Mohamed MEZAACHE

Systems based on artificial intelligence, such as particle swarm optimization and geneticalgorithm have received increased attention in many research areas. One of the main objectives inthe gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as acharacteristic of quality and stiffness. This article has examined the application of particle swarmoptimization algorithm to obtain a better DP in a GMAW and compare the results obtained with thetechnique of genetic algorithms. The effect of four main welding variables in GMAW process whichare the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distanceon the DP have been studied. For the implementation of optimization, a source code has beendeveloped 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 beat their lowest levels; the wire feed speed at its highest level

In this paper, an adaptive fuzzy fast terminalsynergetic voltage regulation for DC/DC buck converter isdesigned based on recently developed synergetic theory and aterminal attractor method. The advantages of presentedsynergetic control include the characteristics of finite timeconvergence, insensitive to parameters variation and chatteringfree phenomena. Rendering the design more robust, fuzzy logicsystems are used to approximate the unknown parameters in theproposed controller without calling upon usual modellinearization and simplifications. Taking the DC/DC buckconverter in continuous conduction mode as an example, thealgorithm of proposed synergetic control is analyzed in detail. Allthe simulation results demonstrate the effectiveness and the highdynamic capability of the proposed AF-FTSC control techniqueover the FTSC strategy

Systems based on artificial intelligence, such as particle swarm optimization and geneticalgorithm have received increased attention in many research areas. One of the main objectives inthe gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as acharacteristic of quality and stiffness. This article has examined the application of particle swarmoptimization algorithm to obtain a better DP in a GMAW and compare the results obtained with thetechnique of genetic algorithms. The effect of four main welding variables in GMAW process whichare the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distanceon the DP have been studied. For the implementation of optimization, a source code has beendeveloped 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 beat their lowest levels; the wire feed speed at its highest level.