Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining
Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining
Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.
Deep learning, Fabrication, Laser machining, Manufacturing, Micro-structuring, Neural networks
1471–1483
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Karnakis, Dimitris
3b909986-40f9-465c-995b-a30c482ec65b
Arnaldo, Daniel
43c31cf2-5752-4a05-8b52-ee85bbab0c05
Pelletier, Etienne
1b1a850d-8de9-4d44-9ad3-16427b44b271
June 2021
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Karnakis, Dimitris
3b909986-40f9-465c-995b-a30c482ec65b
Arnaldo, Daniel
43c31cf2-5752-4a05-8b52-ee85bbab0c05
Pelletier, Etienne
1b1a850d-8de9-4d44-9ad3-16427b44b271
McDonnell, Michael, David Tom, Grant-Jacob, James, Mills, Benjamin, Eason, R.W., Praeger, Matthew, Karnakis, Dimitris, Arnaldo, Daniel and Pelletier, Etienne
(2021)
Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining.
Journal of Intelligent Manufacturing, 32 (5), .
(doi:10.1007/s10845-020-01717-4).
Abstract
Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.
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More information
Accepted/In Press date: 13 November 2020
Published date: June 2021
Additional Information:
Funding Information:
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. Supporting data for this submission can be found at https://doi.org/10.5258/SOTON/D1687 . Oxford Lasers acknowledges the support of Innovate UK and Newton Fund (LASTEC Grant Funding No. 102713).
Publisher Copyright:
© 2021, The Author(s).
Keywords:
Deep learning, Fabrication, Laser machining, Manufacturing, Micro-structuring, Neural networks
Identifiers
Local EPrints ID: 452774
URI: http://eprints.soton.ac.uk/id/eprint/452774
ISSN: 0956-5515
PURE UUID: b019a3b0-147b-438a-8740-67efb48ba206
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Date deposited: 20 Dec 2021 17:33
Last modified: 17 Mar 2024 03:22
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Contributors
Author:
Michael, David Tom McDonnell
Author:
Dimitris Karnakis
Author:
Daniel Arnaldo
Author:
Etienne Pelletier
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