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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1071

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contributor.authorPanda, S S-
contributor.authorMahapatra, S S-
date.accessioned2009-11-19T05:38:06Z-
date.available2009-11-19T05:38:06Z-
date.issued2009-
identifier.citationProceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009 , Article number 5234475, Pages 85-89en
identifier.urihttp://dx.doi.org/ 10.1109/ICCSIT.2009.5234475-
identifier.urihttp://hdl.handle.net/2080/1071-
description.abstractThe present paper describes use of principal components for drill wear prediction. It also makes a comparative analysis in using large sensor based technique in predicting drill wear. In order to reduce the redundancy of the network, principal component has been fused with artificial neural network (ANN) for prediction of drill wear. Large numbers of experiments have been conducted and sensor signals have been acquired using data acquisition system. Cutting force, torque, vibrations along with other process parameters such as spindle speed, feed rate, drill diameter, chip thickness and surface roughness have been used as indicative parameters for characterizing the progressive wear of drill. Principal component of these input parameters has been derived thereafter and has been used to predict the flank wear using BPNN.en
format.extent543322 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectBPNN;en
subjectDesign of experiment;en
subjectFlank wear;en
subjectNeuron;en
subjectPCA;en
subjectSensor integration;en
titlePCA Fused NN Approach for Drill Wear Prediction in Drilling Mild Steel Specimenen
typeArticleen
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