Lasers that Learn: AI-enabled intelligent materials processing

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.

Impact Summary

The main beneficiaries of this research lie within the manufacturing and healthcare sectors, each of which represents a major opportunity for financial return (economic benefit), and healthcare provision (societal impact).

1. Economic benefit

Our industrial project partners, Oxford Lasers and SPI Lasers will have direct benefit from this work. Oxford Lasers will have the potential for faster turn-around times on customer orders, along with higher precision and reliability, and of course freeing up valuable technical staff time for more creative duties. This extended capabilities will therefore offer a direct financial benefit to the company. SPI Lasers, similarly, will have the potential for a more precise and reliable fibre laser system, with capabilities that far surpass current abilities. This will directly lead to the opening of new markets (such as high-speed manufacturing of bespoke security features on high-value objectives), and lead directly to company competitiveness and financial benefit. For both companies, financial gains from increased (global) market share could lead to employment of additional UK staff. Whilst integrating the proposed machined learning and laser machining techniques into UK industry is an important objective of this proposal, a spinout from Southampton is also a realistic possibility, if financial backing is secured to take this forward.

2. Healthcare and societal benefit

With the continuously increasing strains on the over-burdened public health systems of developed nations such as the UK, and likewise for developing countries, the role of healthcare, and in particular the diagnosis and treatment of skin conditions, such as skin cancer and psoriasis, is an increasingly important factor. The proposed technique, which combines state-of-the-art machine learning, with directed laser energy transfer, for the identification and targeted irradiation of affected areas of skin, offers a significant potential for cost savings, and improved patient care. If the initial proof-of-principle experiments proposed here are successful, then there really is potential for a genuinely radical breakthrough in the treatment of psoriasis.

3. Training and the next generation of researchers

This programme will be of immense value to the next generation of research scientists and, in particular, those researchers who can span the 'single discipline' problem so prevalent in academia. Scientists involved in this project will become experts with both lasers and machine learning. When combined with healthcare, their skillset will be extremely valuable for both UK academia and laser-based manufacturing companies in the UK.