Browsing by Author "Wansasueb, Kittinan"
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Item Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation(Inderscience Enterprises, 2020-09-22) Sarangkum, Ruangrit; Wansasueb, Kittinan; Panagant, Natee; Pholdee, Nantiwat; Bureerat, Sujin; Sait, Sadiq M; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği Bölümü.; F-7426-2011; 7102365439This paper proposes an optimisation process for the design of aircraft fuselage stiffeners using evolutionary optimisation. A new design problem is developed to find a layout for fuselage stiffeners (rings and stringers) such that the structural mass, compliance, and the first-mode natural frequency can be optimised, subject to structural constraints. The stiffeners are modelled as beam elements. Three multiobjective meta-heuristics are employed to solve the problem, and a comparative study of the results of these optimisers is carried out. It is found that the proposed layout synthesis problem for aircraft fuselage stiffeners leads to a set of efficient structural solutions, which can be used at the decision-making stage. It is an automated design strategy with high potential for further investigation.Item Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame(Inderscience Enterprises, 2019) Panagant, Natee; Pholdee, Nantiwat; Wansasueb, Kittinan; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği/Konstrüksiyon ve İmalat Bölümü.; F-7426-2011; 7102365439In this paper, an approach called real-code population-based incremental learning hybridised with adaptive differential evolution (RPBILADE) is proposed for solving many-objective automotive floor-frame optimisation problems. Adaptive strategies are developed and integrated into the algorithm. The purpose of these strategies is to select suitable control parameters for each stage of an optimisation run, in order to improve the search performance and consistency of the algorithm. The automotive floor-frame structures are considered as frame structures that can be analysed with finite element analysis. The design variables of the problems include topology, shape, and size. Ten optimisation runs using various optimisers are carried out on two many-objective automotive floor-frame optimisation problems. Twelve additional benchmark tests against all competitors are also performed to demonstrate the search performance of the proposed algorithm. RPBILADE provided better results than other recent optimisers for both the automotive floor-frame optimisation and benchmark problems.