The University of Queensland Team
Mingyuan LU, Xuliang Li, Yadan Luo, Zijian Wang & Helen Huang
AI and Machine Learning for Digital Manufacturing
This program develops AI and machine learning models to predict, prevent, and resolve manufacturing challenges. The focus is on creating models that improve automation, product quality, and production efficiency by addressing issues before they arise.
Aims:
Outcomes:
Project 1 – Design and manufacturing of ceramic composite coatings on titanium/steel sheets for enhanced wear, corrosion, and heat resistance
This project will develop a cost-effective laser-based process for applying protective coatings to a novel titanium/steel hybrid plate, offering exceptional resistance to corrosion, wear, and heat at a significantly lower cost than conventional titanium alloys. A central focus is the integration of a deep learning model to enable intelligent, real-time optimisation of the deposition process, improving quality, consistency, and efficiency. The resulting high-performance hybrid plates are well suited for construction in coastal regions and marine infrastructure, where they can significantly reduce maintenance and life-cycle costs. By combining advanced materials engineering with AI-driven process control, the project advances the vision of digital manufacturing and promotes broader industrial adoption of titanium and steel products.
| Personnel | Name, Project Party |
| Project Leader | Prof. Helen Huang , University of Queensland |
| Program Leader (Academic) | Prof. Helen Huang , University of Queensland |
| Partner Investigator | Sihai Jiao, BAOSTEEL PTY LTD |
| Project Personnel | Prof. Han Huang, University of Queensland
Dr Mingyuan LU, University of Queensland Dr Xuliang Li , University of Queensland Yadan Luo, University of Queensland David Orozco Gonzalez, University of Queensland |
Project 2 – AI/ML models for real-time diagnostics and predictive maintenance
This project aims to develop smart devices integrated with AI/ML models for real-time diagnostics and predictive maintenance to support Logan City Council in addressing community infrastructure and safety challenges. Beginning with a feasibility study of current sensing hardware and privacy considerations, the project will progress to developing a dashboard and digital model, followed by secure and scalable deployment. By leveraging multi-sensor data and advanced machine learning, the system will automate monitoring, predict issues, and optimise resource allocation, aligning with the AI/ML4DM framework for data-driven decision-making.
| Personnel | Name, Project Party |
| Project Leader | Prof. Helen Huang , University of Queensland |
| Program Leader (Academic) | Prof. Helen Huang , University of Queensland |
| Partner Investigator | Jinjiang Zhong, Logan City Council Anthony Southon, Logan City Council Research Assistant, Logan City Council |
| Project Personnel | Dr Yadan Luo, University of Queensland
Dr Djamahl Etchegaray, University of Queensland |
Project 3 – AI Powered Standard Operation Procedure Compliance Monitoring Under Steel Making Environment
This project aims to develop an AI-powered SOP compliance monitoring system tailored for the steel making industry, where complex processes demand strict procedural oversight to ensure product quality, and operational efficiency. By leveraging advanced computer vision and vision-language models, the project will automate the monitoring of critical SOP tasks such as gasket placement, flux package loading, etc. The project will develop a SOP compliance monitoring system to improve precision, reduce reliance on manual inspections, and enhance the consistency of operational outcomes.
| Personnel | Name, Project Party |
| Project Leader | Prof. Helen Huang , University of Queensland |
| Program Leader (Academic) | Prof. Helen Huang , University of Queensland |
| Partner Investigator | Tiegen Peng, Baosteel |
| Project Personnel | Dr Yadan Luo, University of Queensland
Dr Zijian Wang, University of Queensland |
