding using Taguchi’s approach. Materials Science and Technology, 22(10), 1193–1200.
  • Montgomery, D. C. (2006). Design and analysis of experiments (4. baskı). New York: John Wiley & Sons.
  • Ross, P. J. (1988). Taguchi techniques for quality engineering. New York: Tata McGraw Hill.
  • Park, S. H. (1996). Robust design and analysis for quality engineering (1. baskı). Londra: Chapman & Hall.
  • Ross, P. J. (1996). Taguchi techniques for quality engineering. New York: McGraw-Hill.
  • Belavendran, N. (1995). Quality by design. Londra: Prentice Hall.
  • Yang, W. H., & Tarng, Y. S. (1998). Design optimization of cutting parameters for turning operations based on the Taguchi method. Journal of Materials Processing Technology, 84, 122–129.
  • Songa, Y. A., Parka, S., & Chaeb, S. W. (2005). Optimization of the injection molding process for part warpage using Taguchi method. International Journal of Machine Tools & Manufacture, 45(1), 1–7.
  • Syrcos, G. P. (2003). Die casting process optimization using Taguchi methods. Journal of Materials Processing Technology, 135, 68–74.
  • Gadakh, V. S., Shinde, V. B., & Khemnar, N. S. (2013). Optimization of welding process parameters using MOORA method. The International Journal of Advanced Manufacturing Technology, 69, 2031–2039.
  • Brauers, W. K. M. (2004). Optimization methods for a stakeholder society: A revolution in economic thinking by multiobjective optimization. Boston: Kluwer Academic.
  • Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi-objective decision-making for road design. Transport, 23, 183–193.
  • Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi-objective optimization of road design alternatives with an application of the MOORA method. Proceedings of the 25th International Symposium on Automation and Robotics in Construction, Lithuania, 541–548.
  • Brauers, W. K. M. (2008). Multi-objective contractor’s ranking by applying the MOORA method. Journal of Business Economics and Management, 4, 245–255.
  • Brauers, W. K. M., & Zavadskas, E. K. (2009). Robustness of the multiobjective MOORA method with a test for the facilities sector. Technological and Economic Development of Economy, 15(2), 352–375.
  • Kalibatas, D., & Turskis, Z. (2008). Multicriteria evaluation of inner climate by using MOORA method. Information Technology and Control, 37, 79–83.
  • Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24.
  • Lootsma, F. A. (1999). Multi-criteria decision analysis via ratio and difference judgement. Londra: Springer.
  • Qazi, M. I., Akhtar, R., Abas, M., Khalid, Q. S., Babar, A. R., & Pruncu, C. I. (2020). An integrated approach of GRA coupled with principal component analysis for multi-optimization of shielded metal arc welding (SMAW) process. Materials, 13(16), 3457.
  • Stephens, M. A. (1974). EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69, 730–737.
  • Seong, W.-J. (2019). Prediction and characteristics of angular distortion in multi-layer butt welding. Materials, 12, 1435.
  • Armentani, E., Esposito, R., & Sepe, R. (2007). The influence of thermal properties and preheating on residual stresses in welding. International Journal of Computational Materials Science and Surface Engineering, 1, 146–162.
  • Ramasamy, N., Jeyasimman, D., Kathiravan, R., & Raju, N. (2019). Influence of Welding Sequence on Residual Stresses Induced in As-Welded Plug Weld of Low-Carbon Steel Plate. Transactions of the Indian Institute of Metals, 72, 1361–1369.
  • Chate, G. R., Patel, G. M., Kulkarni, R. M., Vernekar, P., Deshpande, A. S., & Parappagoudar, M. B. (2018). Study of the effect of nano-silica particles on resin-bonded moulding sand properties and quality of casting. Silicon, 10, 1921–1936.
There are 33 citations in total.

Details

Primary Language English
Subjects Optimization Techniques in Mechanical Engineering
Journal Section Research Article
Authors

Raviram R 0009-0005-5837-1239

Ranjith Raj A 0000-0003-3125-8146

Shashang G 0009-0007-9673-1674

Shameer Mohamed S 0009-0005-7444-9657

Early Pub Date May 16, 2025
Publication Date June 20, 2025
Submission Date February 1, 2025
Acceptance Date April 7, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA R, R., A, R. R., G, S., S, S. M. (2025). Data-driven optimization of MIG welding: A synergistic approach for superior joint quality. European Mechanical Science, 9(2), 103-113. https://doi.org/10.26701/ems.1621888

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