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Details of Grant 

EPSRC Reference: EP/T026197/1
Title: Lasers that Learn: AI-enabled intelligent materials processing
Principal Investigator: Mills, Dr B
Other Investigators:
Eason, Professor RW Suder, Dr W Niranjan, Professor M
Researcher Co-Investigators:
Project Partners:
Oxford Lasers Ltd SPI Lasers UK Ltd
Department: Optoelectronics Research Centre (ORC)
Organisation: University of Southampton
Scheme: Standard Research
Starts: 01 July 2020 Ends: 30 June 2023 Value (£): 777,859
EPSRC Research Topic Classifications:
Manufact. Enterprise Ops& Mgmt Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Apr 2020 Engineering Prioritisation Panel Meeting 7 and 8 April 2020 Announced
Summary on Grant Application Form
Lasers are used for an extremely wide range of manufacturing processes. This is due, in part, to their significant flexibility with respect to parameters such as pulse length, pulse energy, wavelength, and beam size. However, this flexibility comes at a price, namely the significant amount of time that must be dedicated to finding the optimal set of parameters, for each and every manufacturing process or customer specification. The standard practice in industry is the mechanical collection of laser machining data for all parameter combinations, in order to find the optimal combination of parameters. However, this process is both time-consuming and unfocussed, and it can take days or weeks, hence costing unnecessary time and money. Even when the optimal parameters have been determined, small changes, for example in laser power or beam shape, during manufacturing, can result in a final product quality that is below the required standard, once again costing time and money. There will also be instances where the specification is not known in advance due to variability in the manufacturing process. What is needed, therefore, are a series of methodologies for identifying optimal parameters before manufacturing, for providing real-time monitoring and error correction during manufacturing, and for enabling process-control (for example stopping the laser exactly at task completion, or varying the laser power for the final finishing steps).

The research field of machine learning has seen some extremely significant developments in recent years, and it is now widely understood to be a catalyst for a fundamental change across almost all manufacturing industries. The objective of this proposal is to develop the technological and human expertise required for the integration of machine learning approaches into the UK laser-based manufacturing industry and the NHS.

This proposal therefore seeks to leverage state-of-the-art machine learning techniques for solving well-known problems in laser-based manufacturing and materials processing, resulting in improvements in efficiency, reliability, and precision. The results of this proposal will lead to time and money savings for both the UK laser-based manufacturing industry and the NHS. This proposal will cover the application of neural networks for modelling and optimising of femtosecond laser machining, instantly identifying laser-based manufacturing parameters for any customer specification, automatically compensating for residual cavity effects in fibre lasers, enabling targeted delivery of laser light for psoriasis treatment, and laser welding process enhancement in real-time via multi-sensor data.

Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.soton.ac.uk