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최병렬(Byung-Lyul Choi),이갑성(Gab-Seong Lee),최동훈(Dong-Hoon Choi) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
This research proposes a design process considering uncertainties to obtain optimal hardpoints and bush stiffness coefficients of the automotive suspension systems. To execute the kinematic and compliance analysis of the suspension system, we utilizes ADAMS/Car model of the McPherson strut type front suspension system. The proposed design process consists of 3 steps. The first step is to define the uncertainties. In this step, the properties of automotive parts are considered as random variables. To randomize their nonlinear properties, the engineering sense is needed. The second step is to analyze the influence on suspension system uncertainties. The final step is to optimize the hardpoints considering random variables. In this research, all design studies are executed by the FRAMAX, it is one of the PIDO tools. In conclusion, the design process under uncertainties is proposed and is applied on the automotive suspension system. As a result, the optimal hardpoints and bush stiffness coefficients can be obtained to satisfy target reliabilities of the design requirements.
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PIDO 기술을 이용한 전륜 현가계의 다분야 통합 최적설계
최병렬(Byung-Lyun Choi),이갑성(Gab-Seong Lee),최동훈(Dong-Hoon Choi),남찬혁(Chan-Hyuk Nam),김기훈(Gi-Hoon Kim) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
In this research, a multidisciplinary design optimization of suspension system is executed using the process integration and design optimization technique. To obtain design requirements from multi-disciplines, such as weight, durability, crash, NVH, and K&C analysis, the shapes and thicknesses of front sub-frame are optimized. To define the shape design variables, a morphing technique is utilized. For maximizing the efficiency of multidisciplinary design optimization, we apply an approximate optimization technique and enhance the accuracy of approximate models with augment latin hypercube design sampling, which is one of practical additional design of experiments. We can understand the main effect of each performance using design sensitivity matrix after execution of design of experiments. The response surface method and kriging method are adaptively used. The thickness design variables are dealt as discrete variables so that we adopt an evolutionary algorithm as a discrete optimizer. We successfully obtain alternative designs using various design formulations changing the priorities of weight and fatigue life cycles.
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