Neural network approach to modeling the laser micro-machining process

Basem F. Yousef, George K. Knopf, Evgueni V. Bordatchev, Suwas K. Nikumb

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Lasers are used for a variety of micro-machining applications because these tools provide a highly focused energy source that can be easily transmitted and manipulated to create geometric micro-features, often as small as the laser wavelength. Micro-machining with a laser beam is, however, a complex dynamic process with numerous nonlinear and stochastic parameters [1-3]. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. Furthermore, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can greatly influence the machining process and the quality of part geometry.

Original languageEnglish
Article number1031320
Pages (from-to)193-195
Number of pages3
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume10313
DOIs
Publication statusPublished - May 9 2002
Externally publishedYes
EventSPIE Regional Meeting on Optoelectronics, Photonics, and Imaging, Opto-Canada 2002 - Ottawa, Canada
Duration: May 9 2002May 10 2002

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Neural network approach to modeling the laser micro-machining process'. Together they form a unique fingerprint.

Cite this