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The International Journal of Advanced Manufacturing Technology

Erschienen in:

16.09.2019 | ORIGINAL ARTICLE

Prediction of Inconel 718 roughness with acoustic emission using convolutional neural network based regression

verfasst von: David Ibarra-Zarate, Luz M. Alonso-Valerdi, Jorge Chuya-Sumba, Sixto Velarde-Valdez, Hector R. Siller

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 1-4/2019

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Abstract

Acoustic signals have valuable information and can complement mechanical signals (e.g., effort, roughness, and optics) since both of them have a good correlation. Furthermore, acoustic signals have non-invasive nature. In this work, roughness characterization via acoustic emission, along with the subsequent roughness detection based on convolutional neural networks, is proposed. Results show reliable and adequate roughness measurement via acoustic emission, and convolutional neural networks performance reached an accuracy of 88 % with a mean square error of 3.35 %. The main contribution of this work is the demonstration of deep learning network feasibility on roughness identification, where no previous signal processing is required and which moves towards a highly robust pattern recognition system.

Vorheriger Artikel A multiphase simulation study of electrochemical discharge machining of glass

Nächster Artikel Form error compensation in the slow tool servo machining of freeform optics

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Titel: Prediction of Inconel 718 roughness with acoustic emission using convolutional neural network based regression
verfasst von: David Ibarra-Zarate
Luz M. Alonso-Valerdi
Jorge Chuya-Sumba
Sixto Velarde-Valdez
Hector R. Siller
Publikationsdatum: 16.09.2019
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 1-4/2019
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-019-04378-7

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