Courtier Alexander F, McDonnell Michael, Praeger Matt, Grant-Jacob James A, Codemard Christophe, Harrison Paul, Mills Ben, Zervas Michalis
Opt Express. 2021 Oct 25;29(22):36487-36502. doi: 10.1364/OE.432741.
Laser cutting is a materials processing technique used throughout academia and industry. However, defects such as striations can be formed while cutting, which can negatively affect the final quality of the cut. As the light-matter interactions that occur during laser machining are highly non-linear and difficult to model mathematically, there is interest in developing novel simulation methods for studying these interactions. Deep learning enables a data-driven approach to the modelling of complex systems. Here, we show that deep learning can be used to determine the scanning speed used for laser cutting, directly from microscope images of the cut surface. Furthermore, we demonstrate that a trained neural network can generate realistic predictions of the visual appearance of the laser cut surface, and hence can be used as a predictive visualisation tool.
激光切割是一种在学术界和工业界广泛应用的材料加工技术。然而，在切割过程中可能会形成诸如条纹等缺陷，这会对切割的最终质量产生负面影响。由于激光加工过程中发生的光与物质相互作用高度非线性且难以进行数学建模，因此人们对开发用于研究这些相互作用的新型模拟方法很感兴趣。深度学习为复杂系统的建模提供了一种数据驱动的方法。在此，我们表明深度学习可用于直接从切割表面的显微镜图像确定激光切割所使用的扫描速度。此外，我们证明经过训练的神经网络可以对激光切割表面的视觉外观生成逼真的预测，因此可作为一种预测性可视化工具。
