Title: Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

Authors: J.V. Abellan-Nebot

Addresses: Department of Industrial Systems Engineering and Design, School of Technology and Experimental Sciences, Universitat Jaume I, 12071 Castellon, Spain

Abstract: In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user|s experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The proposed methodology increases the machining performance in 6.1% and improves the understanding of the machining operation through the use of Adaptive Neuro-fuzzy Inference Systems.

Keywords: machining; parameter optimisation; ANFIS; adaptive neuro-fuzzy inference systems; soft computing; GAs; genetic algorithms; multi-objective functions; expert machinists; desirability functions; cutting parameters; milling.

DOI: 10.1504/IJMMS.2010.029866

International Journal of Mechatronics and Manufacturing Systems, 2010 Vol.3 No.1/2, pp.3 - 24

Available online: 02 Dec 2009 *

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