High-Precision Laser-Based Manufacturing

All PhD Projects:

• Entry Requirements. A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). 
• Closing date: Applications are accepted throughout the year and several start dates throughout the year are possible. Applications for the typical Sept./Oct. 2023 start should be received no later than 31 August 2023. 
• Funding: For UK students, Tuition Fees and a stipend of £20,000 tax-free per annum for up to 3.5 years. For international students, the ORC offers a small number of scholarships to outstanding applicants and students with funding are also encouraged to apply.
• Apply online here. 

Deep Learning for Process Control and Predictive Capability for Laser Machining

Supervisory team: Dr Ben Mills, James Grant-Jacob

Advances in lasers now allow the laser-based processing of almost any material. Innovation in this field is now therefore becoming heavily focussed on making existing processing techniques more precise and efficient.

Neural networks are a computing paradigm inspired by the biological neurons in the human brain. They offer the capability for learning directly from experimental data, and hence can be used to find solutions even when the problem is not understood by a human. Neural networks therefore offer a remarkable solution to the optimisation and control of laser machining, which itself is far from understood.

The team is combining state-of-the-art neural networks with high-precision femtosecond laser machining, with the objective of achieving repeatable and high-speed fabrication at resolutions well-below the diffraction limit.

Your PhD will be focussed on the following applications: 1) convolutional neural networks for real-time control of laser machining, and 2) generative adversarial networks for simulating and predicting laser machining. Neural networks require large amounts of experimental data for training, and hence this PhD will therefore involve a mixture of experimental photonics and femtosecond laser machining, experimental automation, and programming and designing neural networks.