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Details of Grant 

EPSRC Reference: EP/V028618/1
Title: RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories
Principal Investigator: Polydorides, Dr N
Other Investigators:
Gerogiorgis, Dr D
Researcher Co-Investigators:
Project Partners:
GlaxoSmithKline plc (GSK) nVIDIA Seagate Technology
Department: Sch of Engineering
Organisation: University of Edinburgh
Scheme: Standard Research
Starts: 01 August 2021 Ends: 31 July 2024 Value (£): 422,204
EPSRC Research Topic Classifications:
Artificial Intelligence Image & Vision Computing
Mobile Computing
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
EP/V02860X/1
Panel History:
Panel DatePanel NameOutcome
23 Mar 2021 EPSRC ICT Prioritisation Panel March 2021 Announced
Summary on Grant Application Form
Modern manufacturing involves highly controlled and automated processes meticulously designed to deliver products to certain needs within strict specifications and in a cost-efficient and sustainable way. To ensure performance in variable and often harsh conditions, sensors capture continuous data streams about the state of the process, e.g. equipment, and the product. The ability to analyse this data in real time, however, offers unique advantages that are currently out of reach. Learning to calibrate its operation from sensor data, monitor its health status and make accurate forecasts on product outcomes and maintenance requirements, are process attributes of future autonomous factories.

We have teamed up with Seagate, a major manufacturer of hard drives, to design, develop and implement such a technology in their factory, GSK, a leading pharmaceutical manufacturer, who have recognised the potential of real-time process analytics and NVIDIA, the global GPU provider. The goal is to establish a level of production robustness against major disruptions and market volatility that create uncertainties on workforce numbers and supply chain continuity.

This vision paves the way for responsive manufacturing systems and digitally controlled factories but to materialise technology that can seamlessly analyse sensor-obtained data and translate it to actionable information. Whilst companies capture large datasets, their ability to process them and react in real-time, is hindered by the algorithms' complexity and scale of the data. Indeed, if anything, the current pandemic has reinforced the need to enhance manufacturing capability to cope with sudden increases in demand, production repurposing, and possibly even unmanned, autonomous production.

A step change is needed in the processing capability and manufacturing systems where the data can be analysed in real time at the edge, i.e. on the factory floor, making it secure and thus ensuring more effective performance by being less reliant on external communications and high-performance processing resources. We propose that this be done in a methodological and secure way with minimal dependencies on external factors, thus prompting us to investigate ways of performing real-time analytics in a practical, cost-effective and sustainable manner.

RAPID proposes a two-pronged approach to reduce the computational dimensionality through novel 'data sketching' algorithms and optimisation using 'transprecision computing' on GPU technology to provide further acceleration. In detailed interactions with Seagate and GSK both based in the UK, we have identified manufacturing stages where real-time analytics can play a major part in transforming processes and outcomes.

In particular, the proposed technology will be applied to a 'diagnostic analytics' case study involving optical imaging data for a critical metrology stage in disk manufacture and two 'predictive analytics' examples for model learning to predict the health state of silicon wafers and for improved fault detection, feature extraction and monitoring of chemical products.

Data sketching dramatically reduces the complexity of computations by randomly sampling few, the most informative, data and model entries leading to small-scale computations that can be performed very quickly with a small compromise on precision. Sketching trades off precision and speed, and if done optimally a two-order of magnitude speedup is feasible, when sampling around 10% of the data.

To exploit this advantage further, the sketched computations are implemented using transprecision computing that challenges traditional computing to further accelerate computations when high precision is not required. In computing with noisy data and learned statistical models in factory environments, a controllable reduction in precision is prudent for performance improvement but also essential for noise robustness.

Key Findings
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