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Reliability-Based Design Optimization for Automotive Wheel Bearings Considering Geometric Uncertainty
ISSN: 0148-7191, e-ISSN: 2688-3627
Published November 05, 2023 by SAE International in United States
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Automotive wheel bearings have the primary function of translating the rotating motion of the wheels into linear vehicle motion while supporting the vehicle weight. As vehicle lives continue to increase, there is a need for longer service lives than those of existing products. There is an even greater need for performance-related reliability during usage. Lateral stiffness, one of the main parameters of wheel bearing design, has a significant influence on ride comfort and steering feel. In this study, reliability-based weight optimization considering geometric uncertainty for automotive wheel bearings was investigated. Deterministic design optimization (DDO) and reliability-based design optimization (RBDO) were performed. For optimization, the following three key relationships were chosen: wheel bearing specification and geometry for design variables, weight for cost function, and stiffness for constraint. A Monte Carlo simulation considering the probability distribution of the geometric uncertainties was performed to identify the effect of variations in dimensions. Since Monte Carlo simulation to analyze the stiffness of the bearings takes a very high computational cost, a regression analysis was performed, followed by running Monte Carlo simulation based on this regression. Based on the simulation results, the design variations for the automotive wheel bearings were evaluated using the results of the Monte Carlo simulation. The results showed that the DDO and RBDO reduced weight by about 36% and 17% respectively, compared with their initial weight.
CitationLee, S., "Reliability-Based Design Optimization for Automotive Wheel Bearings Considering Geometric Uncertainty," SAE Technical Paper 2023-01-1886, 2023, https://doi.org/10.4271/2023-01-1886.
- Lee , S. , Lee , N. , Lim , J. , and Park , J. The Effect of Outer Ring Flange Concavity on Automotive Wheel Bearings Performance SAE Int. J. Passeng. Cars - Mech. Syst. 9 3 2016 https://doi.org/10.4271/2016-01-1958
- Lee , S. Bearing Life Evaluation for Automotive Wheel Bearings Using Design of Experiments SAE Technical Paper 2018-01-1903 2018 https://doi.org/10.4271/2018-01-1903
- Lee , S. Bearing Life Optimization for Automotive Wheel Bearings SAE Technical Paper 2019-01-2137 2019 https://doi.org/10.4271/2019-01-2137
- Lee , S.P. , Kim , J.H. , and Kang , K.W. Drag Torque Prediction of Automotive Wheel Bearing Seals Considering Material and Geometrical Uncertainties Using Monte Carlo Simulation Int. J. Automot. Technol. 21 6 2020 1447 1453 10.1007/s12239-020-0136-2
- Yoon , S.J. and Choi , D.H. Reliability-Based Design Optimization of Slider Air Bearings KSME International Journal 18 10 2004 1722 1729
- Shoaei , P. and Mojtaba , M. Reliability-Based Design of Steel Moment Frame Structures Isolated by Lead-Rubber Bearing Systems Struct . 20 2019 765 778 10.1016/j.istruc.2019.06.020
- Choi , D.H. and Yoon , K.C. A Design Method of an Automotive Wheel-Bearing Unit with Discrete Design Variables Using Genetic Algorithms J. Trib. 123 1 2001 181 187 10.1115/1.1329878
- Nagatani , H. and Niwa , T. Application of Topology Optimization and Shape Optimization for Development of Hub-Bearing Lightening NTN Technical Review 73 2005 14 19
- Lu , X. and Xie , X. Multi-Objective Structural Optimization of Hub Unit Bearing Using Response Surface Methodology and Genetic algorithm J. Adv. Mech. Des. Syst. Manuf. 8 3 2014 14 00019 10.1299/jamdsm.2014jamdsm0035
- Jin , J.W. , Kang , K.W. , and Lee , S.P. Fatigue Analysis for Automotive Wheel Bearing Flanges Int. J. Precis. Eng. Manuf. 24 2023 621 628 10.1007/s12541-023-00773-z