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In this research, a new zeroth-order (non-gradient based) topology optimization methodology for compliance minimization was developed. It is called the Element Exchange Method (EEM). The principal operation in this method is to convert the less effective solid elements into void elements and the more effective void elements into solid elements while maintaining the overall volume fraction constant. The methodology can be integrated with existing FEA codes to determine the stiffness or other structural characteristics of each candidate design during the optimization process. This thesis provides details of the EEM algorithm, the element exchange strategy, checkerboard control, and the convergence criteria. The results for several two- and three-dimensional benchmark problems are presented with comparisons to those found using other stochastic and gradient-based approaches. Although EEM is not as efficient as some gradient-based methods, it is found to be significantly more efficient than many other non-gradient methods reported in the literature such as GA and PSO.

This thesis introduces an optimization software system which supports two separate optimization approaches to solve structural optimization problems with small and large-scale finite element models. The approach for solving the structural optimization problems of small-scale finite element models consists of the gradient-based optimization method and input file regeneration program. The small-scale structural optimization system, requires users only to put in the parameters of the initial design, the system will run the optimization process and generate new models automatically until the solutions are obtained. The approach for solving structural optimization problems of large-scale finite element models combines parametric finite element modeling methods executed by Python scripts with response surface optimization methods (RSM). This approach reduces the number of finite element analyses as well as reduces the optimization process time. The optimization module of the system is performed by the MATLAB optimization toolbox and the Abaqus finite element program with scripts implemented in Python. A benchmark hollow-tube weight-minimization problem is conducted to test the optimization software system. The percent difference between the solution found by the graphical optimization method and the solution found by the 3D beam finite element model with Sequential Quadratic Programming (SQP) solver and the graphical optimization method is 1.99%. The percent difference between the results from the 3D beam finite element model with SQP solver and the result from 3D brick finite element model with response surface method is 8.16%. The percent difference between the results from the 3D brick finite element model with RSM and the result from the graphical optimization method is 10.31%. / Master of Science / Commercial structural optimization software packages which integrate modeling tools, optimization and extensive computational tools such as a finite element solver were developed and pushed to the market. However, some commercial approaches to structural optimization are not very general. In addition, the commercial codes are designed for a specific-purpose, and they may not be suitable in many cases. If the commercial codes do not properly represent the structural optimization problem, users have to write custom Python scripts to assist the software system in retrieving data from the .odb files generated by FEA software. This thesis introduces an optimization software system which supports two separate optimization approaches to solve structural optimization problems with small and large-scale finite element models. The optimization module of the system is performed by the MATLAB optimization toolbox and the Abaqus finite element program with scripts implemented in Python. This optimization software system allows users to extract and manipulate data for optimization without limitations. Furthermore, once the required parameters are input in the system, the scripting software creates the finite element model and proceeds with the optimization automatically.

The detection of internal log defects has been shown to have a potential for increasing the lumber value. As an alternative to other available expensive log scanning devices, a method using radio frequency waves has been used to detect the knots. The main focus of the current research is to investigate the effectiveness of using radio frequency waves to detect the knots. Electrostatic finite element analysis is performed to predict the defects in logs. A script has been written using the commercial finite element ANSYS software to predict defects in log sections. The results are then compared with the experimental data measured on actual log sections. Analysis proved that it is possible to detect presence of knots in the log sections.

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