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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/13065

Title: Prediction and optimization models for turning operations
Authors: Al-Ahmari, A.M.A.
Keywords: Machining economics problem; Neural networks; Response surface methodology
Issue Date: 2008
Publisher: Taylor & Francis
Citation: International Journal of Production Research, 46 (15), pp. 4061-4081
Abstract: Selection of process parameters has very significant impact on product quality, production costs and production times. The quality and cost are much related to tool life, surface roughness and cutting forces which they are functions of process parameters (cutting speed, feed rate, depth of cut and tool nose radius). In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. The process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machineability models. Two data mining techniques are used; response surface methodology and neural networks. The data of 28 experiments have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the selected tool/material combination. The resulting models are utilized to formulate an optimization model and solved to find optimal process parameters, when the objective is minimizing production cost per workpiece, taking into account the related boundaries and limitation of this multi-pass turning operations. Numerical examples are given to demonstrate the suggested optimization models.
URI: http://hdl.handle.net/123456789/13065
ISSN: 00207543
Appears in Collections:College of Engineering

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