Lasers that Learn: AI-enabled intelligent materials processing

会学习的激光器：支持人工智能的智能材料加工

• 批准号: EP/T026197/1
• 负责人: Benjamin Mills
• 金额: $ 99.11万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT026197%2F1
• 关键词: Lasers; Learn; AI; enabled; intelligent

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.

激光器用于极其广泛的制造工艺。这部分是由于它们在脉冲长度、脉冲能量、波长和光束尺寸等参数方面具有显着的灵活性。然而，这种灵活性是有代价的，即必须花费大量时间专门为每个制造工艺或客户规格寻找最佳参数集。行业中的标准做法是对所有参数组合的激光加工数据进行机械收集，以找到最佳的参数组合。然而，这个过程既耗时又缺乏重点，可能需要数天或数周的时间，因此浪费了不必要的时间和金钱。即使确定了最佳参数，制造过程中激光功率或光束形状等微小变化也可能导致最终产品质量低于要求的标准，再次浪费时间和金钱。也存在由于制造过程的变化而无法提前知道规格的情况。因此，我们需要一系列方法来在制造前确定最佳参数，在制造过程中提供实时监控和纠错，以及实现过程控制（例如在任务完成时准确停止激光器，或改变近年来，机器学习研究领域取得了一些极其重大的发展，现在人们普遍认为它是几乎所有制造业发生根本性变革的催化剂。该提案的目标是开发将机器学习方法集成到英国激光制造业和 NHS 所需的技术和人力专业知识。因此，该提案旨在利用最先进的机器学习技术来解决激光制造和材料加工中的众所周知的问题，从而提高效率、可靠性和精度。该提案的结果将为英国激光制造业和 NHS 节省时间和金钱。该提案将涵盖神经网络在飞秒激光加工建模和优化中的应用，立即识别任何客户规格的基于激光的制造参数，自动补偿光纤激光器中的残余腔效应，实现有针对性地传输用于牛皮癣治疗的激光，通过多传感器数据实时增强激光焊接工艺。

项目成果

Modelling of fibre laser cutting via deep learning.

通过深度学习对光纤激光切割进行建模。

• DOI: http://dx.10.1364/oe.432741
• 发表时间: 2021
• 期刊: Optics express
• 影响因子: 3.8
• 作者: Courtier AF

Predictive visualization of fiber laser cutting topography via deep learning with image inpainting

通过深度学习和图像修复对光纤激光切割形貌进行预测可视化

• DOI: http://dx.10.2351/7.0000957
• 发表时间: 2023
• 期刊: Journal of Laser Applications
• 影响因子: 2.1
• 作者: Courtier A

Bi-frequency operation in a membrane external-cavity surface-emitting laser.

薄膜外腔表面发射激光器的双频操作。

• DOI: http://dx.10.1371/journal.pone.0289223
• 发表时间: 2023
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Daykin J

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

深度学习辅助聚焦离子束纳米加工。

• DOI: http://dx.10.1021/acs.nanolett.1c04604
• 发表时间: 2022
• 期刊: Nano letters
• 影响因子: 10.8
• 作者: Buchnev O

Lensless imaging of pollen grains at three-wavelengths using deep learning

使用深度学习在三波长下对花粉粒进行无透镜成像

• DOI: 10.1088/2515-7620/aba6d1
• 发表时间: 2020-07-16
• 期刊: Environmental Research Communications
• 影响因子: 2.9
• 作者: J. Grant;M. Praeger;M. Loxham;R. Eason;B. Mills
• 通讯作者: B. Mills