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.

激光器用于非常广泛的制造工艺。这部分是由于它们相对于诸如脉冲长度、脉冲能量、波长和光束尺寸的参数的显著灵活性。然而，这种灵活性是有代价的，即必须花费大量的时间来为每个制造过程或客户规格找到最佳参数集。工业中的标准做法是机械收集所有参数组合的激光加工数据，以找到最佳参数组合。然而，这个过程既耗时又无重点，可能需要数天或数周，因此会花费不必要的时间和金钱。即使已经确定了最佳参数，在制造过程中，例如激光功率或光束形状的微小变化也可能导致最终产品质量低于所需标准，再次花费时间和金钱。由于生产工艺的可变性，也会出现质量标准事先未知的情况。因此，需要一系列方法，用于在制造之前识别最佳参数，用于在制造期间提供实时监控和误差校正，以及用于实现过程控制（例如在任务完成时精确地停止激光器，或者在最后的精加工步骤中改变激光器功率）。机器学习的研究领域近年来已经看到了一些非常重要的发展，现在人们普遍认为，它是几乎所有制造业发生根本性变化的催化剂。该提案的目标是开发将机器学习方法整合到英国激光制造业和NHS中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基于激光的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，即时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，实现针对银屑病治疗的激光定向输送，以及激光焊接通过多传感器数据实时增强工艺。

项目成果

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

深度学习辅助聚焦的离子束纳米化。

• DOI: 10.1021/acs.nanolett.1c04604
• 发表时间: 2022-04-13
• 期刊: Nano letters
• 影响因子: 10.8
• 作者: Buchnev O;Grant-Jacob JA;Eason RW;Zheludev NI;Mills B;MacDonald KF
• 通讯作者: MacDonald KF

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

• DOI: 10.2351/7.0000957
• 发表时间: 2023-06
• 期刊: Journal of Laser Applications
• 影响因子: 2.1
• 作者: Alex Courtier;M. Praeger;J. Grant-Jacob;Christophe Codemard;Paul Harrison;M. Zervas;B. Mills

Studying the Topography of Laser Cut Aluminium Using Latent Space Produced by Deep Learning

利用深度学习产生的潜在空间研究激光切割铝的形貌

• DOI: 10.5220/0011631400003408
• 发表时间: 2023
• 作者: Courtier A

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

• DOI: 10.1088/2515-7620/aba6d1
• 发表时间: 2020-07-01
• 期刊: ENVIRONMENTAL RESEARCH COMMUNICATIONS
• 影响因子: 2.9
• 作者: Grant-Jacob, James A.;Praeger, Matthew;Mills, Ben
• 通讯作者: Mills, Ben

Predicting the Surface Topography of Stainless Steel Cut by Fibre Laser via Deep Learning

通过深度学习预测光纤激光切割不锈钢的表面形貌

• 发表时间: 2022
• 期刊: 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
• 作者: Courtier A.F.