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http://dx.doi.org/10.14775/ksmpe.2019.18.3.074

Meta-model Effects on Approximate Multi-objective Design Optimization of Vehicle Suspension Components  

Song, Chang Yong (Department of Naval Architecture & Ocean Engineering, Mokpo National University)
Choi, Ha-Young (Department of Mechanical Engineering, Dongyang Mirae University)
Byon, Sung-Kwang (Department of Mechanical Engineering, Dongyang Mirae University)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.18, no.3, 2019 , pp. 74-81 More about this Journal
Abstract
Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.
Keywords
Approximation; Meta-model; Multi-objective Design Optimization; Knuckle;
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