Su-gil Cho

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.

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.

Study on Damage Detection Method using Meta Model

This paper presents an effective damage detection method using a meta model. A meta model is an approximation model that uses the relations between the design and response variables. It eliminates the need for repetitive analyses of computationally expensive models during the optimization process. In this study, a response surface model was employed as the meta model. The surface model was estimated using the correlation of the stiffness and natural frequencies of the structures. The locations and values of the damages were identified using a meta model-based damage detection method. Two numerical examples (a cantilever beam and jacket structure) were considered to verify the performance of the proposed method. As a result, the damages to the structures were accurately detected.

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.

Metamodel-Based Multidisciplinary Design Optimization of a Deep-Sea Manganese Nodules Test Miner

A deep-sea manganese nodules test miner has not only coupled relationship between system components but also various design requirements of each system to meet the specified multitasks. To accomplish the multiobjectives of complex systems, multidisciplinary design optimization (MDO) is performed. Metamodels such as the kriging model and the response surface model are employed to reduce computational costs for MDO and to integrate component systems in a design framework. After verifying the accuracy of each metamodel, metamodel-based MDO for a deep-ocean test miner is formulated and performed. Finally, results and advantages of the proposed design methodology are discussed.

A Study on the Simulation-based Design for Optimum Arrangement of Buoyancy Modules in Marine Riser System

*** 한국해양과학기술원 부설 선박해양플랜트연구소 해양플랜트산업기술센터 ABSTRACT: This paper reports a simulation-based design method for the optimized arrangement design of buoyancy modules in a marine riser system. A buoyancy module is used for the safe operation and structural stability of the riser. Engineers design buoyancy modules based on experience and experimental data. However, they are difficult to design because of the difficulty of conducting real sea experiments and quantifying the data. Therefore, a simulation-based design method is needed to tackle this problem. In this study, we developed a simulation-based design algorithm using a multi-body dynamic simulation and genetic algorithm to perform optimization arrangement design of a buoyancy module. The design results are discussed in this paper.

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.

Efficient Robust Design Optimization Using Statistical Moments Based on Multiplicative Decomposition Method

The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

Efficient Robust Design Optimization Using Statistical Moment Based on Multiplicative Decomposition Considering Non-normal Noise Factors

The performance of a system can be affected by the variance of noise factors, which arise owing to uncertainties of the material properties and environmental factors acting on the system. For robust design optimization of the system performance, it is necessary to minimize the effect of the variance of the noise factors that are impossible to control. However, present robust design techniques consider the variation of design factors, and not the noise factors, as being important. Furthermore, it is necessary to assume a normal distribution; however, a normal distribution is often not suitable to estimate the variations. In this study, a robust design technique is proposed to consider the variation of noise factors that are estimated as non-normal distributions in a real experiment. As an example of an engineering problem, a deep-sea manganese nodule miner tracked vehicle is used to demonstrate the feasibility of the proposed method.

Taguchi Robust Design of Tracked Vehicle for Manganese Nodule Test Miner in Collecting Operation Considering Deep-sea Noise Factors

A deep-sea manganese nodule miner consists of 4 parts: the pickup device, crusher, disposal device, and tracked vehicle. The tracked vehicle is an essential component to keep the self-propelled miner moving across deep-sea soil. The performances of the tracked vehicle are influenced by noise factors: the shear strength of the seafloor, bottom current, seafloor slope, track speed, reaction forces of flexible hose, etc. It is necessary to adopt a robust design method that improves the performances and minimizes the variation caused by noise factors. Taguchi`s method, the most widely known robust design method, searches for the robust optimum using an orthogonal array composed of the product of the inner array and outer array. In this paper, we propose a new screening technique to reduce the number of input factors and apply the MRSN (Multi-Response Signal to Noise) ratio to convert multiple performances into single one in order to overcome the difficulties and limitations of using Taguchi`s method in a case with many input factors and multiple performances. A test miner was already designed and tested. It has about 1/10 the capacity of a commercial one and was successfully operated at an in-shore area. Taguchi`s robust design was applied to the tracked vehicle of the test miner, and design improvements were implemented for the vehicle.