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Predictive visualisation of laser processing via deep learning

Predictive visualisation of laser processing via deep learning
Predictive visualisation of laser processing via deep learning
Laser materials processing is a non-contact manufacturing technique that has applications in many areas of industry and academia. However, due to the highly nonlinear nature, analytical and numerical modelling of laser processing can be extremely challenging. In recent years, deep learning has rapidly become a critically important tool for data-driven modelling of complex systems. This thesis therefore seeks to apply the latest advancements in deep learning for the purpose of predicting, modelling and analysing a range of laser materials processing phenomena.
In this thesis, deep learning techniques were used to predict the appearance and topography of materials cut with continuous wave and pulsed laser sources. The neural networks were able to correctly predict the appearance and topography of the edge of laser cut metal samples, as well as the depth profile of ablated holes produced via femtosecond machining. These techniques enable the prediction of laser cutting defects and ablated hole depth profiles, as well as enabling the simulation of large ranges of configurations through parameter interpolation, all of which lead the way towards automated experimental optimisation. Neural networks were also shown capable of unsupervised identification of underlying experimental parameters, a result which offers an early demonstration of the potential for deep learning in data-driven scientific discovery.
University of Southampton
Courtier, Alexander
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Courtier, Alexander
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Zervas, Michalis
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Mills, Ben
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Courtier, Alexander (2024) Predictive visualisation of laser processing via deep learning. University of Southampton, Doctoral Thesis, 204pp.

Record type: Thesis (Doctoral)

Abstract

Laser materials processing is a non-contact manufacturing technique that has applications in many areas of industry and academia. However, due to the highly nonlinear nature, analytical and numerical modelling of laser processing can be extremely challenging. In recent years, deep learning has rapidly become a critically important tool for data-driven modelling of complex systems. This thesis therefore seeks to apply the latest advancements in deep learning for the purpose of predicting, modelling and analysing a range of laser materials processing phenomena.
In this thesis, deep learning techniques were used to predict the appearance and topography of materials cut with continuous wave and pulsed laser sources. The neural networks were able to correctly predict the appearance and topography of the edge of laser cut metal samples, as well as the depth profile of ablated holes produced via femtosecond machining. These techniques enable the prediction of laser cutting defects and ablated hole depth profiles, as well as enabling the simulation of large ranges of configurations through parameter interpolation, all of which lead the way towards automated experimental optimisation. Neural networks were also shown capable of unsupervised identification of underlying experimental parameters, a result which offers an early demonstration of the potential for deep learning in data-driven scientific discovery.

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Published date: 2024

Identifiers

Local EPrints ID: 493057
URI: http://eprints.soton.ac.uk/id/eprint/493057
PURE UUID: dce8cd97-cb1e-463c-9719-dfd96242f009
ORCID for Alexander Courtier: ORCID iD orcid.org/0000-0003-1943-4055
ORCID for Michalis Zervas: ORCID iD orcid.org/0000-0002-0651-4059
ORCID for Ben Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 22 Aug 2024 16:36
Last modified: 23 Aug 2024 01:58

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Contributors

Author: Alexander Courtier ORCID iD
Thesis advisor: Michalis Zervas ORCID iD
Thesis advisor: Ben Mills ORCID iD

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