Predictive modelling of fibre laser machining topography via deep learning
Predictive modelling of fibre laser machining topography via deep learning
Predicting target material topography resulting from fibre laser cutting is
challenging. We show that deep learning offers a data-driven capability for predicting the topography of 2mm thick laser machined stainless steel for different cutting speeds
1
Courtier, Alexander
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Praeger, Matthew
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Grant-Jacob, James
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Codemard, Christophe
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Harrison, Paul
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Mills, Benjamin
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Zervas, Michalis N.
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31 May 2022
Courtier, Alexander
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Praeger, Matthew
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Grant-Jacob, James
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Codemard, Christophe
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Harrison, Paul
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Mills, Benjamin
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Zervas, Michalis N.
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Courtier, Alexander, Praeger, Matthew, Grant-Jacob, James, Codemard, Christophe, Harrison, Paul, Mills, Benjamin and Zervas, Michalis N.
(2022)
Predictive modelling of fibre laser machining topography via deep learning.
CLEO, , San Jose, United States.
15 - 20 May 2022.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
Predicting target material topography resulting from fibre laser cutting is
challenging. We show that deep learning offers a data-driven capability for predicting the topography of 2mm thick laser machined stainless steel for different cutting speeds
Text
Predictive Modelling of Fibre Laser Machining Topography via Deep Learning
More information
Published date: 31 May 2022
Venue - Dates:
CLEO, , San Jose, United States, 2022-05-15 - 2022-05-20
Identifiers
Local EPrints ID: 467583
URI: http://eprints.soton.ac.uk/id/eprint/467583
PURE UUID: 82aa89d6-68a9-49e3-b3e1-aa0b47e6c73a
Catalogue record
Date deposited: 14 Jul 2022 17:10
Last modified: 17 Mar 2024 03:59
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Contributors
Author:
Alexander Courtier
Author:
Matthew Praeger
Author:
James Grant-Jacob
Author:
Christophe Codemard
Author:
Paul Harrison
Author:
Benjamin Mills
Author:
Michalis N. Zervas
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