Predictive visualisation of fibre laser machining via deep learning
Predictive visualisation of fibre laser machining via deep learning
Fibre laser materials processing is a non-contact manufacturing technique used widely across academia and industry. However, defects such as cracks and striations are generally observed on the surface of the cut material, and hence modelling of the light-matter interaction is of particular interest. Laser machining is a highly non-linear process and is challenging to model via equation-based approaches (e.g. finite element modelling), particularly as the physical origins of many effects are not fully understood [1]. Recently, deep learning has been shown to be capable of modelling femtosecond laser machining [2]. Modelling via deep learning uses a data-driven approach, where the model is created directly from experimental data. Deep learning therefore provides an excellent opportunity for simulating laser machining effects that are not fully understood, and consequently assists in parameter optimisation and even provide novel insights and understanding.
Courtier, Alexander
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McDonnell, Michael David Tom
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Praeger, Matthew
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Grant-Jacob, James
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Codemard, Christophe A.
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Mills, Benjamin
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Zervas, Michael N.
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25 June 2021
Courtier, Alexander
0a50732a-ef3f-4042-82f4-9b573c8c9ee8
McDonnell, Michael David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Codemard, Christophe A.
0a7db5d9-507e-41e3-88bb-2606402f558b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Courtier, Alexander, McDonnell, Michael David Tom, Praeger, Matthew, Grant-Jacob, James, Codemard, Christophe A., Mills, Benjamin and Zervas, Michael N.
(2021)
Predictive visualisation of fibre laser machining via deep learning.
In 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021.
14 pp
.
(doi:10.1109/CLEO/Europe-EQEC52157.2021.9542389).
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Conference or Workshop Item
(Paper)
Abstract
Fibre laser materials processing is a non-contact manufacturing technique used widely across academia and industry. However, defects such as cracks and striations are generally observed on the surface of the cut material, and hence modelling of the light-matter interaction is of particular interest. Laser machining is a highly non-linear process and is challenging to model via equation-based approaches (e.g. finite element modelling), particularly as the physical origins of many effects are not fully understood [1]. Recently, deep learning has been shown to be capable of modelling femtosecond laser machining [2]. Modelling via deep learning uses a data-driven approach, where the model is created directly from experimental data. Deep learning therefore provides an excellent opportunity for simulating laser machining effects that are not fully understood, and consequently assists in parameter optimisation and even provide novel insights and understanding.
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Alex_Courtier_Predictive_Visualisation_of_Fibre_Laser_Machining_via_Deep_Learning
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In preparation date: 28 January 2021
e-pub ahead of print date: 25 June 2021
Published date: 25 June 2021
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Local EPrints ID: 454514
URI: http://eprints.soton.ac.uk/id/eprint/454514
PURE UUID: 423a91cc-0c13-4411-aaf2-dc7a673e1a66
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Date deposited: 14 Feb 2022 17:56
Last modified: 17 Mar 2024 03:59
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