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Image-based monitoring of high-precision laser machining via a convolutional neural network

Image-based monitoring of high-precision laser machining via a convolutional neural network
Image-based monitoring of high-precision laser machining via a convolutional neural network
Materials processing using femtosecond laser pulses offers the potential for high-precision manufacturing. However, due to the associated nonlinear processes, even small levels of experimental noise (e.g. instability in laser power, or unexpected debris) can result in substantial deviations from the desired machined structures. There is therefore much interest in the development of closed-loop feedback processes. Recent advances in the algorithms behind neural networks, and in particular convolutional neural networks (CNNs) have led to rapid advancements in the field. Here, we will present the first demonstration of the application of a CNN for observing and identifying the experimental parameters exclusively from a camera that observes the sample during laser machining. We will show that the CNN was able to accurately determine the laser fluence, number of pulses and the material used.
Although there are many other computational approaches for image-based feedback, this CNN approach has the significant advantage that it works purely as a pattern recognition device, and hence requires minimal human input with regards to the physical processes that underlie the laser machining process. Therefore, this avoids the need for a comprehensive programmatical description of the nonlinear interaction of laser light and material. Training time was one hour, and the time to process and identify the experimental parameters from a single image was approximately 30 milliseconds, hence showing the potential for a CNN to act as the central component of a real-time feedback system for laser machining, and enabling undesired or incorrect machining to be immediately compensated.
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020

Mills, Benjamin, Heath, Daniel, Grant-Jacob, James, Xie, Yunhui, MacKay, Benita, Scout and Eason, Robert (2019) Image-based monitoring of high-precision laser machining via a convolutional neural network. In Photonics West.

Record type: Conference or Workshop Item (Paper)

Abstract

Materials processing using femtosecond laser pulses offers the potential for high-precision manufacturing. However, due to the associated nonlinear processes, even small levels of experimental noise (e.g. instability in laser power, or unexpected debris) can result in substantial deviations from the desired machined structures. There is therefore much interest in the development of closed-loop feedback processes. Recent advances in the algorithms behind neural networks, and in particular convolutional neural networks (CNNs) have led to rapid advancements in the field. Here, we will present the first demonstration of the application of a CNN for observing and identifying the experimental parameters exclusively from a camera that observes the sample during laser machining. We will show that the CNN was able to accurately determine the laser fluence, number of pulses and the material used.
Although there are many other computational approaches for image-based feedback, this CNN approach has the significant advantage that it works purely as a pattern recognition device, and hence requires minimal human input with regards to the physical processes that underlie the laser machining process. Therefore, this avoids the need for a comprehensive programmatical description of the nonlinear interaction of laser light and material. Training time was one hour, and the time to process and identify the experimental parameters from a single image was approximately 30 milliseconds, hence showing the potential for a CNN to act as the central component of a real-time feedback system for laser machining, and enabling undesired or incorrect machining to be immediately compensated.

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More information

Published date: 6 February 2019

Identifiers

Local EPrints ID: 428810
URI: https://eprints.soton.ac.uk/id/eprint/428810
PURE UUID: e80e9270-4c2c-431f-989c-40734a55fb24
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
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

Catalogue record

Date deposited: 11 Mar 2019 17:30
Last modified: 14 Mar 2019 01:55

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Contributors

Author: Benjamin Mills ORCID iD
Author: Daniel Heath
Author: Yunhui Xie
Author: Benita, Scout MacKay ORCID iD
Author: Robert Eason ORCID iD

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