Fuzzy estimation of feed-cutting force from current measurement - A case study on intelligent tool wear condition monitoring

被引:52
作者
Li, XL [1 ]
Li, HX
Guan, XP
Du, R
机构
[1] Yanshan Univ, Inst Elect Engn, Qinghuangdao 066004, Peoples R China
[2] City Univ Hong Kong, Dept Mfg Engn, Hong Kong, Hong Kong, Peoples R China
[3] City Univ Hong Kong, Dept Automat & Comp Aided Engn, Hong Kong, Hong Kong, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2004年 / 34卷 / 04期
基金
中国国家自然科学基金;
关键词
feed-cutting force; feed-motor current; fuzzy classification; monitoring; neuro-fuzzy network; tool wear;
D O I
10.1109/TSMCC.2004.829296
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neuro-fuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring applications.
引用
收藏
页码:506 / 512
页数:7
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