The
precision of parameter selection in submerged arc welding (SAW) significantly
influences weld quality, strength, and efficiency in industrial manufacturing.
Artificial intelligence offers advanced tools for addressing the complex,
non-linear optimization challenges in welding processes where traditional
trial-and-error methods fall short. This paper introduces the Adolescent
Identity Search Algorithm (AISA), an AI-based, human-inspired optimization
technique, to optimize SAW parameters. Implemented in MATLAB, the algorithm was applied
to minimize bead width (BW)—a critical indicator of weld quality—by refining
welding current, voltage, speed, and wire feed. Comparative analysis with the
Rao-1 algorithm was conducted under varying population sizes and iteration
counts. Results show that AISA consistently achieved a minimum bead width of
17.06 mm with a success rate exceeding 99%, outperforming Rao-1, which recorded
a minimum of 17.23 mm under the same conditions. These findings demonstrate
AISA’s robustness, stability, and adaptability in parameter optimization,
confirming its potential as an effective tool for enhancing manufacturing
precision.
[1] K.
Kalita, D. Burande, R. K. Ghadai, and S. Chakraborty, “Finite Element
Modelling, Predictive Modelling and Optimization of Metal Inert Gas, Tungsten
Inert Gas and Friction Stir Welding Processes: A Comprehensive Review,” Arch.
Comput. Methods Eng., vol. 30, no. 1, pp. 271–299, Jan. 2023, doi:
10.1007/s11831-022-09797-6.
[2] B. Lekouaghet, M. Haddad, and N. Hamouda,
“Exploring Recent Metaphor-Based Algorithms for Optimizing TIG Welding
Parameters,” in 2024 2nd International Conference on Electrical Engineering
and Automatic Control (ICEEAC), IEEE, May 2024, pp. 1–5. doi:
10.1109/ICEEAC61226.2024.10576297.
[3] T. Marwala and C. A. Leke, Handbook of
machine learning: Volume 2: Optimization and decision making. World
Scientific, 2019.
[4] B. Eren, M. A. Guvenc, and S. Mistikoglu,
“Artificial Intelligence Applications for Friction Stir Welding: A Review,” Metals
and Materials International, vol. 27, no. 2. The Korean Institute of Metals
and Materials, pp. 193–219, 2021. doi: 10.1007/s12540-020-00854-y.
[5] M. Mezaache, O. F. Benaouda, and A.
Kellai, “Maximizing welding efficiency: applying an improved whale optimization
algorithm for parametric optimization of bead width in a submerged arc welding
process,” Int. J. Adv. Manuf. Technol., vol. 134, no. 5, pp. 2737–2752,
2024, doi: 10.1007/s00170-024-14231-1.
[6] D. Pendokhare and S. Chakraborty,
“Optimizing plasma arc cutting processes using physics-based metaheuristic
algorithms: a comparative analysis,” Int. J. Interact. Des. Manuf.,
2024, doi: 10.1007/s12008-024-02136-y.
[7] R. V. Rao, “Rao algorithms: Three
metaphor-less simple algorithms for solving optimization problems,” Int. J.
Ind. Eng. Comput., vol. 11, no. 1, pp. 107–130, 2020, doi:
10.5267/j.ijiec.2019.6.002.
[8] J. Vora et al., “Optimization of
activated tungsten inert gas welding process parameters using heat transfer
search algorithm: With experimental validation using case studies,” Metals
(Basel)., vol. 11, no. 6, 2021, doi: 10.3390/met11060981.
[9] P. D. Skariya, M. Satheesh, and J. E. R.
Dhas, “Optimizing parameters of TIG welding process using grey wolf
optimization concerning 15CDV6 steel,” Evol. Intell., vol. 11, no. 1–2,
pp. 89–100, 2018, doi: 10.1007/s12065-018-0161-5.
[10] T. Joyce and J. M. Herrmann, “A Review of No
Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation
BT - Nature-Inspired Algorithms and
Applied Optimization,” X.-S. Yang, Ed., Cham: Springer International
Publishing, 2018, pp. 27–51. doi: 10.1007/978-3-319-67669-2_2.
[11] E. Bogar and S. Beyhan, “Adolescent Identity
Search Algorithm (AISA): A novel metaheuristic approach for solving
optimization problems,” Appl. Soft Comput. J., vol. 95, p. 106503, Oct.
2020, doi: 10.1016/j.asoc.2020.106503.
[12] R. V. Rao and D. P. Rai, “Optimisation of
welding processes using quasi-oppositional-based Jaya algorithm,” J. Exp.
Theor. Artif. Intell., vol. 29, no. 5, pp. 1099–1117, 2017.
[13] W. Merrouche, B. Lekouaghet, and E.
Bouguenna, “Artificial Search Algorithm for Parameters Optimization of Li-Ion
Battery Electrical Model,” in 2023 International Conference on Decision Aid
Sciences and Applications (DASA), IEEE, Sep. 2023, pp. 17–22. doi:
10.1109/DASA59624.2023.10286632.
[14] B. Lekouaghet, M. Haddad, M. Benghanem, and
M. A. Khelifa, “Identifying the unknown parameters of PEM fuel cells based on a
human-inspired optimization algorithm,” Int. J. Hydrogen Energy, vol.
129, no. April, pp. 222–235, 2025, doi: 10.1016/j.ijhydene.2025.04.301.