Response to Sha’s comment on article titled ‘Investigative study on machinability aspects of unreinforced and reinforced PEEK composite machining using ANN model’
Restricted accessOtherFirst published online April, 2011
Response to Sha’s comment on article titled ‘Investigative study on machinability aspects of unreinforced and reinforced PEEK composite machining using ANN model’
This article adresses the issue of artificial neural network (ANN) training by limited number of samples as raised by Dr. Sha to predict the machinability aspects of unreinforced and reinforced PEEK composite machining. In view of this, the importance of ANN training with the samples obtained by performing the experiments as per factorial design of experiments with orthogonality property to capture the input-output relationship over an experimental region has been emphasized.
Karnik SR, Gaitonde VN, Mata F. and Davim JPInvestigative study on machinability aspects of unreinforced and reinforced PEEK composite machining using ANN model. J Reinf Plast Compos2008; 27: 751-768.
2.
Gaitonde VN, Karnik SR, Mata F. and Davim JPModeling and analysis of machinability characteristics in PA6 and PA66 GF30 polyamides through artificial neural network. J Thermoplast Compos Mater2010; 23: 313-336.
3.
Himmel C. and May G.Advantages of plasma etch modeling using neural networks over statistical techniques. IEEE Trans Semicond Manuf1993 ; 6(2): 103-111.
4.
Schalkoff RBArtificial neural networks. SingaporeMcGraw Hill, 1997.
