Optimization of TIG Welding Parameters for Enhanced Surface Quality and Mechanical Performance of AISI 316L Stainless Steel
DOI:
https://doi.org/10.31181/rme580Keywords:
TIG Welding, Surface Quality, AISI 316L Stainless Steel, Response Surface Methodology (RSM), Process Parameter OptimizationAbstract
Present research work deals with the design of experiments and optimization of TIG welding parameters to improve the surface quality and mechanical properties of austenitic stainless-steel grade. The key process parameters (welding current, welding speed and shielding gas flow rate) were finalized by conducting systematic experiments on the welding characteristics and properties. Experiments were conducted by Response surface methodology (RSM) using Box-Behnken design to analyze the effect of the above parameters on the surface quality aspects such as weld bead width, surface smoothness, surface defect formation and mechanical properties like ultimate tensile strength (UTS) and hardness. The experimental results indicated that the surface quality is very sensitive to heat input, which is dominated by welding current and velocity. When welding current was 90 A, the fusion went insufficient, which result in the irregular surface profile and the presence of surface discontinuity. When the welding current increased to 115-120 A, the surface finished very well and the weld bead was uniform and smooth, with width of 5.0-5.5 mm. When welding current was increased to 130 A, the welding pool melted excessively, and the width of weld bead was increased to 7.0 mm. When the welding velocity increased from 100 to 200 mm/min, the surface was smooth, and the surface was irregular when the velocity was decreased; the surface was irregular. For the shielding gas flow rate of 15 L/min, good surface appeared, and the oxidation and plasma formation were prevented effectively. The hardness of the base metal decreased from 150 HV to 170 190 HV and the UTS increased to the maximum of 605-615 MPa under the optimal welding condition. The RSM models predicted the surface quality and mechanical properties with high accuracy with the R2 higher than 0.95. Desirability Function Approach (DFA), Teaching learning-based optimization (TLBO) and Grey Relational Analysis (GRA) were used to found optimized welding parameters combination of 115-120 A current, 140-150 mm/min of welding speed and 15 L/min of gases flow rate. These conditions produced performances of around 20% improvements in surface quality and overall weld performance. As a whole, the results give quantitative guidance on how to weld in high quality with great surface finish in industry.
References
Alawadi, S., Fakhouri, H. N., Alkhabbas, F., Kebande, V. R., Awaysheh, F. M., & Cheddad, A. (2026). SHIODEG: a hybrid success-history intelligent optimization algorithm for engineering design problems. Journal of Supercomputing, 82(5). https://doi.org/10.1007/s11227-026-08398-5
Buffa, G., Fratini, L., La Commare, U., Römisch, D., Wiesenmayer, S., Wituschek, S., & Merklein, M. (2022). Joining by forming technologies: current solutions and future trends. International Journal of Material Forming, 15(3), 27. https://doi.org/10.1007/s12289-022-01674-8
David, S. A., Siefert, J. A., & Feng, Z. (2013). Welding and weldability of candidate ferritic alloys for future advanced ultrasupercritical fossil power plants. Science and Technology of Welding and Joining, 18(8), 631-651. https://doi.org/10.1179/1362171813y.0000000152
Falodun, O., Oke, S., & Bodunrin, M. (2025). A comprehensive review of residual stresses in carbon steel welding: formation mechanisms, mitigation strategies, and advanced post-weld heat treatment techniques. The International Journal of Advanced Manufacturing Technology, 136(10), 4107-4140. https://doi.org/10.1007/s00170-025-15088-8
Fashu, S., & Trabadelo, V. (2023). A critical review on development, performance and selection of stainless steels and nickel alloys for the wet phosphoric acid process. Materials & Design, 227, 111739. https://doi.org/10.1016/j.matdes.2023.111739
Görgün, E. (2025). Advancing welding quality through intelligent TIG welding: A hybrid deep learning approach for defect detection and quality monitoring. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 16(3), 677-685. https://doi.org/10.24012/dumf.1642978
Gradl, P., Tinker, D. C., Park, A., Mireles, O. R., Garcia, M., Wilkerson, R., & Mckinney, C. (2022). Robust metal additive manufacturing process selection and development for aerospace components. Journal of Materials Engineering and performance, 31(8), 6013-6044. https://doi.org/10.1007/s11665-022-06850-0
Korovushkin, V., Boichenko, S., Artyukhov, A., Ćwik, K., Wróblewska, D., & Jankowski, G. (2025). Modern optimization technologies in hybrid renewable energy systems: A systematic review of research gaps and prospects for decisions. Energies, 18(17), 4727. https://doi.org/10.3390/en18174727
Kumar, S., Madugula, N. S., Kumar, N., & Giri, J. (2024). An extensive analysis of GTAW process and its influence on the microstructure and mechanical properties of SDSS 2507. Journal of Materials Research and Technology, 33, 8675-8686. https://doi.org/10.1016/j.jmrt.2024.11.157
Peter, A., Mutua, J., Obiko, J., & Ngoret, J. (2025). A Comparative Study on the Effect of GMAW Welding Current on the Performance of API 5L GrB Pipeline Steel Welds for Oil and Gas Industrial Applications. Journal of Pipeline Science and Engineering, 100360. https://doi.org/10.1016/j.jpse.2025.100360
Ramana, M. V., Ali, A. B., Babu, J., Khan, T. Y., Shaik, A. S., Khan, M. A., & Khishe, M. (2026). Welding of Magnesium Alloys to Steels: A Review of Metallurgical Interactions, Mechanical Properties, and Process Control. Journal of Materials Research and Technology. https://doi.org/10.1016/j.jmrt.2026.02.045
Rehman, I. U., Chaudhari, R., Vora, J., Patel, V., Khanna, S., & Bhat, S. K. (2025). Experimental investigations on the machining performance of the WEDM process of NiTi SMA by comparison of Taguchi and Box-Behnken design approaches. Results in Surfaces and Interfaces, 100636. https://doi.org/10.1016/j.rsurfi.2025.100636
Rojas, H., Vargas, Z., Valdez, S., Serrano, M., del Pozo, A., & Alcántara, M. (2024). Taguchi, grey relational analysis, and ANOVA optimization of TIG welding parameters to maximize mechanical performance of Al-6061 T6 alloy. Journal of Manufacturing and Materials Processing, 8(6), 246. https://doi.org/10.3390/jmmp8060246
Roy, A., Ghosh, N., & Mondal, S. (2023). Multi-criteria optimization of TIG welding process parameters on AISI 409M stainless steel by Grey Taguchi method. Welding International, 37(3), 135-141. https://doi.org/10.1080/09507116.2023.2198658
Sama, M., Sata, A., Joshi, G., & Mohan, D. G. (2025). Smart Manufacturing With Industrial Internet of Things: Advances in TIG Welding for SS304 Stainless Steel. Engineering Reports, 7(7), e70257. https://doi.org/10.1002/eng2.70257
Srivastava, M., Rathee, S., Patel, V., Kumar, A., & Koppad, P. G. (2022). A review of various materials for additive manufacturing: Recent trends and processing issues. Journal of Materials Research and Technology, 21, 2612-2641. https://doi.org/10.1016/j.jmrt.2022.10.015
Vadi, S., Bildirici, M., & Kaplan, O. (2025). Performance Comparison of Metaheuristic and Hybrid Algorithms Used for Energy Cost Minimization in a Solar–Wind–Battery Microgrid. Sustainability, 17(19), 8849. https://doi.org/10.3390/su17198849
Velmurugan, K., Di Bona, G., & Silvestri, A. (2025). Optimizing Maintenance Management System: Identification of Critical Risk and Safety Factors in Human Error Based Corrective Maintenance in the SMEs using Fuzzy DEMATEL Approach. Reports in Mechanical Engineering, 6(2), 23-36. https://doi.org/10.31181/rme501
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Reports in Mechanical Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.