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首页> 外文期刊>IEEE transactions on industrial informatics >Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks
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Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks

机译：用混合动力卷积神经网络的机器视觉进行粉床融合过程监控

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摘要

In this article, a method of hybrid convolutional neural networks (CNNs) is proposed for powder-bed fusion (PBF) process monitoring. The proposed method can learn both the spatial and temporal representative features from the raw images automatically based on the advantages of the CNN architecture. The results demonstrate the superior performance of the proposed method compared with the traditional methods with handcrafted features. The overall detection accuracy of four process conditions, e.g., overheating, normal, irregularity, and balling, can be up to 0.997. In addition, it is found that the temporal information for PBF process monitoring by the vision detection of the process zone (including melt pool, plume, and spatters) is significant. As the proposed method can save image processing steps, it simplifies the procedure on feature extraction. This makes it more suitable for online monitoring applications.
机译：在本文中，提出了一种用于粉床融合（PBF）过程监测的混合卷积神经网络（CNNS）的方法。该方法可以基于CNN架构的优点来自动地从原始图像中学习空间和时间代表特征。结果表明，与手工特征的传统方法相比，该方法的卓越性能。四个工艺条件的总检测精度，例如过热，正常，不规则和球形，可高达0.997。另外，发现通过处理区（包括熔体池，羽流和飞溅物）的视觉检测的PBF过程监测的时间信息是显着的。由于所提出的方法可以节省图像处理步骤，因此它简化了特征提取的过程。这使得它更适合在线监控应用程序。

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