Abstract
Systems based on artificial intelligence, such as particle swarm optimization and genetic algorithm have received increased attention in many research areas. One of the main objectives in the gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as a characteristic of quality and stiffness. This article has examined the application of particle swarm optimization algorithm to obtain a better DP in a GMAW and compare the results obtained with the technique of genetic algorithms. The effect of four main welding variables in GMAW process which are the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distance on the DP have been studied. For the implementation of optimization, a source code has been developed in MATLAB 8.3. The results showed that, in order to obtain the upper penetration depth, it is necessary that: the welding voltage, the welding speed and the nozzle-to-plate distance must be at their lowest levels; the wire feed speed at its highest level.
Highlights
M.I.G. (Metal Inert Gas) and M.A.G. (Metal Active Gas) or G.M.A.W. (Gas Metal ArcWelding) is one of the most commonly used processes for joining metal
The quality of a welded product is evaluated by various parameters like deposition rate, weld bead geometry and hardness
The regression equation was maximized by the Proposed Algorithm (PSO) and GA methods
Summary
M.I.G. (Metal Inert Gas) and M.A.G. (Metal Active Gas) or G.M.A.W. (Gas Metal Arc. Welding) is one of the most commonly used processes for joining metal. The basis behind heat production in this process is Joule’s law of heating, where an applied electric current produces heat due to resistance across an electric arc, which heats the filler metal and base metal to form a weld pool. This molten metal is protected from oxidation of the surrounding atmosphere by inert shielding gas coverage [1]. One approach characterizes the type of search strategy, for example, one type of search strategy is to improve simple local search algorithms, and the other type of search strategy contains a learning component in research [3].
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