Semyung Wang

A new segmentation method for point cloud data

In the process of generating a surface model from point cloud data, a segmentation that extracts the edges and partitions the three-dimensional (3D) point data is necessary and plays an important role in fitting surface patches and applying the scan data to the manufacturing process. Many researchers have tried to develop segmentation methods by fitting curves or surfaces in order to extract geometric information, such as edges and smooth regions, from the scan data. However, the surface- or curve-fitting tasks take a long time and it is also difficult to extract the exact edge points because the scan data consist of discrete points and the edge points are not always included in these data. In this research, a new method for segmenting the point cloud data is proposed. The proposed algorithm uses the octree-based 3D-grid method to handle a large amount of unordered sets of point data. The final 3D-grids are constructed through a refinement process and iterative subdivisioning of cells using the normal values of points. This 3D-grid method enables us to extract edge-neighborhood points while considering the geometric shape of a part. The proposed method is applied to two quadric models and the results are discussed.

Maximization of acoustic energy difference between two spaces

There has recently been an increasing interest in the generation of a sound field that is audible in one spatial region and inaudible in an adjacent region. The method proposed here ensures the control of the amplitude and phase of multiple acoustic sources in order to maximize the acoustic energy difference between two adjacent regions while also ensuring that evenly distributed source strengths are used. The performance of the method proposed is evaluated by computer simulations and experiments with real loudspeaker arrays in the shape of a circle and a sphere. The proposed method gives an improvement in the efficiency of radiation into the space in which the sound should be audible, while maintaining the acoustic pressure difference between two acoustic spaces. This is shown to give an improvement of performance compared to the contrast control method previously proposed.

DESIGN SENSITIVITY ANALYSIS OF STRUCTURE-INDUCED NOISE AND VIBRATION

A continuum design sensitivity analysis (DSA) method for dynamic frequency responses of structural-acoustic systems is developed using the adjoint variable and direct differentiation methods. A variational approach with a non-self-adjoint operator for complex variables is used to retain the continuum elasticity formulation throughout derivation of design sensitivity results. It is shown that the adjoint variable method is applicable to the variational equation with the non-self-adjoint operator. Sizing design variables such as the thickness and cross-sectional area of structural components are considered for the design sensitivity analysis. A numerical implementation method of continuum DSA results is developed by postprocessing analysis results from established finite element analysis (FEA) codes to obtain the design sensitivity of noise and vibration performance measures of the structural-acoustic systems. The numerical DSA method presented in this paper is limited to FEA and boundary element analysis (BEA) is not considered. A numerical method is developed to compute design sensitivity of direct and modal frequency FEA results. For the modal frequency FEA method, the numerical DSA method provides design sensitivity very efficiently without requiring design sensitivities of eigenvectors. The numerical method has been tested using passenger vehicle problems. Accurate design sensitivity results are obtained for analysis results obtained from established FEA codes.

Efficient Response Surface Modeling by Using Moving Least-Squares Method and Sensitivity

The response surface method (RSM) has currently become one of the better-known meta-modeling techniques. However, its approximation errors have placed several restrictions on designers as classical RSM uses the leastsquares method (LSM) to find the best-fitting approximation models from the given function data. We discuss how to construct RS models efficiently and accurately using the moving least-squares method (MLSM) combined with sensitivity information. The formulations for incorporating the sensitivity using the MLSM are derived. With this method, several parameters should be determined during the construction of response surfaces. The parametric study and optimization for these parameters are performed. However, because of the discontinuity problem of the optimization, a genetic algorithm is adopted. The correlation coefficient is used for the normalized comparison between the function and gradient errors. Also, the reciprocal condition number is applied to avoid illconditioned approximations. Several difficulties and their respective solutions during the approximation processes are described, and the numerical examples are then demonstrated to verify the accuracy and the efficiency of this method. If the sensitivity of each sampling point can be calculated efficiently by utilizing a cheap computation, the proposed method is recognized as very efficient and accurate.

Reliability-based topology optimization with uncertainties

This research proposes a reliability-based topology optimization (RBTO) using the finite element method. RBTO is a topology optimization based on probabilistic (or reliability) constraints. Young’s modulus, thickness, and loading are considered as the uncertain variables and RBTO is applied to static and eigenvalue problems. The RBTO problems are formulated and a sensitivity analysis is performed. In order to compute probability constraints, two methods—RIA and PMA— are used. Several examples show the effectiveness of the proposed method by comparing the classical safety factor method.

Topology optimization of nonlinear magnetostatics

The topology optimization of nonlinear magnetostatic systems is studied using the finite-element method. The topology design sensitivity formulation of nonlinear magnetostatics is derived using the adjoint variable method. A computer program is developed using object-orient programming and applied to the topology optimization of a C-core actuator. A numerical study shows the effect of saturation by comparing linear and nonlinear topology optimization.

Topology optimization of electromagnetic systems considering magnetization direction

The topology optimization of electromagnetic (EM) systems considering the effect of the magnetization direction in the magnet is investigated using the finite element method. The density method is used for topology optimization and continuum design sensitivity analysis is used for the sensitivity of EM systems. Also ANSYS is used for analysis. The force of a magnet is divided into two components of x and y direction in the rectangular coordinates. And the topology optimization of a c-core and a brushless dc motor are obtained.

Acoustic design sensitivity analysis and optimization for reduced exterior noise

A program, global acoustic design sensitivity analyzer, is developed that can perform a global acoustic design sensitivity analysis of exterior noise with respect to structural sizing design variables. A system for global acoustic design sensitivity is introduced and implemented numerically by employing the continuum sensitivity analysis. A half-scale automobile cavity model is considered as a numerical example. By using a continuum method, we obtained accurate and efe cient sensitivities when the number of design variables was large. Also, the tendency plotsof element sensitivities and energy contribution for global acousticsensitivities areavailable. Finally, a design optimization was performed to simultaneously reduce the weight and sound pressure level at interesting points and frequencies. I. Introduction T HE range of engineering problems that can be solved through numericalanalyses hasbeen greatlybroadened with the advent of high-speed digital computers. In addition, the development of the e nite element method (FEM)and the boundary element method (BEM) has also been important to engineering analysis. These two typesofanalysesareassociatedwithanalyticalstructure-bornenoise prediction. FEM is commonly used to compute the vibration of the structure emitting noise, and BEM can be used to predict the generated noise. Other methods for acoustic prediction, such as statistical energy analysis, are also available. In this paper, the focus is on FEM and BEM. In a gradient-based optimization scheme, it is important to have accurate gradients (sensitivities ) of the objective function and constraints with respect to the design variables. Formulation of global acoustic sensitivity through chain-ruled derivatives using FEM and BEM has been studied and implemented by many researchers. Coyette et al. 1 have previously investigated the computation and utilizationofacousticsensitivitieswith respecttosizingdesignvariables. Two types of sensitivities were considered. One was acoustic sensitivity with respect to the normal velocity ofvibratingstructure, and the other was structural sensitivity of structural velocities with respect to physical sizing design variables such as thickness. The acoustic sensitivities and structural sensitivities were calculated using BEM and FEM, respectively, and then these sensitivities were combined to obtain a globalacousticsensitivity.In addition,this approach was implemented to the commercial code, SYSNOISE. The same approach was presented in the articles by Cunefare et al., 2;3 who focused on e nding the best optimization formulation by comparing the relative performance and results obtained through the use of several different objective functions and constraints. They obtained acoustic sensitivities and structural sensitivities from the commercial codes NASTRAN and COMET. They also developed COMIN to combine the acoustic and structural sensitivities. Most researchers who studied global acoustic design sensitivity analysis (DSA) and acoustic optimization have used the structural sensitivity analysis module supported by commercial codes and have thus faced limitations in the accuracy and number of design variables. There are several reasons for the limitations of the semianalytical method. First, when the semianalytical method is used forstructural sensitivity analysis, a slight error could occur due to the amount of perturbation. For this paper, we used a continuum approach 4i6 to calculate the structural sensitivity combined with acoustic sensitiv

Shape optimization of BLDC motor using 3-D finite element method

A shape design sensitivity analysis for magnetostatic fields is developed using the 3-D finite element method. Until recently, the 2-D design sensitivity analysis for shape optimization was widely used due to the 2-D finite element method. The 3-D design sensitivity formulations are analytically derived using the continuum approach, the domain integration method, and the adjoint variable method. A magnetic sensitivity program, MAGSEN is developed using object orient programming and validated by applying it to the shape optimization of 8-pole 12-slot BLDC spindle motor.

Application of Reliability-Based Topology Optimization for Microelectromechanical Systems

In reliability-based design optimization, the constraints consider the probability of the satisfaction/failure of critical events. Lately, reliability-based design optimization has been applied to topology optimization, resulting in the development of reliability-based topology optimization. And though reliability-based topology optimization can be a useful and meaningful method, it requires excessive computational resources. Therefore, this research proposes a parallel-computed reliability-based topology optimization using the response surface method. This paper demonstrates that the proposed method greatly reduces the computation requirement of reliability-based topology optimization. The proposed methodology is then applied to design microelectromechanical systems. Specifically, in microelectromechanical systems, reliability-based topology optimization can be highly effective because of randomness generated during the etching process and scaling effect. The proposed method successfully designs new devices and verifies the designs via experiment.