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Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing.

Viswanathan, V.; McCloskey, Alex; Mathur, Ruchir; Nguyen, Dinh T.; Faisal, Nadimul Haque; Prathuru, Anil; Llavori, Iñigo; Murphy, Adrian; Tiwari, Ashutosh; Matthews, Allan; Agrawal, Anupam; Goel, Saurav


V. Viswanathan

Alex McCloskey

Ruchir Mathur

Dinh T. Nguyen

Iñigo Llavori

Adrian Murphy

Ashutosh Tiwari

Allan Matthews

Anupam Agrawal

Saurav Goel


Thermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in digitalisation of the thermal spraying process lies in instrumenting the manufacturing platform, as the process includes harsh conditions such as UV rays, high-plasma temperatures, dusty chemical environments and inaccessibility of the spray booth. This paper introduces a novel application of machine learning to the acoustic emission spectra of thermal spraying. By transitioning from the amplitude-time domain to a Fourier-transformed frequency-time domain, it is possible to predict anomalies in real-time - a crucial step towards sustainable material and manufacturing digitalisation. Our experimental results also indicate that this method is suitable for industrial applications, by generating useful data that can be used to develop Visual Geometry Group (VGG) transfer learning models to overcome the traditional limitations of convoluted neural networks (CNN).


VISWANATHAN, V., MCCLOSKEY, A., MATHUR, R., NGUYEN, D.T., FAISAL, N.H., PRATHURU, A., LLAVORI, I., MURPHY, A., TIWARI, A., MATTHEWS, A., AGRAWAL, A. and GOEL, S. 2024. Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing. Mechanical systems and signal processing [online], 208, article number 111030. Available from:

Journal Article Type Article
Acceptance Date Dec 11, 2023
Online Publication Date Dec 19, 2023
Publication Date Feb 15, 2024
Deposit Date Jan 5, 2024
Publicly Available Date Jan 5, 2024
Journal Mechanical systems and signal processing
Print ISSN 0888-3270
Electronic ISSN 1096-1216
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 208
Article Number 111030
Keywords Thermal spraying; Acoustics; Machine learning; Manufacturing; Manufacturing technology
Public URL


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