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dc.contributor.advisorCheraghi, S. Hosseinen_US
dc.contributor.authorManjunatha, Nikethan Narigudde
dc.date.accessioned2007-12-06T17:28:50Z
dc.date.available2007-12-06T17:28:50Z
dc.date.copyright2007en
dc.date.issued2007-05
dc.identifier.othert07029
dc.identifier.urihttp://hdl.handle.net/10057/1147
dc.descriptionThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineeringen
dc.description.abstractThe drilling of metals produces undesirable projections at the surface of the hole called burrs, which are very costly to remove from the work piece. Any effort involved in simplifying the drilling process that decreases the burr size significantly helps in reducing deburring cost. This study focuses on the burrs formed in drilling of AL6061-T6 at the exit side of the work piece as they are usually larger and have complicated shape and size. Two models are developed using back propagation neural networks to predict burr height and thickness separately as a function of point angle, chisel edge angle and lip clearance angle. The results of this research show that the height and thickness of the burr can be controlled by proper selection of drill bit that consists of suitable geometric parameters. The optimal geometric parameters that yield minimum burr height and thickness are also suggested. Thus, the model assists in identifying suitable drill bit that yields minimum burr height and thickness and as a result helps in reducing deburring cost.en
dc.format.extentix, 92 leaves, ill.en
dc.format.extent1172074 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsCopyright Nikethan Narigudde Manjunatha, 2007. All rights reserved.en
dc.subject.lcshElectronic dissertationsen
dc.titleANN model to predict burr height and thicknessen
dc.typeThesisen


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