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.
1-10
Mills, Ben
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Heath, Daniel J.
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Grant-Jacob, James A.
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Xie, Yunhui
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Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
1 January 2019
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 (3), .
(doi:10.1088/2515-7647/aad5a0).
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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Mills 2019 J. Phys. Photonics 1 015008
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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: http://eprints.soton.ac.uk/id/eprint/423066
PURE UUID: 3326a65f-f24e-4565-b750-0474c9f31932
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Date deposited: 13 Aug 2018 16:30
Last modified: 16 Mar 2024 06:56
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Author:
Daniel J. Heath
Author:
Yunhui Xie
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