Abstract:

Lattice structures can be designed to achieve unique mechanical properties and have attracted increasing attention for applications in high-end industrial equipment, along with the advances in additive manufacturing (AM) technologies. In this work, a novel design of plate lattice structures described by a parametric model is proposed to enrich the design space of plate lattice structures with high connectivity suitable for AM processes. The parametric model takes the basic unit of the triple periodic minimal surface (TPMS) lattice as a skeleton and adopts a set of generation parameters to determine the plate lattice structure with different topologies, which takes the advantages of both plate lattices for superior specific mechanical properties and TPMS lattices for high connectivity, and therefore is referred to as a TPMS-like plate lattice (TLPL). Furthermore, a data-driven shape optimization method is proposed to optimize the TLPL structure for maximum mechanical properties with or without the isotropic constraints. In this method, the genetic algorithm for the optimization is utilized for global search capability, and an artificial neural network (ANN) model for individual fitness estimation is integrated for high efficiency. A set of optimized TLPLs at different relative densities are experimentally validated by the selective laser melting (SLM) fabricated samples. It is confirmed that the optimized TLPLs could achieve elastic isotropy and have superior stiffness over other isotropic lattice structures.

Key words: lattice structure, triple periodic minimal surface (TPMS), plate lattice, structural optimization, machine learning