Neural network modeling and analysis of the material removal process during laser machining

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DOIResolve DOI: http://doi.org/10.1007/s00170-002-1441-9
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TypeArticle
Journal titleThe International Journal of Advanced Manufacturing Technology
ISSN1433-3015
Volume22
Issue1-2
Pages4153; # of pages: 13
Subjectlaser micro-machining; pulse energy; crater geometry; artificial neural network
AbstractTo manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. 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. In addition, 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 significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.
Publication date
LanguageEnglish
AffiliationNRC Industrial Materials Institute; National Research Council Canada
Peer reviewedYes
NPARC number21272969
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Record identifiere5b4a0cf-f2ca-4ed1-9c41-258a2cc6d65e
Record created2014-12-03
Record modified2016-05-09
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