Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing

Meenu GUPTA, Surinder Kumar GILL

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PDF(174 KB)
Front. Mech. Eng. ›› DOI: 10.1007/s11465-012-0343-2
RESEARCH ARTICLE
RESEARCH ARTICLE

Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing

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Abstract

Glass fiber reinforced plastics (GFRPs) composite is considered to be an alternative to heavy exortic materials. According to the need for accurate machining of composites has increased enormously. During machining, the obtaining cutting force is an important aspect. The present investigation deals with the study and development of a cutting force prediction model for the machining of unidirectional glass fiber reinforced plastics (UD-GFRP) composite using regression modeling and optimization by simulated annealing. The process parameters considered include cutting speed, feed rate and depth of cut. The predicted values radial cutting force model is compared with the experimental values. The results of prediction are quite close with the experimental values. The influences of different parameters in machining of UD-GFRP composite have been analyzed.

Keywords

UD-GFRP / ANOVA / radial cutting force / PCD tool / Taguchi method / regression analysis / simulated annealing / multi objective techniques

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Meenu GUPTA, Surinder Kumar GILL. Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing. Front Mech Eng, https://doi.org/10.1007/s11465-012-0343-2

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Acknowledgements

The authors were indebted to Maharashtra Engineering Industry, Satara Maharashtra (P) Ltd. for supplying the UD-GFRP rods used in this work.
Notations
b0, b1, b2, b3
a, b, c
X0, X1, X2, X 3
Fr
A
B
C
K
T0
SA
η
y
ϵ
Ŷ
Estimates of parameters
Exponentially determined constant
Logarithmic transformations of machining parameters
Radial force/kg
Cutting speed/rpm
Feed rate/(mm·rev-1)
Depth of cut/mm
Constant
Temperature
Simulated annealing
Cutting force response
Measured cutting force
Experimental error
Estimated response based on first order model/kg

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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