期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 9, 页码 5769-5779出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2956078
关键词
Feature extraction; Cameras; Fiber lasers; Laser modes; Convolutional neural nets; Additive manufacturing; condition monitoring; convolutional neural networks (CNNs); melt-pool-affected zone; powder-bed fusion (PBF)
类别
资金
- National Natural Science Foundation of China [51875379]
- National Additive Manufacturing Innovation Cluster, Singapore, under a PEP Project
- IDI Laser Services Pte Ltd., Singapore
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.
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