Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks
Nikolaos M. Vaxevanidis
John D. Kechagias
Nikolaos A. Fountas
Dimitrios E. Manolakos
|Published in:||The Open Construction and Building Technology Journal, December 2014, n. 1, v. 8|
The present paper investigates the influence of main cutting parameters on the machinability during turning process for three typical materials namely AISI D6 tool steel, Ti6Al4V ELI and CuZn39Pb3 brass, all three under dry cutting environment. Spindle speed, feed rate and depth of cut were selected for study whilst arithmetic surface roughness average (Ra) and main cutting force component (FC) were treated as quality objectives characterizing machinability. For the aforementioned materials a full factorial design of experiments was conducted to exploit main effects and interactions among parameters it terms of quality objectives. The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding all three materials. The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization problems.
|Copyright:||© 2014 Nikolaos M. Vaxevanidis, John D. Kechagias, Nikolaos A. Fountas, Dimitrios E. Manolakos|
This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.
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