(1) Virtual sensing, prognostics & Virtual Factory Simulations
Big data and advanced data analytics are at the heart of Industry 4.0. In i-AMS we are considering challenges such as providing real-time assessment of machine performance, tool health, and energy consumption, achieving fleet level information sharing and integration to deliver enhanced process monitoring and real-time fault detection – and ultimately zero-defect manufacturing, and how to integrate data driven and first principles modelling of complex manufacturing systems to deliver comprehensive virtual factory models.
(2) Flexible automation and cobotics
Flexible automation systems and autonomous robotics have seen major advances in the last decade but many challenges remain with regard to achieving cost-effective and safe solutions in unstructured environments, particularly where there is direct interaction with human operators. i-AMS research in this area is focused on: (1) developing adaptive multi-robot/machine control, self-reconfiguration and self-calibration systems; (2) capturing, modelling, predicting and anticipating human-robot interactions; and (3) designing distributed control and path planning algorithms to deliver flexible and safe multi-robot andhuman-robot collaborative working environments.
(3) Autonomous and Intelligent Decision making
Under this research theme i-AMS is looking at how to design intelligent supervisory control and autonomous decision support systems which can respond in real-time to dynamically changing operating requirements, and take account of the enhanced situational awareness provided by virtual sensing, prognostic models, and virtual factory model predictions in order to achieve optimal decision making with regard to tasks such as maintenance scheduling, quality control and production planning. We are also interested in developing methodologies that can capitalise on production flexibility to reduce energy consumption and/or deliver demand response services to the power system.
We are interested in developing new partnerships with industrial organisations so that research is focused on the most relevant and challenging industrial problems. Partnerships are sought from all sizes of organisations engaged in manufacturing with a full range of partnership platforms and leveraging opportunities available. We hope to develop and demonstrate research solutions and we are able to work across the full TRL spectrum. Academic partners are also sought to broaden and complement our internal themes of research strength.
Kang Li, 01 December 2016 - 30 November 2019, EPSRC project on Optimising Energy Management in Industry - 'OPTEMIN'