Browsing by Author "Tiar, Maissa"

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    Optimal conventional and nonconventional machining processes via particle swarm optimization and flower pollination algorithm
    (Springer Science and Business Media, 2025) Mellal, Mohamed Arezki; Tamazirt, Imene; Tiar, Maissa; Williams, Edward J.
    Manufacturing requires various machining processes. Nowadays, machining implies advanced technologies in order to meet more exacting process performance criteria. This paper addresses the optimization of four conventional and nonconventional machining processes: drilling, grinding, water jet machining (WJM), and wire electrical discharge machining (EDM). The input process parameters are: cutting speed, feed rate, cutting environment, depth of cut, grit size, water jet pressure, diameter of water jet nozzle, traverse rate of the nozzle, stand-off-distance, ignition pulse current, pulse-off time, pulse duration, servo reference mean voltage, servo speed variation, wire speed, wire tension, and injection pressure. The multi-objective EDM optimization problem is converted to a single-objective problem using the weighted-sum method. Two nature-inspired algorithms of artificial intelligence (AI) are implemented for solving these problems, namely the particle swarm optimization (PSO) and the flower pollination algorithm (FPA). Penalty functions are introduced to handle the constraints and to enhance the algorithms for better results. The machining outputs, required number of function evaluations, CPU time, and standard deviations are the performance metrics. The results obtained are compared and show better performance than that already documented in the literature.
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    Optimisation des processus d'usinage des pièces par les algorithmes bio-inspirés
    (Université M’Hamed Bougara Boumerdes : Faculté de Technologie, 2019) Tiar, Maissa; Tamazirt, Imene; Mellal, M. A.(Promoteur); Alem, S.(Co-Promoteur)
    La Fabrication des pièces pour les systèmes mécatroniques nécessite divers processus d'usinage. De nos jours, l'usinage implique des technologies de pointe afin d'aboutir à de meilleurs critères de performance des procédés. L'objectif de ce présent travail est d'optimiser quatre processus d'usinage : perçage, rectification, électroérosion par fil, et jet d'eau à haute pression. Deux algorithmes bio-inspirés de l'intelligence artificielle sont implémentés afin de résoudre ces problèmes : Particle Swarm Optimization (PSO) et Flower Pollination Algorithm (FPA). Une fonction de pénalité est introduite pour la maîtrise des contraintes. Les résultats obtenus comparent les performances des deux algorithmes