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dc.contributor.authorAraya, Bereket-
dc.date.accessioned2014-11-26T08:40:27Z-
dc.date.available2014-11-26T08:40:27Z-
dc.date.issued2006-
dc.identifierM.Techen_US
dc.identifier.urihttp://hdl.handle.net/123456789/11341-
dc.guideMehta, N. K.-
dc.description.abstractJet Electrolytic Drilling (JED) process employs a jet of electrolyte for anodic dissolution of work piece material. It is extensively used for drilling small cooling holes in aircraft turbine blades and for producing mask-less patterns for micro-electronic parts. One of the . most significant problems faced in using JED process is to maintain desired accuracy of the drilled hole and higher velocity of dissolution simultaneously. This dissertation ° -~ reports the experimental findings on the effects of different process parameters,~aplied voltage, stand off distance, electrolyte concentration and pulse on, and off time of current on the velocity of dissolution and radial over-cut. In view of the complex nature of the process, modeling of the process using Artificial Neural Network (ANN) and optimization of the process parameters using Pareto-optimal analysis was done to establish a conclusive analytical model and show optimal operating points. A feed-forward back propagation neural network (FFBPNN) based on Taguchi experimental design is developed to model the machining process. Validation of the proposed models has been done with the experimental results and a good correlation between predicted values from proposed model and experimental results has been observed. Pareto optimal analysis is implemented to find the optimal working conditions so that higher velocity of dissolution can be achieved for a given radial over-cut.en_US
dc.language.isoenen_US
dc.subjectMECHANICAL INDUSTRIAL ENGINEERINGen_US
dc.subjectJET ELECTROLYTIC DRILLING PROCESSen_US
dc.subjectARTIFICIAL NEURAL NETWORKen_US
dc.subjectFFBPNNen_US
dc.titleMODELING AND PARAMETRIC OPTIMIZATION OF JET ELECTROLYTIC DRILLING (JED) PROCESSen_US
dc.typeM.Tech Dessertationen_US
dc.accession.numberG12865en_US
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