Discovery Early Career Researcher Award - Grant ID: DE210101676

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DE210101676

Funding Status
Active

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Funded Activity Summary

Machine learning-based design of triply periodic minimal surface structures. 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.

Funded Activity Details

Start Date: 01-03-2021

End Date: 29-02-2024

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $435,690.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Solid Mechanics | Mechanical Engineering | Numerical Modelling and Mechanical Characterisation

ANZSRC Socio-Economic Objective (SEO)

Aerospace Equipment | Automotive Equipment | Expanding Knowledge in Engineering |

Other Keywords

Aerospace Equipment | Automotive Equipment | Expanding Knowledge in Engineering | Mechanical Engineering | Numerical Modelling and Mechanical Characterisation | Solid Mechanics

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