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Predictive capabilities for laser machining via a neural network

Predictive capabilities for laser machining via a neural network
Predictive capabilities for laser machining via a neural network
The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target.
This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.
1094-4087
1-9
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
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
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020

Mills, Benjamin, Heath, Daniel, Grant-Jacob, James and Eason, Robert (2018) Predictive capabilities for laser machining via a neural network. Optics Express, 26 (13), 1-9. (doi:10.1364/OE.26.017245).

Record type: Article

Abstract

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target.
This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.

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More information

Accepted/In Press date: 1 May 2018
e-pub ahead of print date: 20 June 2018

Identifiers

Local EPrints ID: 421921
URI: http://eprints.soton.ac.uk/id/eprint/421921
ISSN: 1094-4087
PURE UUID: db47e54a-d201-4565-b139-c33f20248854
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 Robert Eason: ORCID iD orcid.org/0000-0001-9704-2204

Catalogue record

Date deposited: 10 Jul 2018 16:30
Last modified: 15 Sep 2021 01:57

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