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Image-based monitoring of femtosecond laser machining via a neural network

Image-based monitoring of femtosecond laser machining via a neural network
Image-based monitoring of femtosecond laser machining via a neural network
Femtosecond laser machining offers the potential for high-precision materials processing. However, due to the nonlinear processes inherent when using femtosecond pulses, experimental random noise can result in large variations in the machined quality, and hence methods for closed loop feedback are of interest. Here we demonstrate the application of a neural network, acting as a pattern recognition algorithm, for visual monitoring of the target substrate via a camera that observes the sample during machining. This approach has the advantage that it requires zero knowledge of the underlying physical processes, and hence avoids the need for modelling the complex photon-atom interactions that occur with femtosecond laser machining. The neural network was shown to accurately determine the type of material, the laser fluence and the number of pulses, directly from a single image of the sample and within ten milliseconds. This approach provides the potential for real-time feedback for femtosecond laser materials processing.
Mills, Ben
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Heath, Daniel J.
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
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Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel J.
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020

Mills, Ben, Heath, Daniel J., Grant-Jacob, James A., Xie, Yunhui and Eason, Robert W. (2019) Image-based monitoring of femtosecond laser machining via a neural network. Journal of Physics: Photonics, 1 (1). (doi:10.1088/2515-7647/aad5a0).

Record type: Article

Abstract

Femtosecond laser machining offers the potential for high-precision materials processing. However, due to the nonlinear processes inherent when using femtosecond pulses, experimental random noise can result in large variations in the machined quality, and hence methods for closed loop feedback are of interest. Here we demonstrate the application of a neural network, acting as a pattern recognition algorithm, for visual monitoring of the target substrate via a camera that observes the sample during machining. This approach has the advantage that it requires zero knowledge of the underlying physical processes, and hence avoids the need for modelling the complex photon-atom interactions that occur with femtosecond laser machining. The neural network was shown to accurately determine the type of material, the laser fluence and the number of pulses, directly from a single image of the sample and within ten milliseconds. This approach provides the potential for real-time feedback for femtosecond laser materials processing.

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More information

Accepted/In Press date: 25 July 2018
e-pub ahead of print date: 6 December 2018
Published date: 1 January 2019

Identifiers

Local EPrints ID: 423066
URI: https://eprints.soton.ac.uk/id/eprint/423066
PURE UUID: 3326a65f-f24e-4565-b750-0474c9f31932
ORCID for Ben Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for James A. Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Robert W. Eason: ORCID iD orcid.org/0000-0001-9704-2204

Catalogue record

Date deposited: 13 Aug 2018 16:30
Last modified: 14 Mar 2019 05:04

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Contributors

Author: Ben Mills ORCID iD
Author: Daniel J. Heath
Author: Yunhui Xie
Author: Robert W. Eason ORCID iD

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