Theory and methods for evaluation of microstructural fatigue damage. The microstructural damage accumulation stage often consumes a significant portion of the total fatigue life of structures. However, its progressive evaluation is beyond the reach of safety inspection techniques which are currently employed to maintain structural integrity and prevent fatigue failures. This project aims to fill this gap by developing innovative methods for the measurement of material properties related to fatig ....Theory and methods for evaluation of microstructural fatigue damage. The microstructural damage accumulation stage often consumes a significant portion of the total fatigue life of structures. However, its progressive evaluation is beyond the reach of safety inspection techniques which are currently employed to maintain structural integrity and prevent fatigue failures. This project aims to fill this gap by developing innovative methods for the measurement of material properties related to fatigue damage and establishing a new theory which links these properties to the remaining life of the structure. The project outcomes will facilitate the global trend towards predictive maintenance strategies, thereby generating substantial cost benefits, specifically, for high-value assets and ageing infrastructure.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101676
Funder
Australian Research Council
Funding Amount
$435,690.00
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 ....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.Read moreRead less