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Cutting tool monitoring is a key technology for automatic, unmanned and adaptive machining. It's vital to choose right monitoring and recognition methods. Cutting force and vibration are good manners for tool wear monitoring. This paper puts forward techniques of applying frequency band energy decomposition using wavelet packets to extract signal features. And aiming at shortcomings of using single...
Cutting tool monitoring is a key technology for automatic, unmanned and adaptive machining. It's vital to choose right feature extracting and recognition methods. By using cutting vibration monitoring and diagnostics technique to monitor tool wear states, this paper puts forward techniques of applying frequency-band energy decomposition using wavelet packets to extract signal features of cutting vibration...
The influence of experimental design on modeling of tool condition monitoring system based on different neural networks was investigated. The orthogonal experiments and complete parameter combination experiments were carried out on a Vertical Machining Centre, and BPNN and CSGFFNN were adopted to model the mapping relations between tool condition and features extracted from different sensor signals...
In manufacturing processes it is very important that the condition of the cutting tool, particularly the indications when it should be changed, can be monitored. Cutting tool condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. The multi-sensor signals reflect the tool condition comprehensively...
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