Optimizing MAG Welding Input Variables to Maximize Penetration Depth Using Particle Swarm Optimization Algorithm

Mohamed Mezaache, O. Benaouda, S. Chaouch, B. Babes, R. Amraoui
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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.
利用粒子群算法优化MAG焊接输入变量以达到最大熔深
基于人工智能的系统,如粒子群优化和遗传算法,在许多研究领域受到越来越多的关注。气体金属弧焊(GMAW)工艺的主要目标之一是实现最大熔透深度(DP)作为质量和刚度的特征。本文研究了粒子群优化算法在GMAW中的应用,并与遗传算法的结果进行了比较。研究了GMAW工艺中焊接电压、焊接速度、送丝速度和喷嘴到板的距离四个主要焊接变量对DP的影响。为实现优化,已在MATLAB 8.3下开发了源代码。结果表明,为了获得最大的熔深,焊接电压、焊接速度和喷嘴到板的距离必须保持在最低水平;送丝速度达到最高水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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