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ARTICLE

Robust Particle Swarm Optimization Algorithm for Modeling the Effect of Oxides Thermal Properties on AMIG 304L Stainless Steel Welds

Rachid Djoudjou^1,*, Abdeljlil Chihaoui Hedhibi³, Kamel Touileb¹, Abousoufiane Ouis¹, Sahbi Boubaker², Hani Said Abdo^4,5

1 Department of Mechanical Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, P.O. Box 655, Al-Kharj, 16273, Saudi Arabia
2 Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia
3 Laboratory of Mechanics of Sousse (LMS), National Engineering School of Sousse, University of Sousse, Sousse, 4054, Tunisia
4 Center of Excellence for Research in Engineering Materials (CEREM), King Saud University, P.O. Box 800, Al-Riyadh, 11421, Saudi Arabia
5 Department of Metallurgical and Materials Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Suez, 43512, Egypt

* Corresponding Author: Rachid Djoudjou. Email:

(This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)

Computer Modeling in Engineering & Sciences 2024, 141(2), 1809-1825. https://doi.org/10.32604/cmes.2024.053621

Received 06 May 2024; Accepted 12 August 2024; Issue published 27 September 2024

Abstract

There are several advantages to the MIG (Metal Inert Gas) process, which explains its increased use in various welding sectors, such as automotive, marine, and construction. A variant of the MIG process, where the same equipment is employed except for the deposition of a thin layer of flux before the welding operation, is the AMIG (Activated Metal Inert Gas) technique. This study focuses on investigating the impact of physical properties of individual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can help determine a relationship among weld depth penetration, the aspect ratio, and the input physical properties of the oxides. Five types of oxides, TiO₂, SiO₂, Fe₂O₃, Cr₂O₃, and Mn₂O₃, are tested on butt joint design without preparation of the edges. A robust algorithm based on the particle swarm optimization (PSO) technique is applied to optimally tune the models’ parameters, such as the quadratic error between the actual outputs (depth and aspect ratio), and the error estimated by the models’ outputs is minimized. The results showed that the proposed PSO model is first and foremost robust against uncertainties in measurement devices and modeling errors, and second, that it is capable of accurately representing and quantifying the weld depth penetration and the weld aspect ratio to the oxides’ thermal properties.

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