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
Repetitive control systems in networked environments. Repetitive control is used in many industry applications to track periodic references and reject periodic disturbances. The development of digital technology brings in more networked control systems, greatly improving distributed manufacturing, which creates new design challenges due to network-induced constraints such as delay, data packet dropouts and cyber-attacks. This project aims to provide new understanding of dynamic behaviours of rep ....Repetitive control systems in networked environments. Repetitive control is used in many industry applications to track periodic references and reject periodic disturbances. The development of digital technology brings in more networked control systems, greatly improving distributed manufacturing, which creates new design challenges due to network-induced constraints such as delay, data packet dropouts and cyber-attacks. This project aims to provide new understanding of dynamic behaviours of repetitive control systems in networked environments, and develop a new theory for the analysis and design of networked repetitive control, subject to network induced constraints. The new control systems should reduce the cost of automation systems and will significantly enhance their performance, allowing Australian industry to remain economically competitive.Read moreRead less
Electromagnetically Interconnected Suspension for Electrified Vehicles . This project aims to develop an innovative, electromagnetically interconnected suspension system to enhance vehicle ride comfort, stability and handling dynamics, and thus safety of electrified vehicles. Specifically, the project integrates a set of novel electromagnetic shock absorbers to form an effective electrical network so as to realise an electromagnetically interconnected suspension system. Advanced integrated con ....Electromagnetically Interconnected Suspension for Electrified Vehicles . This project aims to develop an innovative, electromagnetically interconnected suspension system to enhance vehicle ride comfort, stability and handling dynamics, and thus safety of electrified vehicles. Specifically, the project integrates a set of novel electromagnetic shock absorbers to form an effective electrical network so as to realise an electromagnetically interconnected suspension system. Advanced integrated control techniques can then be applied to improve vehicle performance and dynamics in three planes. The project will assist the rapid development of transportation electrification. The outcomes from this project will lead to tangible improvements in vehicle comfort and safety.Read moreRead less
Innovative metamaterial magnetorheological technology for mining machines. Hard-rock mining machines have been identified as the next generation mining technology, which will finally replace the traditional drill and blast method to increase productivity and mitigate dangerous working conditions. This project aims to develop innovative metamaterial magnetorheological elastomer joints for a typical hard-rock mining machine to improve the mining efficiency by reducing the vibration. The findings a ....Innovative metamaterial magnetorheological technology for mining machines. Hard-rock mining machines have been identified as the next generation mining technology, which will finally replace the traditional drill and blast method to increase productivity and mitigate dangerous working conditions. This project aims to develop innovative metamaterial magnetorheological elastomer joints for a typical hard-rock mining machine to improve the mining efficiency by reducing the vibration. The findings and outcomes of this research will advance the knowledge and practice of hard-rock mining machines in Australia. The success of this project will significantly increase mining productivity and reduce human injuryRead moreRead less
Cepstral methods of operational modal analysis to separate multiple sources. This project aims to develop new methods of operational modal analysis in situations with multiple complex sources, such as rotating machines. The project will obtain scaled mode shapes as well as separated scaled sources. One of the main applications will be to improve the prognostics of machines by having separated scaled estimates of the forcing functions to make it easier to find fault parameters which trend monoton ....Cepstral methods of operational modal analysis to separate multiple sources. This project aims to develop new methods of operational modal analysis in situations with multiple complex sources, such as rotating machines. The project will obtain scaled mode shapes as well as separated scaled sources. One of the main applications will be to improve the prognostics of machines by having separated scaled estimates of the forcing functions to make it easier to find fault parameters which trend monotonically towards failure, and thus greatly improve the estimates of remaining useful equipment life. An additional benefit of the application will be the ability to predict overall noise radiation from a machine or object if both the sources and modal models are scaled.Read moreRead less
Deep Learning Augmented Intelligent Grinding Mill Simulation and Design. Comminution is a key operation in mineral processing that utilises grinding mills to reduce the size of ore for further mineral enrichment processing. The aim of this project is to provide a step change improvement in the operational efficiency and service life of grinding mills through the development of advanced numerical models to simulate the grinding mill process. The outcome will be a hierarchical deep learning progra ....Deep Learning Augmented Intelligent Grinding Mill Simulation and Design. Comminution is a key operation in mineral processing that utilises grinding mills to reduce the size of ore for further mineral enrichment processing. The aim of this project is to provide a step change improvement in the operational efficiency and service life of grinding mills through the development of advanced numerical models to simulate the grinding mill process. The outcome will be a hierarchical deep learning program to select optimal model parameters from which computational algorithms will optimise grinding mill geometries. This research project will deliver substantial improvements to equipment used to process our most valuable exports and result in immediate industry impact.Read moreRead less