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Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

Author

Listed:
  • Michael D. T. McDonnell

    (University of Southampton)

  • Daniel Arnaldo

    (Oxford Lasers)

  • Etienne Pelletier

    (Oxford Lasers)

  • James A. Grant-Jacob

    (University of Southampton)

  • Matthew Praeger

    (University of Southampton)

  • Dimitris Karnakis

    (Oxford Lasers)

  • Robert W. Eason

    (University of Southampton)

  • Ben Mills

    (University of Southampton)

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.

Suggested Citation

  • Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01717-4
    DOI: 10.1007/s10845-020-01717-4
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    References listed on IDEAS

    as
    1. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    2. William Mycroft & Mordechai Katzman & Samuel Tammas-Williams & Everth Hernandez-Nava & George Panoutsos & Iain Todd & Visakan Kadirkamanathan, 2020. "A data-driven approach for predicting printability in metal additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1769-1781, October.
    3. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
    4. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
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    Cited by:

    1. Patrick G. Mongan & Vedant Modi & John W. McLaughlin & Eoin P. Hinchy & Ronan M. O’Higgins & Noel P. O’Dowd & Conor T. McCarthy, 2022. "Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1125-1138, April.
    2. Kai Liao & Wenjun Wang & Xuesong Mei & Wenwen Tian & Hai Yuan & Mingqiong Wang & Bozhe Wang, 2023. "Shape regulation of tapered microchannels in silica glass ablated by femtosecond laser with theoretical modeling and machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2907-2924, October.
    3. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
    4. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.

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