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LAISER: Putting the AI in Laser

LAISER: Putting the AI in Laser
LAISER: Putting the AI in Laser
Advances in lasers now allow the laser-based processing of almost any material. Innovation in this field is now becoming heavily focussed on making existing processing techniques more precise and efficient. A research area of particular current importance is therefore the development of real-time monitoring and feedback systems for laser machining, via visual inspection of the sample during machining.
Convolutional neural networks (CNNs) offer the capability for image processing without the need for understanding the underlying physical processes, and hence offer an ideal solution for the monitoring of laser machining, which itself is not fully understood.
In this talk, the application of CNNs for real-time monitoring and process control for laser machining will be discussed, along with the capability of CNNs for predicting the outcome of laser machining before the experiment occurs. In addition, an application of combining laser light with CNNs for real-time sensing of pollution particulates will be demonstrated.
Mills, Benjamin
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Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Xie, Yunhui
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MacKay, Benita Scout
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McDonnell, Michael David Tom
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Praeger, Matthew
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Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
McDonnell, Michael David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020

Mills, Benjamin, Heath, Daniel J, Xie, Yunhui, MacKay, Benita Scout, McDonnell, Michael David Tom, Praeger, Matthew, Grant-Jacob, James and Eason, R.W. (2019) LAISER: Putting the AI in Laser. AI3SD AI for Materials Discovery Workshop.

Record type: Conference or Workshop Item (Other)

Abstract

Advances in lasers now allow the laser-based processing of almost any material. Innovation in this field is now becoming heavily focussed on making existing processing techniques more precise and efficient. A research area of particular current importance is therefore the development of real-time monitoring and feedback systems for laser machining, via visual inspection of the sample during machining.
Convolutional neural networks (CNNs) offer the capability for image processing without the need for understanding the underlying physical processes, and hence offer an ideal solution for the monitoring of laser machining, which itself is not fully understood.
In this talk, the application of CNNs for real-time monitoring and process control for laser machining will be discussed, along with the capability of CNNs for predicting the outcome of laser machining before the experiment occurs. In addition, an application of combining laser light with CNNs for real-time sensing of pollution particulates will be demonstrated.

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More information

Published date: 19 March 2019
Venue - Dates: AI3SD AI for Materials Discovery Workshop, 2019-03-19

Identifiers

Local EPrints ID: 472180
URI: http://eprints.soton.ac.uk/id/eprint/472180
PURE UUID: 515c7c9f-f229-4b5e-b72c-4323dd25dbeb
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for Benita Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Michael David Tom McDonnell: ORCID iD orcid.org/0000-0003-4308-1165
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 R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204

Catalogue record

Date deposited: 28 Nov 2022 18:17
Last modified: 21 Jun 2023 01:52

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Contributors

Author: Benjamin Mills ORCID iD
Author: Daniel J Heath
Author: Yunhui Xie
Author: Benita Scout MacKay ORCID iD
Author: Michael David Tom McDonnell ORCID iD
Author: Matthew Praeger ORCID iD
Author: James Grant-Jacob ORCID iD
Author: R.W. Eason ORCID iD

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