{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101740"},"data":{"type":"grant-details","id":"DE260101740","attributes":{"code":"DE260101740","administering-organisation":"University of Wollongong","announcement-administering-organisation":"University of Wollongong","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Physics-Informed Digital Twin for Large-scale Metal Additive Manufacturing. This project aims to develop a physics-informed digital twin framework for metal additive manufacturing (AM) to enhance process monitoring, simulation, and control. By integrating multi-sensor fusion, machine learning, and reinforcement learning, the system will enable real-time defect detection, predictive process modelling, and adaptive control. The outcomes include improved accuracy, reduced defects, and enhanced production efficiency, benefiting the aerospace and automotive industries. This research supports Australia’s national priority in advanced manufacturing, strengthens industry collaboration, and trains future experts in intelligent AM technologies, contributing to a more sustainable and globally competitive manufacturing sector.","funding-current":433360.00,"funding-at-announcement":430079,"investigators-current":[{"title":"Dr","firstName":"LEI","familyName":"YUAN","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-0532-7745 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"LEI","familyName":"YUAN","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-0532-7745 "}],"organisations-current":[{"organisationName":"University of Wollongong","roleName":"Administering Organisation","state":"NSW"}],"organisations-at-announcement":[{"organisationName":"University of Wollongong","roleName":"Administering Organisation","state":"NSW"}],"field-of-research":[{"isPrimary":true,"code":"4014","name":"Manufacturing Engineering","type":"FOR20"},{"isPrimary":false,"code":"401401","name":"Additive Manufacturing","type":"FOR20"},{"isPrimary":false,"code":"401607","name":"Metals and Alloy Materials","type":"FOR20"}],"socio-economic-objective":[{"code":"241001","name":"Industrial Instruments","type":"SEO20"},{"code":"241201","name":"3d Printers and Printing","type":"SEO20"},{"code":"241202","name":"Autonomous and Robotic Systems","type":"SEO20"}],"international-collaboration":["Japan","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"Metal 3D printing is revolutionizing Australia’s advanced manufacturing sector, with transformative applications in aerospace, automotive, medical, infrastructure, and energy. Despite its potential, current metal 3D printing processes face significant challenges, including high defect rates and inefficiencies, which limit their industrial adoption. This project aims to develop a physics-informed digital twin framework that integrates multi-sensor fusion, machine learning, and predictive control to optimize processes in real time, reduce defects, and improve efficiency. By addressing these challenges, the research supports Australia’s 2024 National Science and Research Priorities, enhancing local manufacturing capabilities, reducing reliance on imports, and driving innovation in high-value production.\nThe outcomes will benefit the welding, metal fabrication, and manufacturing industries, thereby strengthening Australia’s global competitiveness. Planned engagement with leading industrial partners, including manufacturers and metal processing companies, will facilitate direct industry adoption. Research findings will be disseminated through industry collaborations, workshops, journal publications, and public outreach programs. This project will position Australia as a leader in intelligent, sustainable manufacturing technologies, benefiting the economy, industry, and workforce while promoting sovereign capabilities and a resilient, technologically advanced manufacturing sector."}}}