Applying a DOE-ACO Multi-Objective Approach toward Topology Optimization

Thin-walled structures play an important role in absorbing the energy in a low impact crash of vehicles up to saving lives from high impact Injury. In this paper, the thin-walled columns by using a hybrid Design of Experiments (DOE) and Ant Colony Algorithm (ACO) has been optimized. The analysis of the behavior of the nonlinear models under bending load is done using finite-element software Abaqus. The objective is to study the performance geometrically parameters of the columns using DOE-ACO approach. DOE method is being applied to determine the effects of cross-sections, material, and thickness on the energy absorption; and the ACO method is used for finding more accurate thickness on energy absorption. Four types of thin-walled cross-sections, i.e., circle, ellipse, hexagon, and square are used in this study. The optimized results of DOE method show that aluminum alloy (Al-6061) and high strength low alloy steel (HSLA) square columns have a higher energy absorption in comparison with the other cross-sections. However, the amount of absorbed energy in two types of columns is equal but, 50 percent weight reduction may be seen in Al-6061 columns. The columns are re-optimized by ACO to find the best thickness in the last step. In the following, by topology optimization participation, a new plan is proposed by the same thickness and 50% less weight, that has a higher crashworthiness efficiency by increasing SAE more than 70%. As a result of this plan is bridging the gap between standard topological design and multi-criteria optimization.

Applying a DOE-ACO Multi-Objective Approach toward Topology Optimization Keywords:

Ant colony algorithm , Design of experiments , Energy absorption. Thin-walled column , Topology optimization