General Electric Company

Edward N Diei

Jerry H Carmichael

Steven Robert Hayashi

David W Tong

Kenneth M Martin

A system and method for monitoring vibrations of a cutting tool uses a neural network for classifying signal features as break or non-break or, in another embodiment, as non-break or abnormal. A vibration signal is produced by an accelerometer, positioned to sense vibrations at the tool-workpiece interface. The signal is pre-processed to extract low frequency machining noise and detect the energy in a higher frequency band. The signal is then sampled and segments of the digitized signals are processed by digital logic into feature vectors for input to a trained neural net having two output nodes for classification. The use of a neural net provides performance improvement and economies over previously known heuristic methods of signal analysis.

System and method including neural net for tool break detection

Breaking of a cutter insert in a multiple insert metal cutter is detected by selecting a periodic vibration generated electrical signal having components which track individual insert contribution. These components are filtered to provide RMS values whose predetermined ratio values indicate insert breakage.

Metal cutting tool failure event sensor

A composite low frequency high frequency electrical signal from a metal cutting tool in a metal cutting operation is processed through separate high frequency and low frequency systems and the resulting signals digitized and combined for analysis of a tool break event.

Metal cutting machine tool break detection

In a metal cutting tool wear indicator system, a ratio of an estimated ultrasonic emission signal from a metal cutting process to a measured ultrasonic emission signal, e.g. UE/UE is utilized in place of a prior ratio of horsepower to ultrasonic emission, e.g. HP/UE, together with a continuous revision of the estimator during the cutting process.

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Cincinnati, OH, United States of America

Edward N Diei