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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/13068

Title: Predictive machinability models for a selected hard material in turning operations(2007)
Authors: Al-Ahmari, A.M.A.
Keywords: Machinability models; Neural networks; Response surface methodology
Issue Date: 2007
Publisher: Elsevier B.V.
Citation: Journal of Materials Processing Technology, 190 (1-3), pp. 305-311.
Abstract: In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. Process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machinability models. Two important data mining techniques are used; they are response surface methodology and neural networks. Data of 28 experiments when turning austenitic AISI 302 have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the considered material.
URI: http://hdl.handle.net/123456789/13068
ISSN: 09240136
Appears in Collections:College of Engineering

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