卷积神经网络
材料科学
灵活性(工程)
人工智能
过程(计算)
异常检测
融合
模式识别(心理学)
人工神经网络
比例(比率)
图层(电子)
计算机科学
纳米技术
物理
哲学
操作系统
统计
量子力学
语言学
数学
标识
DOI:10.1016/j.addma.2018.09.034
摘要
In-situ detection of processing defects is a critical challenge for Laser Powder Bed Fusion Additive Manufacturing. Many of these defects are related to interactions between the recoater blade, which spreads the powder, and the powder bed. This work leverages Deep Learning, specifically a Convolutional Neural Network (CNN), for autonomous detection and classification of many of these spreading anomalies. Importantly, the input layer of the CNN is modified to enable the algorithm to learn both the appearance of the powder bed anomalies as well as key contextual information at multiple size scales. These modifications to the CNN architecture are shown to improve the flexibility and overall classification accuracy of the algorithm while mitigating many human biases. A case study is used to demonstrate the utility of the presented methodology and the overall performance is shown to be superior to that of methodologies previously reported by the authors.
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