University of Leicester - Equipment Account

Lead Research Organisation: University of Leicester
Department Name: UNLISTED

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Publications

10 25 50
 
Description (FORGE) - Development of novel and cost-effective coatings for high-energy processing applications
Amount € 5,982,613 (EUR)
Funding ID 958457 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 11/2020 
End 04/2024
 
Description Digital Dynamic Knowledge Platform for Welding in Manufacturing Industries
Amount £2,500,000 (FKP)
Funding ID SEP-210507267 
Organisation European Commission H2020 
Sector Public
Country Belgium
Start 09/2018 
End 08/2023
 
Description Novel Brazing Filler Metals using High Entropy Alloys
Amount £1,074,952 (GBP)
Funding ID EP/S032169/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 01/2020 
End 01/2023
 
Description Superslab - NOVEL UNI-DIRECTIONAL CASTING TECHNOLOGY FOR MANUFACTURING SUPER EXTRA-THICK OFFSHORE STEEL PLATE
Amount £500,000 (GBP)
Funding ID 104015 
Organisation Innovate UK 
Sector Public
Country United Kingdom
Start 03/2018 
End 06/2020
 
Description TWI -UoL Materials Innovation Centre
Amount £360,000 (GEL)
Organisation TWI The Welding Institue 
Sector Private
Country United Kingdom
Start 09/2017 
End 09/2022
 
Description Materials Processing Institute 
Organisation Materials Processing Institute
Country United Kingdom 
Sector Private 
PI Contribution We used the equipment and proposed joint research work to Innovate UK with TWI and NISCO from China through Innovate International funding call.
Collaborator Contribution They provided in-kind support to the CDT, including chairing CDT advisory board, providing materials and consumables for our collaboration
Impact An Innovate UK project on: SUPERSLAB - NOVEL UNI-DIRECTIONAL CASTING TECHNOLOGY FOR MANUFACTURING SUPER EXTRA-THICK OFFSHORE STEEL PLATE APPLICATION REF: 99824-572236; THE INNOVATE UK REFERENCE: 104015
Start Year 2014
 
Description NISCO 
Organisation Nanjing Iron and Steel Co Ltd
Country China 
Sector Private 
PI Contribution Joint research in (1) new steels, (2) digital manufacturing of steel.
Collaborator Contribution providing funding, materials
Impact Joint Innovate UK project on : SUPERSLAB PhD students Joint publication
Start Year 2014
 
Description Rolls-Royce Plc 
Organisation Rolls Royce Group Plc
Country United Kingdom 
Sector Private 
PI Contribution Collaborate work on Nb-Si alloys and Single-crystal manufacturing technologies
Collaborator Contribution Top ups for PhD studentship, Materials , Staff time
Impact Joint publications + Graduated PhDs
Start Year 2006
 
Description TWI collaboration 
Organisation TWI The Welding Institue
Country United Kingdom 
Sector Private 
PI Contribution Research collaboration in the area of welding and digital manufacturing
Collaborator Contribution PhD studentship, sponsorship of my Research Chair of the Royal Academy of Engineering, Research collaboration in the area of welding and digital manufacturing,
Impact Joint research projects sponsored by EPSRC, Innovate UK and EC-Horizon, with total funding over £2M Joint publications Graduated PhDs
Start Year 2008
 
Title Automatic Recognition of Dendritic Solidification Structures: DenMap 
Description We developed a novel DenMap image processing and pattern recognition algorithm to identify dendritic cores. Systematic row scan with a specially selected template image over an image of interest is applied via a normalised cross-correlation algorithm. 
Type Of Technology Software 
Year Produced 2020 
Impact The DenMap algorithm locates the exact dendritic core position with a 98% accuracy for a batch of SEM images of typical as-cast CMSX-4® microstructures in under 90 s per image. Such accuracy is achieved due to a sequence of specially selected image pre-processing methods. Coupled with statistical analysis the model has the potential to gather large quantities of structural data accurately and rapidly, allowing for optimisation and quality control of industrial processes to improve mechanical and creep performance of materials.