Program Structure

The Digital Manufacturing Hub operates through four interconnected programs, delivering industry co-created digital manufacturing solutions. These solutions enable digital representations of complex machines, products, and people, addressing roadblocks associated with scarce, constrained, and interdependent manufacturing data. By overcoming these limitations, the Hub enhances the adoption of AI-driven solutions and reduces platform inefficiencies. The programs work synergistically to provide global solutions beyond specific production lines, fostering innovation and scalability across industries. This integrated approach supports Australia’s sovereign manufacturing goals by optimising processes, enabling predictive maintenance, improving productivity, and contributing to sustainability and efficiency through digital transformation.

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.

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:

  • Develop AI/ML models for automated diagnostics and predictive maintenance using data from various sources.

  • Design ML techniques for monitoring product quality and consistency.

  • Create data-driven optimisation solutions using reinforcement learning to enhance production planning and task scheduling.

Outcomes:

  • This program will develop advanced AI and ML models that will automate diagnostics, predict faults, and improve product quality, boosting overall factory efficiency.

Program Leader: Prof. Helen Huang

Open Platform for Digital Manufacturing

This program aims to create an open platform supporting the development of Digital Twins and AI/ML-based solutions. The platform will include tools for Digital Twin composition, AI/ML management, and seamless computing across different environments.

Aims:

  • Leverage the Knowledge Graph Management Framework (KGMF) to integrate AI/ML models.

  • Develop a programming framework for composing Digital Twins and AI/ML models.

  • Extend work on creating cost-efficient DM solutions from Digital Twin models.

  • Support standardised protocols and semantic data structures in the framework, reducing integration costs and increasing vendor interoperability.

Outcomes:

  • The program will deliver an innovative framework for optimised production planning and seamless integration of Digital Twins and AI/ML models, enhancing vendor interoperability.

Program Leader: Prof. Albert Zomaya

Industry Co-Created Digital Manufacturing Solutions

This program will combine results from all initiatives to co-develop new digital manufacturing solutions with Partners. It will emphasise co-design and co-development, addressing skills gaps by training the workforce and ensuring skilled resources are effectively utilised to help Partners achieve their manufacturing goals.

Aims:
  • Develop solutions for real-time production data access, integrating information for KPIs, and enhancing decision-making through Digital Twins and AI/ML models.

  • Enhance sustainability by optimising supply chains, reducing energy consumption, and improving recycling practices.

Outcomes:

  • This program will deliver digital manufacturing solutions that improve efficiency, product quality, preventive maintenance, and sustainability.

Program Leader: Prof. Zahir Tari

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