ISSN:
1013-9826
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
The objective of this research was to apply the artificial neural network algorithm topredict the surface roughness in high speed milling operation. Tool length, feed rate, spindle speed,cutting path interval and run-out were used as five input neurons; and artificial neural networksmodel based on back-propagation algorithm was developed to predict the output neuron-surfaceroughness. A series of experiments was performed, and the results were estimated. Theexperimental results showed that the applied artificial neural network surface roughness predictiongave good accuracy in predicting the surface roughness under a variety of combinations of cuttingconditions
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/56/transtech_doi~10.4028%252Fwww.scientific.net%252FKEM.364-366.713.pdf
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