• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.3
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• pp.74-81
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• 2019

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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1603-1611
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• 2010

The comparative study of regression-model-based approximate optimization techniques used in the strength design of an automotive knuckle component that will be under bump and brake loading conditions is carried out. The design problem is formulated such that the cross-sectional sizing variables are determined by minimizing the weight of the knuckle component that is subjected to stresses, deformations, and vibration frequency constraints. The techniques used in the comparative study are sequential approximate optimization (SAO), sequential two-point diagonal quadratic approximate optimization (STDQAO), and approximate optimization based on enhanced moving least squares method (MLSM), such as CF (constraint feasible)-MLSM and Post-MLSM. Commercial process integration and design optimization (PIDO) tools are utilized for the application of SAO and STDQAO. The enhanced MLSM-based approximate optimization techniques are newly developed to ensure constraint feasibility. The results of the approximate optimization techniques are compared with those of actual non-approximate optimization to evaluate their numerical performances.