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Estimation of Drilling Burr Formation with Artificial Neural Network Analysis
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In drilling, the unwanted material adhered just beyond the hole produced in a workpiece material is known as a burr. In any conventional manufacturing process like drilling, milling, etc., machining burr is produced. There can be usually no conventional machining process which does not form burr. Presence of burr on the workpiece material leads to increasing production time as well as manufacturing cost. Minimization of burr height and thickness by changing machining process parameters and environmental condition yields decreasing production cost. The present work deals with prediction of burr height and burr thickness in the drilling process. An investigation has been performed by changing different process parameters like feed and cutting environment with respect to different drill diameters. From the experimental observation made by different sets of experiments with varying process parameters, minimum burr height and thickness are tried to find out. It is observed that using the back up support of the work material, burr height and thickness could be reduced remarkably. An Artificial Neural Network (ANN) model is developed using the experimental results. The neural network model estimates show close matching with the experimentally obtained results.
Keywords
Machining, Drilling, Burr, Estimation, Artificial Neural Network, NN, ANN, Modeling.
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