Concurrent lattice infill with feature evolution optimization for additive manufactured heat conduction design

Additive manufacturing (AM) eliminates many of the geometric restrictions in conventional manufacturing, and hence complex geometry, such as lattice structures, can be produced with little additional cost. AM designs based on lattice structuring have become increasingly popular as it possesses tunable properties and can be designed to be self-supporting easily. For these reasons, lattice infill recently has been actively studied and a variety of lattice structure topology optimization methods have been developed. On the other hand, lattice infill cannot span the design domain when there are functional features in the mechanical design (e.g. assembly holes and cooling channels). Also, the geometric form of these functional features need to be maintained and cannot be replaced by the lattice structure. Thus far, lattice structure topology optimization considers these features fixed in space without design freedom and obviously, this treatment lacks overall optimality. To fill this critical gap, this work combines the feature evolution into the variable-density lattice structure topology optimization framework, which leads to a concurrent lattice density and feature layout optimization method. Parametric level set functions are employed for the feature representation and R-functions are adopted to combine the density and level set fields. Sensitivity information is calculated on both the lattice densities and feature parameters, in order to solve the problem through a unified gradient-based approach. Several 3D numerical examples are provided to demonstrate the efficiency and robustness of the proposed method.