Visualizing laser ablation using plasma imaging and deep learning
Visualizing laser ablation using plasma imaging and deep learning
High power laser ablation can lead to the creation of plasma and the emission of bright light, which can prevent the direct observation of the workpiece. Alternative techniques for enabling the visualization of the sample during laser machining are therefore of interest. Here, we show that the plasma created during laser ablation, when viewed perpendicular to the sample surface, contains information regarding the appearance of the sample. Specifically, we show that deep learning can predict the 2D appearance of the sample, directly from 2D projected images of the plasma produced during single pulse femtosecond laser ablation. In addition, this approach also enables the identification of the pulse energy of the most recent laser pulse used to machine the sample. This work could have applications across laser materials processing in research and industry, in cases where there is a requirement for real-time visualization of the sample surface during laser ablation.
1678-1687
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
15 July 2023
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James A., Mills, Benjamin and Zervas, Michael N.
(2023)
Visualizing laser ablation using plasma imaging and deep learning.
Optics Continuum, 2 (7), .
(doi:10.1364/OPTCON.495923).
Abstract
High power laser ablation can lead to the creation of plasma and the emission of bright light, which can prevent the direct observation of the workpiece. Alternative techniques for enabling the visualization of the sample during laser machining are therefore of interest. Here, we show that the plasma created during laser ablation, when viewed perpendicular to the sample surface, contains information regarding the appearance of the sample. Specifically, we show that deep learning can predict the 2D appearance of the sample, directly from 2D projected images of the plasma produced during single pulse femtosecond laser ablation. In addition, this approach also enables the identification of the pulse energy of the most recent laser pulse used to machine the sample. This work could have applications across laser materials processing in research and industry, in cases where there is a requirement for real-time visualization of the sample surface during laser ablation.
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Plasma_Accepted
- Accepted Manuscript
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optcon-2-7-1678
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More information
Accepted/In Press date: 23 June 2023
Published date: 15 July 2023
Additional Information:
Funding Information:
Engineering and Physical Sciences Research Council (EP/P027644/1, EP/T026197/1, EP/W028786/1).
Publisher Copyright:
© 2023 OSA - The Optical Society. All rights reserved.
Identifiers
Local EPrints ID: 478897
URI: http://eprints.soton.ac.uk/id/eprint/478897
ISSN: 2770-0208
PURE UUID: a390fd0d-96fa-41a2-82a4-7e9fdca5abb0
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Date deposited: 12 Jul 2023 16:43
Last modified: 18 Mar 2024 02:38
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