Selection of dry drilling parameters for minimal burr size and desired drilling quality

Abstract

The drilling of metals produces undesired raised material which is defined as burr. It is important to minimize the burr size by modifying the drill geometry or selection of drilling parameters. Although, selection of optimal drilling parameters can be minimize the burr size, but it may be increases the overcut or decreases the material removal rate (MRR). In this paper, drilling parameters have been selected for minimal burr size and desired overcut and MRR. Four adaptive neuro fuzzy inference system (ANFIS) models have been designed based on experimental observation in drilling of copper. Outputs of ANFIS models are burr height, burr thickness, burr type and overcut of hole; While input parameters of drilling process are cutting speed, tool diameter and ratio of feed rate to diameter. Then the particle swarm optimization method has been used to select the optimum condition of input parameters to minimize the burr size in desired value of overcut and MRR. Results showed that the proposed models can be predict the outputs well and they can be used as adequate predictors and optimizer for achieving the drilling parameters which gives a type of burr with low value of burr height and burr thickness with desirable overcut and MRR.

Keywords

Drilling burr adaptive neuro fuzzy inference system particle swarm optimization

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