Applying a DOE-ACO Multi-Objective Approach toward Topology Optimization
Publish place: Automotive Science and Engineering، Vol: 9، Issue: 4
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJAEIU-9-4_001
تاریخ نمایه سازی: 4 دی 1402
Abstract:
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-۶۰۶۱) 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, ۵۰ percent weight reduction may be seen in Al-۶۰۶۱ 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 ۵۰% less weight, that has a higher crashworthiness efficiency by increasing SAE more than ۷۰%. As a result of this plan is bridging the gap between standard topological design and multi-criteria optimization.
Keywords:
Ant colony algorithm , Design of experiments , Energy absorption. Thin-walled column , Topology optimization
Authors
Masoud Afrousheh
School of Automotive Engineering, Iran University of Science and Technology
Javad Marzbanrad
School of Automotive Engineering, Iran University of Science and Technology
Sanaz Abdollahzadeh
School of Automotive Engineering, Iran University of Science and Technology
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