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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/9983

Title: Modeling and optimization of machining process in discontinuously reinforced aluminium matrix composites
Authors: PENDSE, DM
JOSHI, SS
Keywords: neural-networks
wear
alloy
art
Issue Date: 2004
Publisher: MARCEL DEKKER INC
Citation: MACHINING SCIENCE AND TECHNOLOGY, 8(1), 85-102
Abstract: This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.
URI: http://dx.doi.org/10.1081/MST-120034242
http://dspace.library.iitb.ac.in/xmlui/handle/10054/9983
http://hdl.handle.net/10054/9983
ISSN: 1091-0344
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