Optimizing Weld Bead Geometry for High-Performance ‎Welding of Stainless-Steel Using Grey Relational Analysis

  • Authors

    • Dr. Sivakumar C Department of Mechanical Engineering, Al–Ameen Engineering College, Erode, Tamil Nadu, India
    • Dr. Balusamy R. Department of Mechanical Engineering, Al–Ameen Engineering College, Erode, Tamil Nadu, India
    • Mr. Surendran M. Department of Mechanical Engineering, Al–Ameen Engineering College, Erode, Tamil Nadu, India
    • Mr. Naveen Kumar Department of Mechanical Engineering, Al–Ameen Engineering College, Erode, Tamil Nadu, India
    • Mr. MohideenBatcha M. Department of Mechanical Engineering, Al–Ameen Engineering College, Erode, Tamil Nadu, India
    • Mr. Srenatthan P.L. Department of Mechanical Engineering, Al–Ameen Engineering College, Erode, Tamil Nadu, India
    https://doi.org/10.14419/exebz605

    Received date: May 2, 2025

    Accepted date: May 29, 2025

    Published date: November 1, 2025

  • MIG Welding; Stainless Steel; Grey Relational Analysis; Optimization; Bead Geometry; ANOVA‎.

  • Abstract

    This study focuses on optimizing the weld bead geometry in Metal Inert Gas (MIG) butt-welding of stainless steel using Grey Relational ‎Analysis (GRA). Process parameters significantly influence weld quality, and suboptimal conditions often result in joint failure. Traditional ‎trial-and-error approaches have been replaced by advanced computational and statistical techniques to enhance process efficiency and ‎reliability. This research investigates the relationship between welding parameters—such as welding current, welding speed, and arc ‎voltage—and key output responses, including bead penetration, tensile strength, and microhardness. GRA, which efficiently ranks ‎parameter combinations according to a gray relational grade, was used to build a multi-objective optimization technique. Analysis of variance ‎‎(ANOVA) was used to determine the importance of each factor, and confirmation tests were performed to verify the optimal settings. The ‎results demonstrate improved weld quality and mechanical properties, confirming the effectiveness of the proposed methodology in ‎optimizing stainless steel welding‎.

  • References

    1. Mohd Said, J., &MohdTuran, F. (2022). Optimising MIG weld bead geometry of hot rolled carbon steel using response surface method. In Ena-bling Industry 4.0 through Advances in Manufacturing and Materials: Selected Articles from iM3F 2021, Malaysia (pp. 179–188). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2890-1_18.
    2. Manikandan, N., Thejasree, P., Khan, M. A., Joseph, J., Mangalathu, G. S., &Jeyaprakash, N. (2024). Integration of hybrid grey based ANFIS tool for enhanced laser beam welding of nickel alloy using computational modelling. International Journal on Interactive Design and Manufacturing (IJIDeM), 1–12. https://doi.org/10.1007/s12008-024-02073-w.
    3. Jambhale, S., Kumar, S., & Kumar, S. (2020). Multi-response optimization of friction stir spot welded joint with grey relational analysis. Materials Today: Proceedings, 27, 1900–1908. https://doi.org/10.1016/j.matpr.2020.03.830.
    4. Devaraj, J. (2021). Minimization of the weld distortion by weld sequence optimization using artificial intelligence [Unpublished manuscript].
    5. Yadav, A., Srivastava, M., Jain, P. K., &Rathee, S. (2024). Investigation of bead morphology and mechanical behaviour for metal inert gas weld-ing-based WAAM in pulsed mode metal transfer on 316LSi stainless steel. Journal of Adhesion Science and Technology, 38(5), 738–769. https://doi.org/10.1080/01694243.2023.2241642.
    6. Dave, R., Dave, I. B., Vora, J. J., Chaudhari, R., Das, S., &Gangwar, P. K. (2024). Multiobjective optimization of welding process variables in RMD and FCAW techniques using a heat transfer search algorithm for 316LN stainless steel. Discover Applied Sciences, 7(1), 12. https://doi.org/10.1007/s42452-024-06400-4.
    7. Surwase, S. S., & Bhosle, S. P. (2023). Investigating the effect of residual stresses and distortion of laser welded joints for automobile chassis and optimizing weld parameters using random forest based grey wolf optimizer. Welding International, 37(1), 46–67. https://doi.org/10.1080/09507116.2023.2174915.
    8. Moi, S. C. (2019). Effect of process parameters of tungsten inert gas welding on the weld quality of austenitic stainless steel [Unpublished manu-script].
    9. Ravikumar, R., &Mathivanan, A. (2025). Machine learning prediction and optimization of cold metal transfer welding parameters for enhancing the mechanical and microstructural properties of austenitic-ferritic stainless-steel joints. Journal of Materials Engineering and Performance, 1–22. https://doi.org/10.1007/s11665-025-10764-y.
    10. Datta, S., & Mahapatra, S. I. B. A. S. (2010). Use of desirability function and principal component analysis in grey-Taguchi approach to solve corre-lated multi-response optimization in submerged arc welding. Journal of Advanced Manufacturing Systems, 9(2), 117–128. https://doi.org/10.1142/S0219686710001843.
    11. Ahmad, A. (2021). Microstructure analysis and multi-objective optimization of pulsed TIG welding of 316/316L austenite stainless steel. In Hand-book of Smart Materials, Technologies, and Devices: Applications of Industry 4.0 (pp. 1–33). Springer International Publishing. https://doi.org/10.1007/978-3-030-58675-1_127-1.
    12. Madsa, T., Jattakul, P., Pansa-nga, S., &Mookam, N. (2024). Investigation of CMT welding AISI H13 hot work tool steel on weld bead geometry, microstructure, and mechanical performance. Arabian Journal for Science and Engineering, 49(8), 10943–10959. https://doi.org/10.1007/s13369-023-08547-5.
    13. Tabaie, S., Greene, T., & Benoit, M. J. (2023). Optimization of GMAW process parameters for weld overlay of Inconel 686 superalloy on low-carbon steel. The International Journal of Advanced Manufacturing Technology, 127(9), 4769–4788. https://doi.org/10.1007/s00170-023-11798-z.
    14. Sonar, T., Balasubramanian, V., Malarvizhi, S., Venkateswaran, T., & Sivakumar, D. (2020). Multi-response mathematical modelling, optimization and prediction of weld bead geometry in gas tungsten constricted arc welding (GTCAW) of Inconel 718 alloy sheets for aero-engine components. Multiscale and Multidisciplinary Modeling, Experiments and Design, 3, 201–226. https://doi.org/10.1007/s41939-020-00073-3.
    15. Dave, R., Dave, I. B., Vora, J. J., Chaudhari, R., Das, S., &Gangwar, P. K. (2025). Discover Applied Sciences. Discover, 7, 12. https://doi.org/10.1007/s42452-024-06400-4.
    16. Punam, S. R. (2024). Reconfigurable intelligent surfaces: Enabling spectrum-efficient and adaptive communication for 6G wireless networks. Elec-tronics, Communications, and Computing Summit, 2(3), 49–57.
    17. Kavitha, M. (2025). Design and Optimization of High-Speed Synchronous Reluctance Machines for Industrial Drives. National Journal of Electri-cal Machines & Power Conversion, 1-10.
    18. Rahim, R. (2024). Scalable architectures for real-time data processing in IoT-enabled wireless sensor networks. Journal of Wireless Sensor Networks and IoT, 1(1), 44-49. https://doi.org/10.31838/WSNIOT/01.01.07.
    19. Sipho, T., Lindiwe, N., &Ngidi, T. (2025). Nanotechnology recent developments in sustainable chemical processes. Innovative Reviews in Engi-neering and Science, 3(2), 35–43.
    20. Srirangan, A. K., & Paulraj, S. (2016). Multi-response optimization of process parameters for TIG welding of Incoloy 800HT by Taguchi grey rela-tional analysis. Engineering science and technology, an international journal, 19(2), 811-817. https://doi.org/10.1016/j.jestch.2015.10.003.
    21. Kasman, Ş. (2013). Multi-response optimization using the Taguchi-based grey relational analysis: a case study for dissimilar friction stir butt weld-ing of AA6082-T6/AA5754-H111. The international journal of Advanced manufacturing technology, 68(1), 795-804. https://doi.org/10.1007/s00170-012-4720-0.
    22. Abdullah, D. (2025). Nonlinear dynamic modeling and vibration analysis of smart composite structures using multiscale techniques. Journal of Ap-plied Mathematical Models in Engineering, 1(1), 9–16.
    23. Andrade, F. (2024). The Role of Total Quality Management: SME. National Journal of Quality, Innovation, and Business Excellence, 1(1), 30-36.
    24. Wei, L., & Maidanov, K. (2025). Fog-assisted anomaly detection in IoT-WSN ecosystems using hybrid deep learning models. Journal of Wireless Sensor Networks and IoT, 3(1), 1–9.
    25. Luedke, R. G., & Tandi, M. R. (2025). Secure and scalable LPWAN architectures for industrial Internet of Things (IIoT). Progress in Electronics and Communication Engineering, 3(1), 65–72.*
    26. Btia, J., & David, G. (2025). Embedded implementation of speech-to-text translation using compressed deep neural networks. National Journal of Signal and Image Processing, 1(3), 39–47.
    27. Rahim, R., & Shimada, T. (2025). Thermal-aware floorplanning and optimization framework for high-performance heterogeneous SoC architectures in edge-AI and embedded systems. Journal of Integrated VLSI, Embedded and Computing Technologies, 3(1), 38–46.
    28. Beyes, J. O., & Kavitha, M. (2025). Antenna-in-package (AiP) integration strategies for high-frequency RFICs in 5G/6G edge devices: Design chal-lenges, performance trade-offs, and future opportunities. National Journal of RF Circuits and Wireless Systems, 3(2), 16–24.
    29. Aleem, F. M., & Pamije, L. K. (2025). Multimodal emotion recognition for human–robot interaction using speech, facial dynamics, and physiologi-cal signals. National Journal of Speech and Audio Processing, 1(3), 9–17.
    30. Alabi, D., & Mpamije, L. J. (2025). Mixed-signal SoC for ultra-low-noise sensor interfaces in next-generation electronic systems. National Journal of Electrical Electronics and Automation Technologies, 1(2), 69–75.

  • Downloads

  • How to Cite

    C, D. S. ., R., D. B. ., M., M. S., Kumar, M. N. ., M., M. M., & P.L., M. S. . (2025). Optimizing Weld Bead Geometry for High-Performance ‎Welding of Stainless-Steel Using Grey Relational Analysis. International Journal of Basic and Applied Sciences, 14(SI-1), 456-462. https://doi.org/10.14419/exebz605