Predictive capabilities for laser machining via a neural network
Predictive capabilities for laser machining via a neural network
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通过神经网络实现激光加工的预测能力
DOI：
10.1364/oe.26.017245
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发表时间：
2018-06-25
期刊：
影响因子：
3.8
通讯作者：
Eason, Robert W.
中科院分区：
文献类型：
--
作者：
Mills, Ben;Heath, Daniel J.;Eason, Robert W.
通过分析方法对激光加工过程中光与物质之间的相互作用进行建模尤其具有挑战性。在这里，我们展示了统计方法的应用，该方法直接根据激光加工样品的实验图像构建加工过程的模型，从而无需了解底层的物理过程。具体来说，我们使用神经网络将激光空间强度分布转换为激光加工目标的等效扫描电子显微镜图像。这种方法能够对任何激光空间强度分布的激光加工结果进行模拟可视化，从而展示了激光加工的预测能力。经过训练的神经网络被发现具有与衍射定律一致的编码功能，因此显示了这种方法直接从实验数据发现物理定律的潜力。由光学协会根据知识共享署名 4.0 许可条款发布。
The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License.