Data fusion neural network for tool condition monitoring in CNC milling machining

被引:76
作者
Chen, SL [1 ]
Jen, YW [1 ]
机构
[1] Natl Cheng Kung Univ, Inst Mfg Engn, Tainan 701, Taiwan
关键词
tool wear; data fusion; neural networks; feature elements; milling;
D O I
10.1016/S0890-6955(99)00066-8
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Several data fusion methods are addressed in this research to integrate the detected data for the neural network applications of on-line monitoring: of the tool condition in CNC milling machining. One dynamometer and one accelerometer were used in the experiments. The collected signals were pre-processed to extract the feature elements for the purpose of effectively monitoring the tool wear condition. Different data fusion methods were adopted to integrate the obtained feature elements before they were applied into the learning procedure of the neural networks. The training-efficiency and test-performance of the data fusion methods were then analyzed. The convergence speed and the test error were recorded and used to represent the training efficiency and test performance of the different data fusion methods. From an analysis of the results of the calculations based on the experimental data, it was found that the performance of the monitoring system could be significantly improved with suitable selection of the data fusion method. (C) 1999 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:381 / 400
页数:20
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