Lightweight optimization for additive manufacturing parts based on genetic algorithms, metamodels and finite element analysis

Abstract

Additive manufacturing (AM) has become in a competitive method for short series production and high flexibility applications even for functional parts. Few constraints in the manufacturing process involve a great design freedom, allowing minimization of weight by using internal cellular and lattice structures, while minimal mechanical requirements are kept. Weight minimization implies a lower use of material and hence a reduction in manufacturing time, leading to a cost reduction. However, design optimization requires a greater effort in the design process, which also results in more costs. In order to reduce the design process, an optimization method based on genetic algorithms (GAs) and computer aided design/finite element method (CAD/FEM) simulations is proposed to optimize the cellular structure design and minimize the weight for AM parts. New optimization strategies based on GAs combined with surrogate models are evaluated and compared to reduce as much as possible the number of FEM simulations.