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Deep Learning for

机译：深度学习

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摘要

Additive manufacturing (AM) is considered as a revolution in manufacturing. However, the high expectations face technical difficulties that prevent further penetration into wider industries. The main reason is the lack of process reproducibility and the absence of a reliable and cost-effective process monitoring. This paper is a supplement to existing studies in this field and proposes a unique combination of highly sensitive acoustic sensor and machine learning for process monitoring. The acoustic signals from a real powder-bed fusion AM process were collected using a fiber Bragg grating. The process parameters are intentionally tuned to achieve three levels of quality categories, which are related to the porosity contents inside the workpiece. The quality categories are defined as high, medium, and poor quality and their corresponding porosity contents are 0.07%, 0.30%, and 1.42%, respectively. Wavelet spectrograms of the signals and their encoded label representations, obtained from spectral clustering, are taken as features. A deep convolutional neural network is used to classify the features from each category and the classification accuracy ranges between 78% and 91%. Hence, the proposed method has significant industrial potentials for in situ and real-time quality monitoring of AM processes since it requires minimum modifications of commercially available industrial machines.
机译：增材制造（AM）被认为是制造业的一场革命。但是，高期望值面临着技术难题，无法进一步渗透到更广泛的行业中。主要原因是缺乏过程可重复性以及缺乏可靠且具有成本效益的过程监控。本文是对该领域现有研究的补充，并提出了将高灵敏声传感器和机器学习进行过程监控的独特组合。使用光纤布拉格光栅收集来自真实粉末床融合AM工艺的声音信号。故意调整工艺参数以实现三个级别的质量类别，这三个级别与工件内部的孔隙率有关。质量类别定义为高，中和劣质，其相应的孔隙率分别为0.07％，0.30％和1.42％。从频谱聚类获得的信号的小波频谱图及其编码的标签表示均作为特征。深度卷积神经网络用于对每个类别的特征进行分类，分类精度介于78％和91％之间。因此，所提出的方法具有对AM过程进行原位和实时质量监控的巨大工业潜力，因为它需要对商用工业机械进行最少的修改。

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