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Details of Grant 

EPSRC Reference: EP/S001387/1
Title: Transfer Learning for Robust, Resilient and Transferable Cyber Manufacturing Systems
Principal Investigator: Giannetti, Dr C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Crown Packaging Plc Tata Steel Vortex IoT
Department: College of Engineering
Organisation: Swansea University
Scheme: EPSRC Fellowship - NHFP
Starts: 29 June 2018 Ends: 31 December 2021 Value (£): 477,185
EPSRC Research Topic Classifications:
Artificial Intelligence Manufact. Enterprise Ops& Mgmt
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 2 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
Digital Manufacturing relies on pervasive and ubiquitous use of Information and Communication Technology (ICT), sensors, intelligent robots to deliver the next generation of intelligent, co-operating and interconnected manufacturing systems. The research is aimed to improve techniques that can be used to develop digitalised manufacturing systems to reduce existing inefficiencies in production processes that impact on production costs, unplanned downtime, quality and yields. This is not only detrimental to manufacturing businesses but has a negative impact on the UK Economy. The current productivity levels of UK manufacturers and suppliers is lagging behind global competitors and prevents the UK from successfully competing with other countries in the manufacturing domain - which is vital to keep businesses and jobs in the UK rather than relocate production abroad.

The UK Government wants to increase the strength of the UK Manufacturing Sector. A key means of doing this is the widespread adoption of industrial digital technologies (IDT). Cyber Manufacturing Systems (CMS) are the building blocks of digitalised manufacturing and generate vast amount of data that can be used for real time decision making to achieve optimised performance through predictive and prescriptive analytics. The latter are techniques that use, combine and analyse available data to develop computational models that can predict future outcomes and determine the best course of action.The research, under the fellowship, solves some of the existing problems in this area (CMS), developing new techniques and resources for predictive and prescriptive analytics with the potential to increase efficiency, accuracy and productivity of manufacturing processes. Businesses are therefore more likely to adopt IDTs and improve profitability and sustainability and provide high-quality jobs in a thriving part of the economy.

This project will study novel and robust data analytics methods that will enable to build predictive models that take into account uncertainty, complexity and dynamic behaviour of productions systems. The project will involve:

Objective 1 - develop algorithms that can reuse previously acquired data/knowledge to build more accurate predictive models that work well in the presence of noise (i.e. 'robust'), are able to adapt to changes over time (i.e. 'resilient') and can be scaled up across multiple factories (i.e. 'transferable').

Objective 2 - develop and test novel non-parametric methods for estimation of uncertainty and risks associated to a decision to enable real time mission and safety critical decision making (both automated and human driven) based on predictions.

Objective 3 - iteratively develop, deploy and test predictive and prescriptive models in real and simulated industrial scenarios to obtain acceptable level of performance, usability and robustness.

There will be significant involvement from industrial collaborators who will provide labelled and aggregated datasets for testing the proposed methods through computer simulations and enable feasibility studies to be conducted in factory environments.

The outcomes of the research, as mentioned above, are ultimately to improve the quality of products, achieving less wastage and unnecessary costs. Through increased adoption of IDTs, the production of goods will, importantly, be more efficient, reliable and profitable. This will support the regeneration of the Manufacturing Sector and boost the global competitiveness of the UK.

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.swan.ac.uk