Maximizing welding efficiency: applying an improved whale optimization algorithm for parametric optimization of bead width in a submerged arc welding process

Owing to its low operating costs and high productivity, submerged arc welding (SAW) is a widely utilized machine in various sectors, particularly industry. However, achieving optimal bead width (BW) remains a challenge due to the process being intricate and non-linear. This research is devoted to proposing a novel nature-inspired meta-heuristic optimization algorithm based on the whale optimization algorithm (WOA), called the improved whale optimization algorithm (IWOA), an inertia weight is included, which imitates the social behavior of hunting humpback whales. This parameter controls the influence of the current best solution on the search process during the exploitation phase, allowing an exhaustive search near the optimal solution. Today, due to its competitiveness and efficiency, this algorithm has been widely used to solve complex optimization problems in various fields, including manufacturing and engineering. The proposed algorithm was used to adjust four parameters of the SAW process, including arc voltage (V), welding current (I), welding speed (S), and wire feed (F). The objective function is minimizing the bead width, which is an essential factor in order to maximize weld quality and neatness (consistency). The obtained results have revealed that the IWOA can effectively optimize the SAW process parameters. When compared to other optimization algorithms, which include particle swarm optimization (PSO) and WOA, the suggested method exceeds them all in terms of fast convergence time and solution quality.