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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.
Shevchik et al. (Sun,) studied this question.