Simulation-driven-design of metal lattice structures for a target stress–strain curve

Abstract

Additive manufacturing (AM) allows for the creation of novel complex structures previously impossible using traditional subtractive methods; however, there is a need for new design approaches to fully exploit this potential. Metal lattice structures have attracted attention for their broad customisable range of configurations and potential properties, and to take advantage of these possibilities there is a need for intelligent design tools to optimise the lattice according to the desired application and properties. This study presents and demonstrates a method to automatically design lattice structures which achieve a desired compressive stress–strain curve and satisfy user-defined manufacturing limits. A genetic algorithm iterates selected design variables (unit cell aspect ratio, strut taper, and thickness gradient) and minimises the error between the target stress–strain curve and predicted curve which is determined via automated finite element (FE) modelling of each lattice design. The optimised design is validated through manufacture and compression testing of 17-4PH stainless steel lattice structures, and micro-CT imaging to assess build quality. The tool's versatility is demonstrated by successfully designing lattices to fit a range of target curves. This work demonstrates the potential of this smart inverse design tool to exploit AM and optimise lattice design based on desired performance. The custom written design tool presented in this study is available open source at https://github.com/I-Form/LiDOT.

Keywords

• Lattice Structures
• Design for Additive Manufacturing
• Design and Optimisation
• Finite Element Analysis
• Powder bed fusion