Microsoft Word 14 Material Removal

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 2, May-August (2012), © IAEME 
128 
 
 
 
 
 
 
 
 
 
 
MATERIAL
 
REMOVAL
 
RATE
 
PREDICTION
 
OF
 
C-SIC
 
COMPOSITE:
 
COMPARATIVE
 
ANALYSIS
 
OF
 
NEURAL
 
NETWORK
 
AND
 
FUZZY
 
LOGIC
 
 
 
Pallavi.H.Agarwal
1
, Prof.Dr.P.M.George 
2
and Prof.Dr.L.M.Manocha 
3 
1
Research Scholar, Sardar Patel University, Vallabh Vidyanagar, Gujarat 
[e-mail]
2
Professor (Mechanical), B.V.M.Engineering College, Vallabh Vidyanagar, Gujarat 
3
Professor (Material Science), Sardar Patel University, Vallabh Vidyanagar, Gujarat 
 
ABSTRACT 
Material removal rate is an important objective function in manufacturing 
engineering. It holds the characteristic that is can influence the performance of 
mechanical parts, which is proportional to manufacturing cost. MRR (material 
removal rate) is also an aspect for designing mechanical elements. Material removal 
rate is an essential feature of drilling operation since most of the holes applications 
are required for assembly work. The aim of this experimental and analytical research 
is to identify the parameters which enable the prediction of MRR in drilling. Two 
expert systems are used to analyze the best fit model in predicting the MRR for this 
specific drill job on C-SiC composite. The prediction accuracy is then compared to 
analyze which model could give better results so that it can be recommended for 
machine learning and also future work. It is found that BPN-ANN gives better and 
closer values as compared to the Sugeno ANN model.