Junyong Jang

Forced Vibration Analysis of an IPM Motor for Electrical Vehicles due to Magnetic Force

This paper discusses a method to analyze the forced vibration of an interior permanent magnet (IPM) motor due to magnetic force. A structural finite element (FE) model of the IPM motor that includes stiffness of ball bearings and laminated effect of core is developed and verified by comparing the simulated natural frequencies and mode shapes with experimental data. A magnetic FE model is also developed and both the radial and tangential magnetic forces are calculated in the air-gap. These forces are subsequently transformed into equivalent nodal forces and applied to the structural FE model to investigate the forced vibration characteristics of the IPM motor. It was found that the magnetically induced vibration of a stator mainly results from the contribution of the dominant harmonics of the magnetic force and structural resonance. In addition, the magnetically induced vibration of the rotor mainly appeared as rigid body modes due to the flexibility of ball bearings.

Reliability-Based Robust Design Optimization With Kernel Density Estimation for Electric Power Steering Motor Considering Manufacturing Uncertainties

Reliability-based robust design optimization (RBRDO) is a notable method to secure the quality and feasibility of performances from uncertainties. In this paper, geometric uncertainties of a stator sheet that can occur during a stamping process are considered as the uncertainty of controllable variables. Then, as the uncertainty of uncontrollable variables, skew angle due to inexact lamination is considered. No researches on the effect of this uncertainty have been performed. To analyze the effects of the uncertainties, the kernel density estimation (KDE) was employed to estimate the distribution because of its convenience, flexibility, and robustness. Then, RBRDO with the KDE is performed and the optimum results are compared with real data measured from manufactured motors.

Vibration and Noise in a HDD Spindle Motor Arising from the Axial UMF Ripple

We investigated numerically the characteristics of axial unbalanced magnetic force (UMF) due to axial magnetic design in the spindle motor of a hard disk drive (HDD). The HDD spindle motor has a magnet-overhang and a pulling plate to generate the axial magnetic force, which is applied to the fluid dynamic bearings (FDBs) as a preload in order to increase the axial stiffness of the HDD spindle system. However, the axial UMF ripple with the least common multiple (LCM) harmonics of pole and slot is generated by the pulling plate and magnet-overhang in the HDD spindle motor. We also investigated the characteristics of the axial magnetic forces generated in the pulling plate and the stator core, separately. We found that the axial magnetic forces generated in the pulling plate and the stator core have opposite phases. Furthermore, we proposed an optimal position of permanent magnet with respect to the stator core. We experimentally verified that the LCM harmonics of pole and slot in the vibration and acoustic noise mostly originate from axial UMF ripple and that the proposed design can effectively minimize the LCM harmonics in the vibration and acoustic noise of HDD systems.

Design Optimization of PZT-Based Piezoelectric Cantilever Beam by Using Computational Experiments

Piezoelectric energy harvesting is gaining huge research interest since it provides high power density and has real-life applicability. However, investigative research for the mechanical–electrical coupling phenomenon remains challenging. Many researchers depend on physical experiments to choose devices with the best performance which meet design objectives through case analysis; this involves high design costs. This study aims to develop a practical model using computer simulations and to propose an optimized design for a lead zirconate titanate (PZT)-based piezoelectric cantilever beam which is widely used in energy harvesting. In this study, the commercial finite element (FE) software is used to predict the voltage generated from vibrations of the PZT-based piezoelectric cantilever beam. Because the initial FE model differs from physical experiments, the model is calibrated by multi-objective optimization to increase the accuracy of the predictions. We collect data from physical experiments using the cantilever beam and use these experimental results in the calibration process. Since dynamic analysis in the FE analysis of the piezoelectric cantilever beam with a dense step size is considerably time-consuming, a surrogate model is employed for efficient optimization. Through the design optimization of the PZT-based piezoelectric cantilever beam, a high-performance piezoelectric device was developed. The sensitivity of the variables at the optimum design is analyzed to suggest a further improved device.

Reliability-based design optimization of an automotive structure using a variable uncertainty

Reliability-based design optimization is an optimization technique based on the stochastic approach. Many studies using this approach assume the uncertainty in the design variable to be constant. However, when the uncertainty depends on the values of the design variable, this assumption results in the wrong conclusions. Therefore, the uncertainty should be considered as a variable in reliability-based design optimization. The uncertainty in the thickness during optimization, such as the tolerance, had been assumed to be a constant in automotive structures. However, in practice, the tolerance of the thickness depends on the nominal thickness. Hence, in this paper, reliability-based design optimization of an automotive structure such as an engine cradle and a body-in-white with a variable uncertainty is carried out. General Motors Korea provides the tolerance guide which defines the dependence between the nominal thickness and the tolerance. The information is adopted to define the variable uncertainty. Thus, the variable uncertainty can modify the uncertainty with respect to the design point, resulting in an accurate reliability estimation. Finally, reliability-based design optimization with a variable uncertainty is performed using the Akaike information criterion method which determines the fittest distribution of the performance based on the maximum likelihood estimation of the candidate distributions. Consequently, the automotive structures are optimized to reduce the mass while still satisfying the target reliabilities of the performances when considering a variable uncertainty.

Reliability-Based Optimum Tolerance Design for Industrial Electromagnetic Devices

Principle of tolerance design is receiving increased research focus to determine the optimal tradeoff between manufacturing cost and quality. However, current tolerance designs are not suitable for products such as electromagnetic devices that require high reliability, i.e., a very low failure rate. In this paper, a new tolerance design named as reliability-based optimum tolerance design is formulated and performed to guarantee the high reliability of the products while maximizing manufacturing tolerances. The proposed method quantifies the reliability using reliability analysis, which reflects the tolerance in the tolerance design. To validate the proposed method, tolerance design is applied to two examples: 1) a magnetic circuit ( C -core) and 2) a mass-produced interior permanent magnet motor that contains manufacturing tolerances of permanent magnet.

Taguchi Robust Design of Back Electromotive Force Considering the Manufacturing Tolerances in IPMSM

This paper presents the robust design of back electromotive force (EMF) characteristic analysis considering the manufacturing tolerances of the permanent magnets (PM) in the interior permanent magnet synchronous motor (IPMSM). We choose the design variables that make an impact on the EMF, and then perform the robust design of EMF using the Taguchi method in order to reduce the effect of variation of the noise variables caused by the manufacturing tolerances. Taguchi method, the most widely known robust design method, is searched for the robust optimum using orthogonal array composed of the product of inner array and outer array. Finally, the robust optimum combination of design variables is selected basis on the result about the manufacturing tolerances of PM. The optimization is verified by comparison of average and variance between robust model and initial model according to the variation of noise factor.

대체모델의 정확성 및 강건성 향상을 위한 가중함수 기반 순차 최소거리최대화계획

효율적인 최적설계를 위해 공학분야에 도입된 대체모델의 정확성은 표본점에 큰 영향을 받는다. 대체모델의 정확성을 높이는 방법으로 기 추출한 응답을 이용하는 순차실험계획이 제안되었다. 크리깅 대체모델의 상관계수를 가중치로 적용하여 대체모델의 정확성을 향상시킨 연구가 있었으나, 주어진 정보가 부족하거나 상관계수가 잘못 추정된 경우 표본점이 잘못 추출되어 대체모델의 강건성이 저하된다. 본 논문에서는 기존 순차실험계획의 여러 문제점을 제시하고, 이를 해결하기 위한 가중함수 기반 순차 최소거리최대화계획을 제안한다. 제안하는 순차실험계획의 효용성을 수학 함수에 적용하여 기존 순차실험계획들과 비교하여 정확성과 강건성이 향상됨을 예시한다.

Reliability-based Design Optimization for Lower Control Arm using Limited Discrete Information

Lower control arm (LCA) is a part of chassis in automotive. Performances of LCA such as stiffness, durability and permanent displacement must be considered in design optimization. However it is hard to consider different performances at once in optimization because these are measured by different commercial tools like Radioss, Abaqus, etc. In this paper, firstly, we construct the integrated design automation system for LCA based on Matlab including Hypermesh, Radioss and Abaqus. Secondly, Akaike information criterion (AIC) is used for assessment of reliability of LCA. It can find the best estimated distribution of performance from limited and discrete stochastic information and then obtains the reliability from the distribution. Finally, we consider tolerances of design variables and variation of elastic modulus and achieve the target reliability by carrying out reliability-based design optimization (RBDO) with the integrated system.

An Efficient Constraint Boundary Sampling Method for Sequential RBDO Using Kriging Surrogate Model

대체모델을 이용한 신뢰성기반 최적설계에서 최적해와 신뢰도의 정확성은 제한조건경계의 대체모델의 정확도에 영향을 받는다. 기존 제안된 제한조건경계 샘플링 기법은 제한조건경계에 실험점을 생성하여 이러한 정확성을 높일 수 있었다. 하지만, 제한조건경계 샘플링 기법은 최적해와 먼 부근의 제한조건경계에도 불필요한 실험점을 생성하여 과도한 계산비용이 발생한다. 본 논문에서는 크리깅 대체모델의 통계적 정보를 이용하여 최적해 근처의 제한조건경계에 실험점을 생성하는 효율적인 제한조건경계 샘플링 기법을 제안한다. 제안한 기법의 효율성과 정확성은 수학예제를 통하여 확인한다.