Robust design optimization of the McPherson suspension system with consideration of a bush compliance uncertainty

Kang, Daeoh; Heo, Seung-Jin; Kim, M.-S.

doi:10.1243/09544070jauto1228

Cited by 17 publications

(8 citation statements)

References 6 publications

(5 reference statements)

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“…Usually four to six design variables were chosen for optimization in previous studies. 20,21 In our study, to include more design variables in the optimization, design variables with the top 60% (9 of 15) overall sensitivity index values were selected from Table 3, which were damper upper y , control arm front z , control arm outer z , control arm outer y , control arm outer x , steering rod inner z , steering rod outer z , steering rod outer x , and steering rod inner x .…”

Section: Selection Of Design Variables and Uncertain Variablesmentioning

confidence: 99%

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Shi

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the traditional design of a vehicle suspension system, mechanical parameters, such as spring stiffness and tire radial stiffness, are determined before optimizing the coordinates of suspension hard-points based on handling and stability. However, the mechanical parameters of vehicle suspension systems vary with repeated changes in ambient temperature and loading conditions. Consequently, the original hard-point coordinates cannot guarantee satisfactory handling and stability after long-term vehicle use. To address this issue, an A0 class vehicle with a MacPherson suspension was modeled in ADAMS/Car and was validated against the results of field tests. The relationships between the maximum absolute values of front wheel alignment parameters during parallel wheel travel analysis and the coordinates of suspension hard-points, spring stiffness, and tire radial stiffness were determined using support vector regression. Next, a multi-objective optimization function was formulated based on the interval analysis method. Finally, a novel double-loop multi-objective particle swarm optimization algorithm was designed for global optimization of hard-point coordinates. The ADAMS simulation results indicated that, compared with the vehicle with the original hard-point coordinates, the double-loop multi-objective particle swarm optimization algorithm, the traditional multi-objective particle swarm optimization algorithm, and the genetic algorithm can effectively reduce the variation ranges of front wheel alignment parameters, regardless of the values of the mechanical parameters. It also showed that the double-loop multi-objective particle swarm optimization algorithm performs better than the traditional multi-objective particle swarm optimization and the genetic algorithm with both the original and changed mechanical parameters. Except for an increase in the variation range of the caster angle by 9.24–10.69%, the variation ranges of the toe angle, camber angle, and kingpin inclination angle under various mechanical parameters using the double-loop multi-objective particle swarm optimization algorithm were observed to be reduced by 50.45–79.39%, 1.84–4.24%, and 2.11–2.96%, respectively. Last but not least, the proposed double-loop multi-objective particle swarm optimization algorithm provided better handling, stability, and ride comfort values than the traditional multi-objective particle swarm optimization algorithm and the genetic algorithm.

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Section: Selection Of Design Variables and Uncertain Variablesmentioning

confidence: 99%

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Shi

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Choi et al (2004) performed the reliability optimization design by SLSV method using the results of deterministic optimization as the initial value of the reliability based optimization design after using the design sensitivity which was applied by the finite difference method. Kang et al (2010) suggested the design method which maximized the functions of suspension system and minimized the deviation simultaneously by applying the robust design optimization based on meta-model to avoid the difficulties of design sensitivity analysis and numerical noise. Figure 1 is the typical design process of suspension system.…”

Section: Review Of Suspension System Designmentioning

confidence: 99%

Robust design optimization of suspension system by using target cascading method

Kang

Heo

Kim

et al. 2011

Int.J Automot. Technol.

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This study presents the robust design optimization process of suspension system for improving vehicle dynamic performance (ride comfort, handling stability). The proposed design method is so called target cascading method where the design target of the system is cascaded from a vehicle level to a suspension system level. To formalize the proposed method in the view of design process, the design problem structure of suspension system is defined as a (hierarchical) multilevel design optimization, and the design problem for each level is solved using the robust design optimization technique based on a meta-model. Then, In order to verify the proposed design concept, it designed suspension system. For the vehicle level, 44 random variables with 3% of coefficient of variance (COV) were selected and the proposed design process solved the problem by using only 88 exact analyses that included 49 analyses for the initial meta-model and 39 analyses for SAO. For the suspension level, 54 random variables with 10% of COV were selected and the optimal designs solved the problem by using only 168 exact analyses for the front suspension system. Furthermore, 73 random variables with 10% of COV were selected and optimal designs solved the problem by using only 252 exact analyses for the rear suspension system. In order to compare the vehicle dynamic performance between the optimal design model and the initial design model, the ride comfort and the handling stability was analyzed and found to be improved by 16% and by 37%, respectively. This result proves that the suggested design method of suspension system is effective and systematic.

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“…either on the ride comfort or on the handling performance. [11][12][13][14][15] Therefore, in this paper, a new methodology is proposed specifically to address the problem of a large number design variables with conflicting design requirements. Regularity-model-based multi-objective estimation of the distribution algorithm (RM-MEDA) is employed to solve this problem to produce the Pareto front of an achievable set of solutions which is a compromise between the ride comfort and the handling performance.…”

Section: Introductionmentioning

confidence: 99%

A new multi-objective optimization method for full-vehicle suspension systems

Tey

Ramli

Abdullah

2016

Proceedings of the Institution of Mechanical Engineers, Part D:

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The conventional approach in vehicle suspension optimization based on the ride comfort and the handling performance requires decomposition of the multi-performance targets, followed by lengthy iteration processes. Suspension tuning is a time-consuming process, which often requires the benchmarking of competitors’ vehicles to define the performance targets of the desired vehicle by experimental techniques. Optimum targets are difficult to derive from benchmark vehicles as each vehicle has its own unique vehicle set-up. A new method is proposed to simplify this process and to reduce significantly the development process. These design objectives are formulated into a multi-objective optimization problem together with the suspension packaging dimensions as the design constraints. This is in order to produce a Pareto front of an optimized vehicle at the early stages of design. These objectives are minimized using a multi-objective optimization workflow, which involves a sampling technique, and a regularity-model-based multi-objective estimation of the distribution algorithm to solve greater than 100-dimensional spaces of the design parameters by the software-in-the-loop optimization process. The methodology showed promising results in optimizing a full-vehicle suspension design based on the ride comfort and the handling performance, in comparison with the conventional approach.

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Robust design optimization of the McPherson suspension system with consideration of a bush compliance uncertainty

Cited by 17 publications

References 6 publications

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Robust design optimization of suspension system by using target cascading method

A new multi-objective optimization method for full-vehicle suspension systems

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