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 proc

Description

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.. Scheme: Discovery Early Career Researcher Award. Field: 4014 - Manufacturing Engineering. Lead: Dr LEI YUAN