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
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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
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Eason, R.W.
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19 March 2019
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
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Date deposited: 28 Nov 2022 18:17
Last modified: 21 Jun 2023 01:52
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Contributors
Author:
Daniel J Heath
Author:
Yunhui Xie
Author:
Benita Scout MacKay
Author:
Michael David Tom McDonnell
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