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Content Provider | IEEE Xplore Digital Library |
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Author | Kandilli, I. Sonmez, M. Ertunc, H.M. Cakir, B. |
Copyright Year | 2007 |
Description | Author affiliation: Univ. of Kocaeli, Kocaeli (Kandilli, I.) |
Abstract | In manufacturing systems the detection of tool wear during cutting process is one of the most important considerations. In order to perform online tool condition monitoring (TCM) for different cutting conditions, a sensor-integration strategy with machining parameters is proposed. TCM systems are most frequently based on the research which attempts to correlate the condition of drilling and milling tools to the signals obtained from multiple sensors (namely, cutting forces, vibration, current and sound connected to a CNC machine). The aim of the proposed study is to create a TCM system that will lead to a more efficient and economical machining tool usage. The used system is capable of accurate tool wear monitoring in around 97% accuracy. Experimental results under different conditions have demonstrated that TCM can be implemented by using neural network. |
Starting Page | 1388 |
Ending Page | 1394 |
File Size | 247228 |
Page Count | 7 |
File Format | |
ISBN | 9781424408276 |
DOI | 10.1109/ICMA.2007.4303752 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-08-05 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Drilling Milling Neural networks Machining Artificial neural networks Condition monitoring Acoustic sensors Force measurement Sensor systems Production Artificial neural network Tool wear Tool condition monitoring |
Content Type | Text |
Resource Type | Article |
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