The University of Southampton
University of Southampton Institutional Repository

Predictive modelling of fibre laser machining topography via deep learning

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
0a50732a-ef3f-4042-82f4-9b573c8c9ee8
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Codemard, Christophe
3aa50483-b61c-4e7e-b178-c9a88bb47bef
Harrison, Paul
2d67bb8e-4452-4b8f-adc0-5e6d324ac825
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis N.
26d5ccd0-3c9e-45fa-93c4-08366b2dcd85
Courtier, Alexander
0a50732a-ef3f-4042-82f4-9b573c8c9ee8
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Codemard, Christophe
3aa50483-b61c-4e7e-b178-c9a88bb47bef
Harrison, Paul
2d67bb8e-4452-4b8f-adc0-5e6d324ac825
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis N.
26d5ccd0-3c9e-45fa-93c4-08366b2dcd85

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. p. 1 .

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
Available under License Creative Commons Attribution.
Download (610kB)

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
ORCID for Alexander Courtier: ORCID iD orcid.org/0000-0003-1943-4055
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 14 Jul 2022 17:10
Last modified: 17 Mar 2024 03:59

Export record

Contributors

Author: Alexander Courtier ORCID iD
Author: Matthew Praeger ORCID iD
Author: Paul Harrison
Author: Benjamin Mills ORCID iD
Author: Michalis N. Zervas

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×