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RISS 인기검색어
- 검색키워드
- 저자 : Sleesongsom, Suwin (검색결과 4 건)
- 무료
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Multiobjective optimization of a steering linkage
S. Sleesongsom,S. Bureerat 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.8
In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rackand-pinion steering linkages satisfying both steering error and turning radius criteria.
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Multi-objective reliability-based topology optimization of structures using a fuzzy set model
Suwin Sleesongsom,Sujin Bureerat 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.10
This research proposes a multi-objective reliability-based topology optimization (MORBTO) for structural design, which considers uncertain structural parameters based on a fuzzy set model. The new technique is established in the form of multi-objective optimization where the equivalent possibilistic safety index (EPSI) is included as one of the objective functions along with mass, and compliance. This technique can reduce complexity due to a doubleloop nest problem used previously due to performing single objective optimization. The present technique can accomplish within one optimization run using a multi-objective approach. Two design examples are used to demonstrate the present technique, which have the objectives as structural mass and compliance with the constraint of structural strength. The results show the proposed technique is effective and simple compared to previous techniques.
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Synthesis of four-bar linkage motion generation using optimization algorithms
Phukaokaew, Wisanu,Sleesongsom, Suwin,Panagant, Natee,Bureerat, Sujin Techno-Press 2019 Advances in computational design Vol.4 No.3
Motion generation of a four-bar linkage is a type of mechanism synthesis that has a wide range of applications such as a pick-and-place operation in manufacturing. In this research, the use of meta-heuristics for motion generation of a four-bar linkage is demonstrated. Three problems of motion generation were posed as a constrained optimization probably using the weighted sum technique to handle two types of tracking errors. A simple penalty function technique was used to deal with design constraints while three meta-heuristics including differential evolution (DE), self-adaptive differential evolution (JADE) and teaching learning based optimization (TLBO) were employed to solve the problems. Comparative results and the effect of the constraint handling technique are illustrated and discussed.
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Wansaseub K.,Sleesongsom S.,Panagant N.,Pholdee N.,Bureerat S. 한국항공우주학회 2020 International Journal of Aeronautical and Space Sc Vol.21 No.3
This paper presents a numerical strategy for reliability-based design optimisation of an aircraft wing structure using a surrogate-assisted approach. The design problem is set to minimise aircraft wing mass subject to structural and aeroelastic constraints, while design variables are structural dimensions. The problem has uncertainties in the material properties. The Kriging model is used for estimating the values of design functions. Two strategies of sampling technique are used, i.e., optimum Latin hypercube sampling (OLHS) with and without infill sampling. Uncertainty quantification is achieved by means of optimum normal distribution Latin hypercube sampling. The original design problem is converted to be a multiobjective optimisation problem. Optimum results show that OLHS with infill sampling gives a more accurate surrogate model; however, OLHS without infill sampling results in the better design solutions based on actual function evaluations.
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