Gabseong Lee

Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display

Design of an optical pattern in a light-guide panel (LGP) has relied on empirical methods. However, the characteristics of developing liquid-crystal display (LCD) products such as frequent design modifications, various design conditions, and a short development period make it difficult for the empirical design approach to cope with various design requirements for size, shape, and optical performance of the LCD products. The most important tasks for the design of LGPs are improving average illuminance and the uniformity of the backlight unit. To meet these requirements, a design for an incoupling and an outcoupling part of the LGP is presented. These two parts can be designed in two separate phases: the first for the incoupling part and the second for the outcoupling part. The shape of serration in the incoupling part was first determined by design of experiments, and the dot patterns in the outcoupling part were subsequently determined by a density-based approach with progressive quadratic response surface modeling. Using this design approach, the illuminance was increased from 2241 lx in the initial design to 2299 lx in the optimal design, and its uniformity also increased from 38% to 82%.

Material Optimization of BIW for Minimizing Weight

In this study, we propose the method of optimally changing material of BIW for minimizing weight while satisfying vehicle requirements on static stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the linear polynomial regression (PR) model. Using the linear PR model, optimization is carried out an evolutionary algorithm (EA) that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 44.8% while satisfying all the design constraints.In this study, we propose the method of optimally changing material of BIW for minimizing weight while satisfying vehicle requirements on static stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the linear polynomial regression (PR) model. Using the linear PR model, optimization is carried out an evolutionary algorithm (EA) that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 44.8% while satisfying all the design constraints.

Multidisciplinary Design Optimization of Vehicle Front Suspension System Using PIDO Technology

Multidisciplinary design optimization (MDO) for a suspension component of the vehicle front suspension was performed in this research. Shapes and thicknesses of the subframe were optimized to satisfy multi-disciplinary design requirements; weight, fatigue, crash, noise, vibration, and harshness (NVH), and kinematic and compliance (K&C). Analyses procedures of the performance disciplines were integrated and automated by using the process integration and design optimization (PIDO) technique, and the integrated and automated analyses environments enabled various types of analytic design methodologies for solving the MDO problem. We applied an approximate optimization technique which involves sequential sampling and metamodeling. Since the design variables for thicknesses should be dealt as discrete variables. the evolutionary algorithm is selected as optimization technique. The MDO problem was formulated three types of problems according to the order of priorities among the performance disciplines, and the results of MDO provided design alternatives for various design situations.

Reliability-Based Design Optimization Using Kriging Metamodel with Sequential Sampling Technique

RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.

A Sampling-based Reliability-Based Design Optimization Using Kriging Metamodel with Constraint Boundary Sampling

First Order reliability Method (FORM) is the most common approach to calculate probability of failure for reliability analysis. It is used in Reliability Based Design Optimization (RBDO) and provides acceptable solutions. However, for problems involving highly nonlinear performance functions, this method may not provide reliable results. Compared to FORM, Monte Carlo Simulation (MCS) is more accurate and provides a simple sampling method. The limitations of MCS are that it is very computationally intensive and may not be used in gradient-based optimization algorithms for RBDO. In order to overcome the disadvantages of both methods, we propose a new RBDO method based on a Latin Hypercube Sampling (LHS) using the Kriging metamodel with Constraint Boundary Sampling (CBS). In this new method, an MPP search is not required and it can provide more improved computational efficiency than the MCS techniques for reliability analysis. A LHS technique applied to the Kriging metamodel is used in this work to assess the uncertainty in a design, which can be incorporated with a gradient based optimizer for RBDO. Because the proposed method can make use of analytic sensitivities and smoothness of probability of failure estimate using the Cumulative Distribution Function (CDF) and Probability Density Function(PDF), which are constructed using Moving Least Square (MLS). RBDO test problems are used to demonstrate the effectiveness of the proposed method in reducing the number of function evaluations.

Material Arrangement Optimization for Automotive BIW considering a Large Number of Design Variables

Weight reduction of a automobile has been steadily tried in automotive industry to improve fuel efficiency, driving performance and the production profits. Since the weight of BIW takes up a large portion of the total weight of the automobile, reducing the weight of BIW greatly contributes to reducing the total weight of the vehicle. To reduce weight, vehicle manufacturers have tried to apply lightweight materials, such as aluminum and high-strength steel, to the components of BIW instead of conventional steel. In this research, material arrangement of an automotive BIW was optimized by formulating a design problem to minimize weight of the BIW while satisfying design requirements about bending and torsional stiffness and perform a metamodel-based design optimization strategy. As a result of the design optimization, weight of the BIW is reduced by 45.7% while satisfying all design requirements.

Material Selection Optimization of A-Pillar and Package Tray Using RBFr Metamodel for Minimizing Weight

Abstract : In this study, we propose the method of optimally selecting material of front pillar (A-pillar) and package tray for minimizing weight while satisfying vehicle requirements on static stiffness and dynamic stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness and dynamic stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimi-zation, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the radial basis function regression (RBFr). Using the RBFr models, optimization is carried out an evolutionary algorithm that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 49.8% while satisfying all the design constraints.

Design optimization of one-time-use leaping mechanism for sensor node relocation

This paper proposes a design automation method to optimize one-time-use leaping mechanism for relocating energy-constrained sensor nodes. The leaping mechanism is expected to enhance coverage and connectivity of sensor networks initially randomly deployed with minimum energy consumed. Of particular interest is proper relocation of isolated nodes under uncertain environment conditions. Specifically, we consider how the aerodynamic disturbance can be minimized with an optimized launch angle of the leaping mechanism. To construct an automated simulation and design environment, the process integration and design optimization (PIDO) approach is employed. We not only obtain an optimum solution satisfying all imposed requirements, but also demonstrate an automated design process for controlled node mobility.

Reliability-Based Design Optimization Using Enhanced Dimension Reduction Method With Variable Sampling Points

Reliability-Based Design Optimization (RBDO) is an effective method to handle an optimization problem constrained by reliability performance. In spite of its great benefits, one of the most challenging issues for implementing RBDO is associated with very intensive computational demands of Reliability Analysis (RA). Moreover, an accurate and efficient RA method is indispensible to apply RBDO to practical engineering design problems. Among various RA methods, an enhanced Dimension Reduction (eDR) method is the most popular one due to the high computational efficiency. It is very desirable to obtain an accurate and efficient RA result by using the minimum number of sampling points. But, it is difficult to determine it. That is because it depends on the nonlinearity of a constraint from approximating a model and the degree of uncertainty from integrating a design factor. In this research, eDR method with variable sampling points has been studied and proposed to resolve the early mentioned difficulties. The main idea of the suggested method is to employ a different number of axial sampling points for each random design factor. It is according to the nonlinearity of a constraint and the degree of uncertainty of each random design factor. For each random variable, it begins to use three points first and decides to stop or increase the axial sampling points based upon the proposed criteria in this study. In case of increasing sampling points, it is incremented by one sampling point and ended up five sampling points at most. As it shown in the result, the efficiency of eDR method with variable sampling points for each random variable is superior to the one with fixed sampling points without sacrificing any accuracy. Through the three representative RA problems, it is verified that the proposed RA method generates the result 26.5% more efficiently on average than the conventional eDR method with fixed sampling points. Furthermore, the Performance Measure Approach (PMA) was used to evaluate the performance of RBDO using the new RA method. For the comparison, three mathematical and one engineering RBDO problems were solved by both eDR method with variable sampling points and conventional one with fixed sampling points. Finally, the comparison results clearly demonstrate that RBDO using the suggested RA method is superior to the conventional one in terms of accuracy and efficiency.Copyright

Design Optimization Considering Optical Performances for LCD/BLU Using PIDO

Among many kinds of parts for Liquid Crystal Display (LCD), a Back Light Unit (BLU) that changes the path of the light from light source to screen is the most important part to improve optical performances such as uniformity and average value of brightness. Up to recently, design process of BLU has been carried out by depending on the experience of design engineer. Using this approach, however, is proven that it is hard to effectively deal with difficulties in a process of the LCD development such as frequent design modifications, various design requirements, and short-term development. To cope with this situation, we applied a Process Integration and Design Optimization (PIDO) based design environment. PIDO is a software package to integrate multiple simulation processes and find a better solution using various design techniques. In this research, we found a design solution satisfying all design requirements by using the PIDO.