Classifying paint colour using acoustic data from laser ablation
Classifying paint colour using acoustic data from laser ablation
We present an approach for identifying the paint colour and tone on a surface by using the acoustic signal collected during laser ablation. We trained convolutional neural networks to classify the colour (from 8 different colours) and the tone (the percentage of black in the paint). The colour was predicted with ∼91% accuracy and the tone with an R-value of 0.95. This technique has significant potential for supporting real-time optimisation in laser-material processing, particularly for high-precision laser cleaning, as well as broader applications where direct visual observation of the sample is not feasible.
766-774
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
18 March 2025
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James A., Zervas, Michalis N. and Mills, Ben
(2025)
Classifying paint colour using acoustic data from laser ablation.
Optical Materials Express, 15 (4), .
(doi:10.1364/OME.558459).
Abstract
We present an approach for identifying the paint colour and tone on a surface by using the acoustic signal collected during laser ablation. We trained convolutional neural networks to classify the colour (from 8 different colours) and the tone (the percentage of black in the paint). The colour was predicted with ∼91% accuracy and the tone with an R-value of 0.95. This technique has significant potential for supporting real-time optimisation in laser-material processing, particularly for high-precision laser cleaning, as well as broader applications where direct visual observation of the sample is not feasible.
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Accepted/In Press date: 10 March 2025
Published date: 18 March 2025
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Local EPrints ID: 499905
URI: http://eprints.soton.ac.uk/id/eprint/499905
ISSN: 2159-3930
PURE UUID: cf8b8afe-a925-433c-a4a5-35f7606be864
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Date deposited: 08 Apr 2025 16:36
Last modified: 22 Aug 2025 02:03
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Contributors
Author:
James A. Grant-Jacob
Author:
Michalis N. Zervas
Author:
Ben Mills
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