Lasers that learn: the interface of laser machining and machine learning
Lasers that learn: the interface of laser machining and machine learning
Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, the highly nonlinear interactions between laser light and matter, and the variety of materials involved, mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in the field of machine learning now mean that neural networks are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well-beyond existing theoretical approaches, with applications including 3D surface visualisation and real-time error correction. In this review, a perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of the future milestones and challenges for this field.
207-224
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
October 2021
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin and Grant-Jacob, James
(2021)
Lasers that learn: the interface of laser machining and machine learning.
IET Optoelectronics, 15 (5), .
(doi:10.1049/ote2.12039).
Abstract
Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, the highly nonlinear interactions between laser light and matter, and the variety of materials involved, mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in the field of machine learning now mean that neural networks are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well-beyond existing theoretical approaches, with applications including 3D surface visualisation and real-time error correction. In this review, a perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of the future milestones and challenges for this field.
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Submitted date: 1 September 2020
Accepted/In Press date: 2 February 2021
Published date: October 2021
Additional Information:
Funding Information:
B. M. was supported by an EPSRC Early Career Fellowship (EP/N03368X/1) and EPSRC Investigator-Led Grant (EP/T026197/1). The authors would like to thank Prof. Robert Eason for constructive help in reviewing this manuscript. Data supporting this study are openly available from the University of Southampton repository at https://doi.org/10.5258/SOTON/D1710.
Funding Information:
B. M. was supported by an EPSRC Early Career Fellowship (EP/N03368X/1) and EPSRC Investigator‐Led Grant (EP/T026197/1). The authors would like to thank Prof. Robert Eason for constructive help in reviewing this manuscript. Data supporting this study are openly available from the University of Southampton repository at https://doi.org/10.5258/SOTON/D1710 .
Publisher Copyright:
© 2021 The Authors. IET Optoelectronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
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Local EPrints ID: 446641
URI: http://eprints.soton.ac.uk/id/eprint/446641
ISSN: 1751-8768
PURE UUID: 176ca4e0-2091-4be6-81fc-99b72854dfdf
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Date deposited: 17 Feb 2021 17:30
Last modified: 17 Mar 2024 03:22
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Author:
Benjamin Mills
Author:
James Grant-Jacob
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