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

Michael D. T. McDonnell (), Daniel Arnaldo, Etienne Pelletier, James A. Grant-Jacob, Matthew Praeger, Dimitris Karnakis, Robert W. Eason and Ben Mills
Additional contact information
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

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 5, No 16, 1483 pages

Abstract: 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.

Keywords: Laser machining; Neural networks; Deep learning; Fabrication; Manufacturing; Micro-structuring (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01717-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01717-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01717-4

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().