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On-machine surface defect detection using light scattering and deep learning.
Journal of the Optical Society of America A ( IF 1.9 ) Pub Date : 2020-07-24 , DOI: 10.1364/josaa.394102
Mingyu Liu , Chi Fai Cheung , Nicola Senin , Shixiang Wang , Rong Su , Richard Leach

This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate, and robust defect detection. The system capability is validated on microstructured surfaces produced by ultraprecision diamond machining.

中文翻译:

使用光散射和深度学习进行机械表面缺陷检测。

本文提出了一种利用光散射和深度学习的机器表面缺陷检测系统。监督式深度学习模型用于从光散射图案中挖掘与缺陷有关的信息。在由严格的正向散射模型预测的散射模式的大型数据集上训练卷积神经网络。该模型对具有均质材料的任何表面形貌均有效,并且已通过与实验数据进行比较进行了验证。一旦训练了神经网络,它就可以快速,准确和强大地检测缺陷。该系统功能已在超精密金刚石加工产生的微结构表面上得到验证。
更新日期:2020-09-02
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