The University of Southampton
University of Southampton Institutional Repository

Neural networks for predictive laser machining capabilities

Neural networks for predictive laser machining capabilities
Neural networks for predictive laser machining capabilities
Predictive visualisation for laser-processing of materials can be challenging, as the nonlinear interaction of light and matter is complicated to model, particularly when scaling up from atom-level to bulk material. Here, we demonstrate a predictive visualisation approach that uses a pair of neural networks (NNs) that are trained using data obtained from laser machining using a digital micromirror device (DMD) acting as an intensity spatial light modulator. The DMD enables laser machining using many beam shapes, and hence can be used to produce significant amounts of training data for NNs. Here, the training data corresponds to hundreds of DMD patterns (i.e. beam shapes) and their associated images and 3D depth profiles. The trained NNs are able to generate a surface image and 3D depth profile, showing what the ablated surface would look like, for a wide range of ablating beam shapes. The predicted visualisations are remarkably effective and almost indistinguishable from real experimental data in appearance.

Such a NN approach has considerable advantages over modelling techniques that start from first-principles (i.e. light-atom interaction), since zero understanding of the underlying physical processes is needed, as instead the NN learns directly via observation of labelled experimental data. We will show that the NN learns key optical properties such as diffraction, the nonlinear interaction of light and matter, and the statistical distribution of debris and burring of material, all with zero human assistance. This offers a new paradigm in predictive capabilities, which could be applied to almost any manufacturing process.
SPIE
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
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
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020

Mills, Benjamin, Heath, Daniel, Grant-Jacob, James, MacKay, Benita, Scout and Eason, Robert (2019) Neural networks for predictive laser machining capabilities. In Emerging Digital Micromirror Device Based Systems and Applications XI. SPIE.. (doi:10.1117/12.2507375).

Record type: Conference or Workshop Item (Paper)

Abstract

Predictive visualisation for laser-processing of materials can be challenging, as the nonlinear interaction of light and matter is complicated to model, particularly when scaling up from atom-level to bulk material. Here, we demonstrate a predictive visualisation approach that uses a pair of neural networks (NNs) that are trained using data obtained from laser machining using a digital micromirror device (DMD) acting as an intensity spatial light modulator. The DMD enables laser machining using many beam shapes, and hence can be used to produce significant amounts of training data for NNs. Here, the training data corresponds to hundreds of DMD patterns (i.e. beam shapes) and their associated images and 3D depth profiles. The trained NNs are able to generate a surface image and 3D depth profile, showing what the ablated surface would look like, for a wide range of ablating beam shapes. The predicted visualisations are remarkably effective and almost indistinguishable from real experimental data in appearance.

Such a NN approach has considerable advantages over modelling techniques that start from first-principles (i.e. light-atom interaction), since zero understanding of the underlying physical processes is needed, as instead the NN learns directly via observation of labelled experimental data. We will show that the NN learns key optical properties such as diffraction, the nonlinear interaction of light and matter, and the statistical distribution of debris and burring of material, all with zero human assistance. This offers a new paradigm in predictive capabilities, which could be applied to almost any manufacturing process.

Full text not available from this repository.

More information

Published date: 4 March 2019

Identifiers

Local EPrints ID: 428811
URI: https://eprints.soton.ac.uk/id/eprint/428811
PURE UUID: 11e09b4e-a3cb-4210-b8ed-bc4d54baa622
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: 09 Apr 2019 00:38

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×