Machine learning-based design of triply periodic minimal surface structures

Funding: 2022: $145,615
2023: $145,344
2021: $144,731
2021: $145,615
2022: $145,344
2023: $144,731

Project Member(s): Fang, J.

Funding or Partner Organisation: Australian Research Council (ARC DECRA Scheme)
Australian Research Council (ARC DECRA Scheme)

Start year: 2021

Summary: This project aims to develop a new approach to design of new lightweight, crashworthy and manufacturable structures by taking advantage of the latest technologies in computational optimisation, artificial intelligence and additive manufacturing. The study intends to develop a new machine learning-based multiscale design framework to seek optimal triply periodic minimal surface structures, considering fabrication-induced defects and uncertainty. The expected outcome of this project is new methodologies for generating eco-friendly structures with robust mechanical properties in crashing applications. This should provide significant benefits to transport industries by enhancing structural safety and energy saving for next generation vehicles.

FOR Codes: Numerical Modelling and Mechanical Characterisation, Solid Mechanics, Expanding Knowledge in Engineering, Aerospace Equipment, Automotive Equipment

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.