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Sensor Fusion and Neural Network Analysis for Drill-Wear Monitoring  

Prasopchaichana, Kritsada (인하대학교 대학원 기계공학과)
Kwon, Oh-Yang (인하대학교 기계공학부)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.17, no.1, 2008 , pp. 77-85 More about this Journal
Abstract
The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.
Keywords
Neural network; Levenberg-Marquardt; sensor fusion; drill wear; tool-condition monitoring;
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Times Cited By KSCI : 1  (Citation Analysis)
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