EPSRC logo

Details of Grant 

EPSRC Reference: EP/S001328/1
Title: A semantic infrastructure for advanced manufacturing
Principal Investigator: Qi, Dr Q
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Huazhong University of Sci and Tech National Physical Laboratory Reliance Precision Ltd
University of Sheffield
Department: Sch of Computing and Engineering
Organisation: University of Huddersfield
Scheme: EPSRC Fellowship - NHFP
Starts: 25 June 2018 Ends: 24 December 2021 Value (£): 482,941
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 4 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
We are stepping into a new era of digitalisation. In this new era machines will communicate and exchange large amounts of data to ensure they can work harmoniously and collaboratively with little human intervention. Current machines use symbolic language to represent the data, but they cannot directly interpret its meaning. As a result, information loss and incorrect interpretation can often happen during communication. To improve manufacturing intelligence, we need the manufacturing system to "understand" the data, which we refer to as "semantics" of the data. If the manufacturing system can be represented at a semantic level, the data will become knowledge to the machine and enable it to be ready for exchange, interrogation and reuse. There is current work taking place to upgrade manufacturing systems to a semantic level but this is still at an early and enabling stage. This fellowship aims to effect a step change in manufacturing intelligence, to support rigorous semantic exchanges between different manufacturing phases, and to allow formalisation and reuse of new/existing knowledge from advanced manufacturing.

The proposed research will build a novel semantic infrastructure for advanced manufacturing by supporting knowledge representation, interrogation, reasoning and exchange for smart design, manufacturing and measurement of advanced products. The focus will be on the development of a toolbox to formalise knowledge in/between design, manufacturing and measurement, especially for additive manufacturing (AM). The resulting semantic infrastructure will allow the machine to "interpret" the meaning of the data/information. To be more specific: how the design parameters (geometries, tolerances and materials) are related to each other; how the design parameters relate with the AM process/post process parameters (layer thickness, build orientation); and how the design and process parameters contribute to the measurement details (methods, calibration, etc.). The work will provide a new universal language for any data/information involved in a manufacturing value chain, and will permit a comprehensive infrastructure to digitalise the fast growing AM industry.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Summary
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.hud.ac.uk