An artificial-neural-networks-based in-process tool wear prediction system in milling operations

被引：0
|
作者：
Jacob C. Chen
Joseph C. Chen
机构：
[1] Iowa State University,Dept. Industrial Education & Technology
来源：
The International Journal of Advanced Manufacturing Technology | 2005年 / 25卷
关键词：
Artificial neural networks; Cutting force; Dynamometer; Milling ; Tool wear ;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation ANN model. The input variables for the proposed ANN-ITWP system were feed rate, depth of cut from the cutting parameters, and the average peak force in the y-direction collected online using a dynamometer. After the proposed ANN-ITWP system had been established, nine experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm. Experiments have shown that the ANN-ITWP system is able to detect tool wear in 3-insert milling operations online, approaching a real-time basis .
引用
收藏
页码：427 / 434
页数：7
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