Machine learning for 3D simulated visualization of laser machining

  • Heath D
  • Grant-Jacob J
  • Xie Y
  • et al.
43Citations
Citations of this article
57Readers
Mendeley users who have this article in their library.
Get full text

Abstract

© 2018 OSA - The Optical Society. All rights reserved. Laser machining can depend on the combination of many complex and nonlinear physical processes. Simulations of laser machining that are built from first-principles, such as the photon-atom interaction, are therefore challenging to scale-up to experimentally useful dimensions. Here, we demonstrate a simulation approach using a neural network, which requires zero knowledge of the underlying physical processes and instead uses experimental data directly to create the model of the experiment. The neural network modelling approach was shown to accurately predict the 3D surface profile of the laser machined surface after exposure to various spatial intensity profiles, and was used to discover trends inherent within the experimental data that would have otherwise been difficult to discover.

Cite

CITATION STYLE

APA

Heath, D. J., Grant-Jacob, J. A., Xie, Y., Mackay, B. S., Baker, J. A. G., Eason, R. W., & Mills, B. (2018). Machine learning for 3D simulated visualization of laser machining. Optics Express, 26(17), 21574. https://doi.org/10.1364/oe.26.021574

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free