Mechanical and Manufacturing Engineering
Industry 4.0 reflects the ongoing fourth industrial revolution promotes digital production and transforms today's factories into smart ones to increase production system productivity, and product quality, shorten lead times, and reduced environmental footprints. Hence, industry 4.0 and its related technologies are becoming increasingly important around the globe and in Australia.
The key enabling technologies of Industry 4.0 are cyber-physical systems, the Internet of Things (IoT), Artificial Intelligence (AI), simulation, and digital twin [4]. Digital twin is defined as the digital replica of an object, mapping its characteristics, and aiming to support the design, planning, and control of products and systems.
The idea of a digital twin relies on having a seamless connection between a physical object and its digital twin to improve its performance. However, both academia and industry believe that this seamless integration has not been completely achieved yet and it is needed to reduce and handle the issues of not having complete lifecycle data and data transmission latency to achieve the full potential of digital twin.
On the other hand, the Circular economy concept (CE) revolves around the idea of enhancing resource efficiency through the product lifecycle with various strategies including the known R-strategies: remanufacturing, recycling, reuse, reducing material consumption, and so on.
In this research project, you have the opportunity to work on various projects as some are listed below and can be re-scoped:
- investigate the application of industry 4.0 technologies such as digital twin and AI to realize smart product life cycle development
- integration of AI and machine learning with Digital Twin in manufacturing systems for data-driven production management
- application of AI for supply chain management
- application of Industry 4.0 technologies to realize circular economy
School
Research Area
Industry 4.0 | Digital twin | Circular economy | Artificial intelligence | Machine learning | Smart production logistic systems | Product life cycle development | Sustainable development
- Research Environment
- Expected Outcomes
- Supervisory team
- Reference Material/Links
You have the opportunity to be part of a vibrant team of Industry 4.0 in the school of mechanical and manufacturing engineering.
The team includes Research associates, PhD students, master's by research, and thesis students which work in different aspects of the explained project with a close collaboration. the team has a vibrant enviornment in sharing the research results on regular meetings.
- Depending on the chosen project the expected outcome can include: a documented report as critical survey on the literature, a working model (e.g., an AI model), and or a conceptual framework.
- Depending on the quality of the work, a journal or conference publication can be an outcome of this project with the guidance offered by the supervisory team.
View Profile
- A target-driven framework for assessing the effectiveness of product-service-system development approaches at System of Systems level
- Experimentable Digital Twin for Virtual Validation of Manufacturing Systems
- Automated Disassembly of Lithium Batteries; Methods, Challenges, and a Roadmap
- A framework for analysing the environmental impact and support decision making in sustainable development context
- Digital twin and Value Stream Mapping of Warehousing in Era of Industry 4.0
- Challenges and Opportunities in Product Life Cycle Management in the Context of Industry 4.0
- An Investigation on Available Technologies and Approaches for Integration of Control Systems with the Digital twin in Cyber-Physical Production Systems
- A modelling framework to support integrated design of production systems at early design stages