Smart Railway System

Digital Twin-driven lifecycle smart product design and manufacturing for railway industry (British Council Project)

This is a five-year project funded by British Council, led by Professor Shengfeng Qin.

The Smart Design Lab is leading an UK-China-BRI Countries Educational Partnership initiative on “Digital Twin-driven lifecycle smart product design and manufacturing for railway industry” (2020-2025). The collaborative partners include Newcastle University, University of Huddersfield, University of Zagreb of Croatia, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand, University of Electronic Science and Technology, and Southwest Jiaotong University.


The key investigators are:

Dr Sheng-feng Qin is a Professor of Design at Northumbria, with expertise in ICT technology-enabled lifecycle Smart product and manufacturing system design. He has excellent experience in international collaboration and joint project management.


Dr Gui-Yun Tian is a Professor of Sensor Technologies at Newcastle, with expertise in Sensor technologies, smart systems, on-line monitoring and quality control, and multi-national collaborative research projects. Professor Tian is also PI of the UK-China international collaborative project funded by NSFC (2020.01-2024.12) and director of international collaborative centre of Sichuan University, USTC and Newcastle University in the area of NDT&E.


Dr Simon Iwnicki is a Professor of Railway Engineering at Huddersfield, a Fellow of Royal Academy of Engineering, with expertise in wheel-rail contact, computer modelling of railway vehicle suspensions and the design of railway vehicles and track.


Dr Guofu Ding is a Professor of Digital Design and Manufacturing at SWJTU, with expertise in manufacturing system simulation and scheduling, machine tools and advanced CNC machining, digital twin and smart manufacturing.


Professor Weihua Zhang is the Director of the Research Center of Super High-Speed Maglev Transport in Low-Pressure Tubes and the former Director of the State Key Laboratory of Traction Power (TPL) at SWJTU. His research interests include lifecycle rail vehicle design theory and vehicle system dynamics and control.


Dr Bin Gao is a Professor of Automation Engineering at UESTC, with expertise in statistical signal processing, data analysis, machine learning, electromagnetic and thermal sensing and Non-Destructive Evaluation (NDE).


Professor Mark Robinson is Director of NewRail at Newcastle. His main research interests include railway technology such as lightweight material for rail vehicles and crashworthy composite structures.


Dr Darko Vasić is an associate professor at the University of Zagreb. His research interests are in the field of electronic instrumentation, networked sensors, signal detection and inverse problems.


Dr Ruslee Sutthaweekul is an assistant professor at KMUTNB. His main research interest are smart wireless sensor network for real-time monitoring. He also has had working experience in Bombardier Transportation (Railway Signalling).


A digital twin is a dynamic digital representation of a physical system, which is continually updated with the physical system’s performance, maintenance, and health status data through its life cycle. It allows manufacturers or users to have a complete digital footprint of the product they concerned and informed decision-making through-life.


Smart High-Speed Train’s lifecycle development


Under the Industry 4.0 revolution, one common goal is smart manufacturing, realising the data-driven product design and manufacturing paradigm. Nowadays, the success of product design increasingly depends on the manufacturer’s capability to handle data and interact with them. The figure below shows a smart high-speed train through its lifecycle including design, production, distribution, service, maintenance, upgrade and recycle with a digital twin support to effectively interact with humans/users, physical environment and cyberspace environment. The enabling technologies include sensors, monitoring, IoT, and big data analytics and digital twin (DT).

While the research in digital twin-driven smart product design and manufacturing is still in its infant stage and requires multi-disciplinary and international research collaborations. Thus, establishing a joint digital-twin driven manufacturing research centre will (1) create an enabling environment to support related staff and student exchange in partner institutions, and (2) enhance the students’ employability by (3) fostering their entrepreneurialism and creativity.