Machine Learning for Fibre Laser Materials Processing

激光材料加工是一个价值100亿英镑的全球产业，每年持续快速增长约10%，其中大部分是由光纤激光器的发展推动的。在未来十年中，对效率和精度的不断提高将使机器学习方法与光纤激光技术完全集成。然而，目前有相当大的挑战阻碍了进展，特别是收集和处理适当的训练数据，开发适当的神经网络，以及提高鲁棒性以确保工业应用的可靠性。该博士项目将涉及与SPI激光器的技术人员密切合作，以便将机器学习方法与用于材料加工的新型光纤激光器相结合。这些纤维本身的设计目的是将加工过程中材料产生的光收集到纤维包层中，然后将其传输到一个内联光谱仪，该光谱仪记录下时间光谱数据。这些数据包含大量的结构，这些结构看起来像“噪音”，因此人眼无法识别。该项目的假设是，机器学习可以用来解释这些时间光谱数据，以便实时监控和控制用于材料加工的光纤激光器，从而推动精度和效率方面的创新。此外，将应用强化学习的最新发展，以探索计算机化发现新制造方法的潜力。这个项目将涉及实验、理论和机器学习方法的结合。

Laser materials processing is a £10 billion global industry that continues to experience rapid growth of approximately 10% per year, with much of this driven by developments in fibre lasers. In the coming decade, the continual drive for improvements in efficiency and precision will see machine learning approaches fully integrated with fibre laser technology.However, there are considerable challenges that are currently holding back progress, in particular the collection and processing of appropriate training data, the development of appropriate neural networks, and improving the robustness to ensure reliability sufficient for industrial applications.This PhD project will involve working closely with technical staff at SPI Lasers in order to integrate machine learning approaches with novel fibre lasers used for materials processing. The fibres themselves are designed to collect the light that is produced from the material during machining into the fibre cladding, and then to an inline spectrometer that records the temporal spectral data.This data contains a significant amount of structure that appears as "noise", and hence unidentifiable by human eye. The project hypothesis is that machine learning can be used to interpret this temporal spectral data, in order to enable real-time monitoring and control of fibre lasers for materials processing, hence driving innovations in precision and efficiency. In addition, state-of-the-art developments in reinforcement learning will be applied in order to explore the potential for computerised discovery of novel manufacturing approaches. This project will involve the combination of experimental, theoretical, and machine learning approaches.