Surface Roughness Modeling and Prediction by ANN when Drilling Udimet 720

Abstract:

Article deals with design of artificial neural network (ANN) for prediction of the surface roughness as one of the important indicators of machined surface quality. Back propagation neural network was trained and tested for prediction of the machined surface roughness. Cutting conditions, selected monitoring indices and tool wear parameter were given as inputs to the ANN. Test sample was nickel based super alloy Udimet 720, which is used as material for highly stressed jet engine components. Experimental data collected from tests were used as input into ANN to identify the sensitivity among cutting conditions, monitoring indices and progressive tool wear and machined surface roughness.

• Periodical: Key Engineering Materials (Volume 581)
• Pages: 366-371
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.581.366
• Online since: October 2013
• Authors: Marek Vrabel', Ildikó Maňková, Jozef Beňo
• Keywords: Artificial Neural Network (ANN), Drilling, Nickel Based Alloy, Surface Roughness (SR)

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