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Details of Grant 

EPSRC Reference: EP/R024162/1
Title: Manufacturing Fellowship Extension in: Controlling geometrical variability of products in the digital and smart manufacturing era
Principal Investigator: Scott, Professor PJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
High Value Manufacturing (HVM) Catapult National Physical Laboratory OCF
Royal National Orthopaedic Hosp NHS Tr Taylor Hobson Ltd
Department: Sch of Computing and Engineering
Organisation: University of Huddersfield
Scheme: EPSRC Fellowship
Starts: 09 December 2018 Ends: 08 December 2022 Value (£): 697,733
EPSRC Research Topic Classifications:
Civil Engineering Materials Design & Testing Technology
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Feb 2018 Engineering Prioritisation Panel Meeting 7 and 8 February 2018 Announced
04 Jun 2018 Manufacturing Fellowship Interview Panel June 2018 Announced
Summary on Grant Application Form
This fellowship proposal is a three year extension of the current EPSRC manufacturing fellowship: Controlling Geometrical Variability of Products for Manufacturing (EP/I033424/1). The current fellowship is exploring the mathematical fundaments for the decomposition of geometry (i.e. size, shape and texture) and creating ground-breaking technology to control geometrical variability in manufactured products. The approach links fundamental geometrical mathematics direct to key component's design, manufacturing and verification from different industrial sectors (i.e. aerospace, optics, healthcare and catapult centres). In this case, the different types of geometrical decompositions specified geometrical surface requirements (spectrum, morphological and segmentation decompositions).

The fellowship extension proposal will take the research results from the current fellowship and use them as a stepping stone for more advanced fundamental research in new areas within the manufacturing value chain. The research work is broken down to four aspects:

1. Different aspects of the manufacturing process leave different multi-scalar geometrical features, in a surface, at different scales (i.e. size, shape and texture). By decomposing these different signature features, information regarding different manufacturing aspects can be gained enabling characterisation and control of different aspects of the manufacturing process.

2. Sensor network provide information in the form of an irregular image, like a cubist painting, with different views, and times, of the environment all combined together. Decomposition of this information will provide access to features, and their relationships enabling an agile dynamic predictive model to be self-aware of its environment enabling mathematical foundations for bio-inspired feedback control loops from sensor networks to be developed.

3. Smart autonomous manufacturing will require access to the huge amassed manufacturing knowledge-base (National and International Standards, Materials data-sheets, etc.). Create the foundations of decomposition of information structures for the automatic creation of smart information systems that are machine readable and to apply this result to develop the full rigorous mathematical foundations for the manufacturing value chain.

4. Using the EPSRC Future HUB in Advanced Metrology (EP/P006930/1) as leverage, disseminate the results from the above to solve real industrial problems to demonstrate the advantage of using fundamental decomposition theory, as developed in the previous manufacturing fellowship and this extension, over traditional approaches.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.hud.ac.uk