@article{(Open Science Index):https://publications.waset.org/pdf/3672,
	  title     = {Modeling of Surface Roughness in Vibration Cutting by Artificial Neural Network},
	  author    = {H. Soleimanimehr and  M. J. Nategh  and  S. Amini},
	  country	= {},
	  institution	= {},
	  abstract     = {Development of artificial neural network (ANN) for
prediction of aluminum workpieces' surface roughness in ultrasonicvibration
assisted turning (UAT) has been the subject of the present
study. Tool wear as the main cause of surface roughness was also
investigated. ANN was trained through experimental data obtained
on the basis of full factorial design of experiments. Various
influential machining parameters were taken into consideration. It
was illustrated that a multilayer perceptron neural network could
efficiently model the surface roughness as the response of the
network, with an error less than ten percent. The performance of the
trained network was verified by further experiments. The results of
UAT were compared with the results of conventional turning
experiments carried out with similar machining parameters except for
the vibration amplitude whence considerable reduction was observed
in the built-up edge and the surface roughness.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {3},
	  number    = {4},
	  year      = {2009},
	  pages     = {398 - 403},
	  ee        = {https://publications.waset.org/pdf/3672},
	  url   	= {https://publications.waset.org/vol/28},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 28, 2009},
	}