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%.