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Deep learning for the monitoring and process control of femtosecond laser machining

Deep learning for the monitoring and process control of femtosecond laser machining
Deep learning for the monitoring and process control of femtosecond laser machining
Whilst advances in lasers now allow the processing of practically any material, further optimisation in precision and efficiency is highly desirable, in particular via the development of real-time detection and feedback systems. Here, we demonstrate the application of neural networks for system monitoring via visual observation of the work-piece during laser processing. Specifically, we show quantification of unintended laser beam modifications, namely translation and rotation, along with real-time closed-loop feedback capable of halting laser processing immediately after machining through a ~450 nm thick copper layer.We show that this approach can detect translations in beam position that are smaller than the pixels of the camera used for observation. We also show a method of data augmentation that can be used to significantly reduce the quantity of experimental data needed for training a neural network. Unintentional beam translations and rotations are detected concurrently, hence demonstrating the feasibility for simultaneous identification of many laser machining parameters. Neural networks are an ideal solution, as they require zero understanding of the physical properties of laser machining, and instead are trained directly from experimental data.
Femtosecond laser, Artificial intelligence, Convolutional Neural Networks, feedback control
2515-7647
1-10
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Xie, Yunhui, Heath, Daniel J, Grant-Jacob, James, MacKay, Benita, Scout, McDonnell, Michael, David Tom, Praeger, Matthew, Eason, Robert and Mills, Benjamin (2019) Deep learning for the monitoring and process control of femtosecond laser machining. Journal of Physics: Photonics, 1 (3), 1-10. (doi:10.1088/2515-7647/ab281a).

Record type: Article

Abstract

Whilst advances in lasers now allow the processing of practically any material, further optimisation in precision and efficiency is highly desirable, in particular via the development of real-time detection and feedback systems. Here, we demonstrate the application of neural networks for system monitoring via visual observation of the work-piece during laser processing. Specifically, we show quantification of unintended laser beam modifications, namely translation and rotation, along with real-time closed-loop feedback capable of halting laser processing immediately after machining through a ~450 nm thick copper layer.We show that this approach can detect translations in beam position that are smaller than the pixels of the camera used for observation. We also show a method of data augmentation that can be used to significantly reduce the quantity of experimental data needed for training a neural network. Unintentional beam translations and rotations are detected concurrently, hence demonstrating the feasibility for simultaneous identification of many laser machining parameters. Neural networks are an ideal solution, as they require zero understanding of the physical properties of laser machining, and instead are trained directly from experimental data.

Text
Xie 2019 J. Phys. Photonics 1 035002 - Version of Record
Available under License Creative Commons Attribution.
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More information

Submitted date: 28 January 2019
Accepted/In Press date: 10 June 2019
Published date: 28 June 2019
Keywords: Femtosecond laser, Artificial intelligence, Convolutional Neural Networks, feedback control

Identifiers

Local EPrints ID: 432292
URI: https://eprints.soton.ac.uk/id/eprint/432292
ISSN: 2515-7647
PURE UUID: 2981f5a1-a3b9-4db4-b4ae-aae2917980da
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benita, Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Robert Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 08 Jul 2019 16:30
Last modified: 10 Jul 2019 00:38

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