Real-time online tool wear monitoring method based on wavelet analysis and neural network
A neural network and wavelet analysis technology, applied in manufacturing tools, measuring/indicating equipment, metal processing machinery parts, etc., can solve problems such as inability to characterize local time-frequency characteristics of signals, discrete Fourier transform fence effects, and inability to process fuzzy signals. Achieve fast and efficient detection, shorten learning time, and achieve convenient results
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[0029] Such as figure 1 and figure 2 Shown, the tool wear real-time on-line monitoring method based on wavelet analysis and neural network of the present invention, comprises the following steps:
[0030] Step 1. Detection and transmission of the three-way cutting force: use the three-way cutting force gauge 1, the resistance strain gauge 2 pasted on the surface of the three-way cutting force gauge 1 and the dynamic resistance strain gauge 3 connected to the resistance strain gauge 2 Measure the three-way cutting force in real time, use the data collector 4 to collect the three-way cutting force, and use the network filter 6 to filter out the environmental noise interference signal and convert the three-way cutting force signal F x , F y and F z Transmission to host computer 5;
[0031] In this embodiment, the three-way cutting dynamometer 1 in step 1 is an octagonal ring type three-way cutting dynamometer, the dynamic resistance strain gauge 3 in step 1 is a SDY2101 dyna...
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