Author(s)

David Webb¹, Qian Liu², Wissam Alobaidi^2*, Eric Sandgren^2*

¹Axalta Coating Systems, Front Royal, USA.
²Systems Engineering Department, Donaghey College of Engineering & Information Technology, University of Arkansas at Little Rock, Little Rock, USA.

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.

Topological Design, Structural Optimization, Genetic Optimization, Variable Material Design

Webb, D. , Liu, Q. , Alobaidi, W. and Sandgren, E. (2017) Topological Design via a Rule Based Genetic Optimization Algorithm. American Journal of Computational Mathematics, 7, 291-320. doi: 10.4236/ajcm.2017.73023.