Acoustic and plasma sensing of laser ablation via deep learning
Acoustic and plasma sensing of laser ablation via deep learning
Monitoring laser ablation when using high power lasers can be challenging due to plasma obscuring the view of the machined sample. Whilst the appearance of the generated plasma is correlated with the laser ablation conditions, extracting useful information is extremely difficult due to the highly nonlinear processes involved. Here, we show that deep learning can enable the identification of laser pulse energy and a prediction for the appearance of the ablated sample, directly from camera images of the plasma generated during single-pulse femtosecond ablation of silica. We show that this information can also be identified directly from the acoustic signal recorded during this process. This approach has the potential to enhance real-time feedback and monitoring of laser materials processing in situations where the sample is obscured from direct viewing, and hence could be an invaluable diagnostic for laser-based manufacturing.
28413-28422
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
14 August 2023
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James, Mills, Benjamin and Zervas, Michael N.
(2023)
Acoustic and plasma sensing of laser ablation via deep learning.
Optics Express, 31 (17), , [28413].
(doi:10.1364/OE.494700).
Abstract
Monitoring laser ablation when using high power lasers can be challenging due to plasma obscuring the view of the machined sample. Whilst the appearance of the generated plasma is correlated with the laser ablation conditions, extracting useful information is extremely difficult due to the highly nonlinear processes involved. Here, we show that deep learning can enable the identification of laser pulse energy and a prediction for the appearance of the ablated sample, directly from camera images of the plasma generated during single-pulse femtosecond ablation of silica. We show that this information can also be identified directly from the acoustic signal recorded during this process. This approach has the potential to enhance real-time feedback and monitoring of laser materials processing in situations where the sample is obscured from direct viewing, and hence could be an invaluable diagnostic for laser-based manufacturing.
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Published date: 14 August 2023
Additional Information:
Funding Information:
Funding. 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: 480950
URI: http://eprints.soton.ac.uk/id/eprint/480950
ISSN: 1094-4087
PURE UUID: 50cb2179-3265-4323-a7a0-94443031122b
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Date deposited: 11 Aug 2023 16:51
Last modified: 17 Mar 2024 03:22
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Contributors
Author:
James Grant-Jacob
Author:
Benjamin Mills
Author:
Michael N. Zervas
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