Funders: UK India Education and Research Initiative (UKIERI), UK and the Department of Science and Technology (DST), India.
Project Partners: London Digital Twin Research Centre (Middlesex University), and Indian Institute of Information Technology (IIIT) Sri City
Industrial Partners: SPL, Festo Didatic, Siemens
Project Lead: Prof. Huan X. Nguyen (UK Lead) and Dr. Hrishikesh V Raman (Indian Lead)
Project Team: Dr Ramona Trestian (Co-I), Prof. Mehmet Karamanoglu, and Prof. Balbir Barn
Goals and Objectives
- Produce state of the art digital twin model to work with Siemens/Festo Industry 4.0 Cyber physical facility for thorough evaluation, debugging and optimization of applications
- Use proposed digital twin to suggest and counteract delay inducing elements in safety, preventive maintenance and regulatory systems in Industry 4.0
- Digitizing manufacturing processes in industry 4.0 for optimal efficiency, including detecting and solving physical issues faster, predicting outcomes to a much higher degree of accuracy, scheduling activities in the most efficient and cost-conscious way
Brief Description of the Project
The rapid advancements in manufacturing technologies and industry transformation in 4th Industrial Revolution requires more sophisticated tools to enable high productivity, lower running costs, product quality improvement, minimized maintenance and shutdown. In Industry 4.0, fully automated smart industrial infrastructure relies on low latency feedback networks, high efficiency distributed control systems, fool-proof emergency and safety systems, energy efficient and self-sustaining processes and supportive digital technologies.
The existing industrial systems are highly complex and require several processes to operate simultaneously to achieve the desired objectives. To ensure efficient operations within industrial processes, human intelligence, intervention and feedback is widely used. To enable truly self-reliant and autonomous industries, the developments are on the way. One major hurdle in achieving fully autonomous industries is lack of software-based counterparts to support vigorous testing.
This project targets implementation of digital counterpart (a Digital Twin model) of Industry 4.0 to replicate its functionalities, data, communications, feedback, emergency and safety aspects. The proposed digital twin for industry 4.0 will not only offer a digitized replication of functionalities but will also enable development towards self-correcting smart process control facility. The digital twin will also facilitate debugging, testing and reforming processes. It is expected that the developments in the project will provide solutions for some of the most critical aspects of the present-day industries. The developments in this project will be cross-validated and vigorously tested in state of the art Siemens/Festo cyber factory facility installed at Middlesex University (MDX), which acts as the physical twin in the project.
The key research question that will be addressed is how intelligently digital twin can predict the chain of events triggered as a consequence of certain variations in some processes, within the hundred plus manufacturing industries in and around Sricity/Andhra-Pradesh.
Scientific & Technical Details
Industry 4.0 aims to offer next generation of industrial automation which emphasises on interconnected and decentralized intelligent systems, capable of self-sustaining. However, the complexity of smart industrial processes is unfathomable, given the interjection of countless smart processes, which need to work seamlessly perfect to achieve the desired outcomes. For such interconnected systems, the impact of changes in one process is hard to predict.
The use of digital twin encompasses the functionality and interconnection of different processes within the industry and bears the potential to replicate interlinked complex processes in digital domain. This can provide a framework to investigate experimental setup in the simulations with more confidence. It also offers a platform to evaluate system limitations and impact of malfunction in one process on the others. The digital model of industry 4.0 will provide limitless opportunities to observe the impact of failure in one small block and how it will impact the entire setup. It will also enable the development of backup solutions to deal with the arising situation. Notably, the IoT and Analytics are required for real-time data collection, analysis and decision making which are crucial for the proper operation of the Cyber Physical System (CPS). The interaction of the IoT-based smart objects within the CPS will generate large amounts of data needs to be processed for extracting valuable and timely information.
This project targets implementation of digital counterpart of industry 4.0 to replicate its functionalities, data, communications, feedback, emergency and safety aspects. The project will develop digital twin to mirror the smart cyber factory facility at Middlesex University supplied by Festo/Siemens which comprises a comprehensive six-station table top unit (two production cells of three stations), as well as two bridging stations that enable an Automated Guided Vehicle (AGV) to deliver the logistics/transport between the cells. The validation for the Digital Twin will focus on the following aspects: i) energy monitoring; ii) tracking components and goods by means of tags which transmit a radio signal using Radio Frequency identification (RFID); iii) digital maintenance; iv) augmented reality of a real-world manufacturing process; v) direct communication among the objects using near field communication (i.e., objects equipped with a chip to exchange information directly); and vi) manufacturing execution system. The primary objectives are to develop a self-correcting smart process control facility where the digital twin can extend the debugging, testing and reforming processes before physical implementation. It is expected that the developments in the project will provide solutions for some of the most critical aspects of the present-day industries. The project will also aim to minimize the sensing, communications and processing delays for such applications. It will also target regulatory control applications within industry 4.0 to improve the overall efficiency of the plant/factory. Since the effective operation of regulatory control in industries require feedback response within a fixed time window for optimal process efficiency, therefore, the digital twin will serve as a digital alternate to predict any expected variations in regulatory control systems’ delays.