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Investigations on Drilling of Multimaterial and Analysis by ANN

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  • NETL
  • NSTL
  • Trans Tech Publications Ltd
This paper presents experimental and analytical investigation on drilling of carbon fibre reinforced plastic and aluminium stacks. The experimental results conducted as per full factorial experimental design reveal that drill diameter and feed rate have significant effects in reducing thrust force and torque while spindle speed has the least effect. The analytical study is based on artificial neural network (ANN) training using feed-forward back propagation network. The correlations obtained by multi-variable regression analysis and ANN, indicate that ANN is more effective than regression analysis.

drillingCFRP/aluminium stackthrust forcetorqueneural networkregression

Vijayan Krishnaraj、Redouane Zitoune、Francis Collombet

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Department of Mechanical Engineering, PSG College of Technology, 641004, India Institute of Clement Ader, IUT-University of Paul Sabatier, Toulouse, 31077, France

Institute of Clement Ader, IUT-University of Paul Sabatier, Toulouse, 31077, France

2010

10.4028/www.scientific.net/KEM.443.347

Key engineering materials

Key engineering materials

ISSN：1013-9826
年,卷(期)：2010.443
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