Topological Design via a Rule Based Genetic Optimization Algorithm

• 作者: David Webb Eric Sandgren Qian Liu Wissam Alobaidi
• 来源期刊: 美国计算数学期刊(英文)
• 学科: 医学
• 关键词: TOPOLOGICAL DESIGN Structural OPTIMIZATION GENETIC OPTIMIZATION VARIABLE Material DESIGN
• 年，卷（期）: 2017,（3）
• 页码范围: 291-320
• 页数: 30页
• 分类号: R73

摘要

A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.