RESEARCH ARTICLE
Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks
Nikolaos M. Vaxevanidis*, 1, John D. Kechagias2, Nikolaos A. Fountas1, Dimitrios E. Manolakos3
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 389
Last Page: 399
Publisher ID: TOBCTJ-8-389
DOI: 10.2174/1874836801408010389
Article History:
Received Date: 17/11/2014Revision Received Date: 26/12/2014
Acceptance Date: 29/12/2014
Electronic publication date: 31/12/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
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.