Digital Twins for Digital Manufacturing

This program focuses on creating advanced technologies to develop Digital Twins for key manufacturing entities like machines, personnel, products, and processes. These Digital Twins will improve production efficiency, ensure product quality, reduce maintenance costs, and enhance sustainability.

Aims:

  • Develop novel technologies to model industrial machines, personnel, products, and processes.

  • Create techniques for linking physical entities with their digital counterparts through Digital Twins.

  • Establish a framework for managing the lifecycle of Digital Twins for continuous improvement.

Outcome:

  • This program will reduce the cost and effort of creating Digital Twins by minimising the need for extensive individual programming and decreasing the overall number of Digital Twins required, thereby streamlining processes and enhancing efficiency for our partners.

Project 1 – AI Models for Digital Manufacturing

This project will develop AI/ML models and an open platform to enhance digital manufacturing, addressing predictive maintenance, product quality, and production efficiency. The AI/ML models will proactively predict, prevent, and fix issues using Digital Twins, while the platform will provide a flexible framework for model integration, runtime management, and edge computing for seamless deployment across various manufacturing environments. Outcomes include autonomous diagnostics, improved product consistency, optimised production planning, and a resilient, scalable edge system for real-time manufacturing support.

Personnel Name, Project Party
Project Leader Prof. Yang Xiang, SWINBURNE
Program Leader (Academic) Prof. Prem Prakash Jayaraman, SWINBURNE
Partner Investigator Lin He, SYSBOX
Project Personnel Prof. Dimitrios Georgakopoulos, SWINBURNE

Dr. Wei Zhou, SWINBURNE

Dr Wanlun Ma, SWINBURNE

Dr Sheng Wen, SWINBURNE

PhD (to be hired)

Project 2 – Design and Development of Single Source of Truth (SSoT) Knowledgebase to enable data-driven Productivity Improvements

This project aims to design and develop a Single Source of Truth (SSoT) Knowledgebase solution to support productivity improvements, improved traceability and enhanced data analytics.

Personnel Name, Project Party
Project Leader Prof. Suresh Palanisamy, SWINBURNE
Program Leader  Prof. Prem Prakash Jayaraman, SWINBURNE
Partner Investigator Peter Sutton, SUTTON
Project Personnel Prof. Dimitrios Georgakopoulos, SWINBURNE

Dr. Rizwan Abdul Rahman Rashid, SWINBURNE

Senior Research Associate (To be hired)

Research Associate (To be hired)