Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks

Additive manufacturing, also known as 3D printing, is a new technology that obliterates the geometrical limits of the produced workpieces and promises low running costs as compared to traditional manufacturing methods. Hence, additive manufacturing technology has high expectations in industry. Unfortunately, the lack of a proper quality monitoring prohibits the penetration of this technology into an extensive practice. This work investigates the feasibility of using acoustic emission for quality monitoring and combines a sensitive acoustic emission sensor with machine learning. The acoustic signals were recorded using a fiber Bragg grating sensor during the powder bed additive manufacturing process in a commercially available selective laser melting machine. The process parameters were intentionally tuned to invoke different processing regimes that lead to the formation of different types and concentrations of pores (1.42 +/- 0.85 %, 0.3 +/- 0.18 % and 0.07 +/- 0.02 %) inside the workpiece. According to this poor, medium and high part qualities were defined. The acoustic signals collected during processing were grouped accordingly and divided into two separate datasets; one for the training and one for the testing. The acoustic features were the relative energies of the narrow frequency bands of the wavelet packet transform, extracted from all the signals. The classifier, based on spectral convolutional neural network, was trained to differentiate the acoustic features of dissimilar quality. The confidence in classifications varies between 83 and 89 %. In view of the narrow range of porosity, the results can be considered as promising and they showed the feasibility of the quality monitoring using acoustic emission with the sub-layer spatial resolution. (C) 2017 Elsevier B.V. All rights reserved.