Drill wear monitoring using back propagation neural network

被引：67

作者：

Panda, SS

Singh, AK

Chakraborty, D ^{[1
]}

Pal, SK

机构：

[1] Indian Inst Technol, Dept Engn Mech, Gauhati 781039, India

[2] Indian Inst Technol, Dept Mech Engn, Kharagpur 721302, W Bengal, India

来源：

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY | 2006年 / 172卷 / 02期

关键词：

flank wear; artificial neural network; drilling; chip thickness;

D O I：

10.1016/j.jmatprotec.2005.10.021

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Present work deals with prediction of flank wear of drill bit using back propagation neural network (BPNN). Drilling operations have been performed in mild steel work-piece by high-speed steel (HSS) drill bits over a wide range of cutting conditions. Important process parameters have been used as input for BPNN and drill wear has been used as output of the network. Inclusion of chip thickness as an input in addition to conventional parameters leads to better training of the network. Performance of the neural network has been found to be satisfactory while validated with experimental result. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：283 / 290

页数：8

共 14 条

[1] Drilling wear detection and classification using vibration signals and artificial neural network [J].