Seungpyo Lee

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.

The automotive wheel bearing is an integral component of automobiles that transfers rotation and supports the weight of the vehicle. The life of automotive wheel bearings can be categorized into the raceway and flange lives. The raceway life is based on the spalling that occurs in the raceway and can be determined analytically using the basic rating life calculation method for the rolling bearing. The flange life is based on failure involving the flange and can be numerically predicted through fatigue analysis. In particular, the fatigue analysis for predicting the flange life must consider rotation owing to the bearing characteristics, unlike the general fatigue analysis. In this study, we examined the fatigue analysis of the automotive wheel bearing flange while considering rotation. We performed sequential stress and fatigue analyses using commercial software. As the fatigue material properties are required for fatigue analysis, we obtained the properties by conducting a rotary bending fatigue test on the bearing materials. To verify the results of the analysis, we compared the analysis results with the test results. The validity of the proposed fatigue analysis was confirmed.

In this study, we identified the noise generated from automotive wheel bearings, which has recently emerged as a new problem in electric vehicles. The wheel bearing assembly considered in this study consists of a wheel bearing, dust shield, and knuckle, which are fastened with bolts. To obtain the noise characteristics of the wheel bearing, the noise and vibration were experimentally measured when the bearing rotated. Additionally, the natural frequencies and mode shapes of the main components of the bearing were acquired via modal testing. By comparing the obtained natural frequencies with the peak frequencies of the measured noise and vibration signals, we identified where the noise radiated. To specifically identify bearing defects, a finite element analysis model was established, and the deformation of the bearing under load was analyzed. Based on the analysis, we determined that the deformation of the outer ring in an outboard row, which resulted from bolt fastening, leads to noise and vibration in the wheel bearing.

Due to the need to develop clean and environmentally friendly energy sources, there is a growing interest in wind energy. In this study, reliability-based weight optimization considering material uncertainty for composite wind turbine blade was investigated. Deterministic design optimization was performed. Reliability-based design optimization was performed and compared with their results. From the numerical analysis results, deflection, safety factor, and blade thickness were calculated to evaluate the total weight of the composite blade. For optimization, design variables were chosen as ply thickness of blade and the objective function was chosen as weight. The reliability-based design optimization takes into account the uncertainty of the matrix and fiber of composite material properties. From the deterministic and reliability-based optimization results, the effects of design variables on objective function were investigated.