Intelligent Cyber-Physical-Systems

Current production environments face increasing challenges to dynamically react to constantly changing conditions. Cyber-Physical-Systems with “plug-and-produce” capabilities are perceived to be critical to deliver system agility. The vision behind this laboratory is to facilitate the seamless deployment and integration of heterogeneous intelligent solutions in industrial environments. The focus of our research is on enabling agile systems without compromising on their performance. This is viewed as critical for the successful dissemination of this paradigm.

Our research focuses on system design, system re-configuration, system ramp-up and system operation. We are working towards providing models to ensure seamless flow of information across these different stages. This in parallel with the necessary mechanisms to use this information and make systems more intelligent. We are working to develop new methods to make systems more agile without compromising on their performance. The vision is to deliver self-adapting systems, composed of heterogeneous “plug-and-produce” devices, which deliver high performance automation systems.

This work is seen to provide the foundations for the advent of Industry 4.0. In fact the success of the new digital revolution will require the existence of cyber-physical-systems, which are able to provide information but also adapt to an changing environment.

Current PhD Students

Abdulaziz Alotaibi
"Applying Multi-Agent System to Reduce Energy Consumption in Flexible Manufacturing Systems"

Paul Danny
"Reliability-Based Methodology to Support Reuse or Buy Decisions for Automation Components"

Spartak Ljasenko
“Self-Organisation of Mobile Robots in Large Structure Assembly Using Multi-Agent Systems”

In order to succeed on the competitive market of manufactured goods, manufacturers are required to maximise their asset utilisation and improve operational resilience. Currently large products in the aerospace industry are commonly moved using cranes. Such an approach is very complicated, expensive and requires highly qualified labour. The crane system can become a bottleneck and reduce machine utilisation if it is not fast enough. By using mobile robots, we reduce the need to move such work pieces between manufacturing equipment. Instead, we move the manufacturing equipment to the work pieces. As a result of that, the shop floor becomes dynamic and more responsive to various changes and disturbances. 

Melanie Zimmer 
“Decision Support Framework for System Ramp-Up towards Improving Production Sustainability"

Nowadays, shorter and more flexible production cycles are vital to meet the increasing customised product demand. As any delays and downtimes in the production towards time-to-market means a substantial financial loss, manufacturers are keen to get the production system to full utilization as quickly as possible. The concept of plug-and-produce manufacturing systems facilitates an easy integration process through embedded intelligence in the devices. However, a human still needs to validate the functionality of the system and more importantly must ensure that the required quality and performance is delivered. This is done during the ramp-up phase, where the system is put together and tested first-time. System adaptations and a lack of standard procedures make the ramp-up process still largely dependent on the operator’s experience level. A major problem that currently occurs during ramp-up, is a loss of knowledge and information due to a lack of means to capture the human’s experience. Capturing this information can be used to facilitate future ramp-up cases as additional insights about change actions and their effect on the system could be revealed. Hence, this research proposes a decision-support framework for plug-and-produce assembly systems that will help to reduce the ramp-up effort and ultimately shorten ramp-up time.

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