Predicting drill wear using an artificial neural network

被引：0
|
作者：
A.K. Singh
S.S. Panda
D. Chakraborty
S.K. Pal
机构：
[1] Indian Institute of Technology,Department of Mechanical Engineering
[2] Indian Institute of Technology,Department of Mechanical Engineering
来源：
The International Journal of Advanced Manufacturing Technology | 2006年 / 28卷
关键词：
Artificial neural network; Drilling; Flank wear ;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The present work deals with drill wear monitoring using an artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high-speed steel (HSS) drill bits for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feedrate, spindle speed, and drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data, and has been found to be satisfactory.
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页码：456 / 462
页数：6
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