System and method including neural net for tool break detection

Claim 1

A system for detecting tool break events while machining a workpiece comprising: means for generating electrical signals representing vibrations at an interface between a tool and said workpiece; analog signal processing means for producing from said signals, unipolar, signal energy-versus-time analog waveforms; digital processor means for a) sampling and digitizing said unipolar output signals, b) screening said sampled and digitized signals for alerting spikes, and c) calculating a plurality of feature vectors for digitized signals at, preceding, and following said alerting spikes, said plurality of feature vectors including mean values, slopes of mean values, standard deviations, and slopes of standard deviations; and a trained neural net for receiving said plurality of feature vectors at an input layer of said trained neural net, said neural net trained iteratively by applying inside data representative of a normal event and outside data representative of an abnormal event, the neural net using an adjustable bias parameter for biasing correct classification of inside and outside data during training and for forming an acceptable class boundary about said inside data and outside data, said trained neural net using said acceptable class boundary to classify said plurality of features as at least one of a break event, a non-break event, and an abnormal event.

Claim 2

A system for detecting tool break events while machining a workpiece comprising: means for generating electrical signals representing vibrations at an interface between a tool and said workpiece; analog signal processing means for producing from said signals, unipolar, signal energy-versus-time analog waveforms; digital processor means for a) sampling and digitizing said unipolar output signals, b) screening said sampled and digitized signals for alerting spikes, and c) calculating a plurality of feature vectors for digitized signals at, preceding, and following said alerting spikes, said plurality of feature vectors including mean values, slopes of mean values, standard deviations, and slopes of standard deviations; and a trained neural net for receiving said plurality of feature vectors at an input layer of said trained neural net, said neural net trained iteratively by applying inside data representative of a normal event and outside data representative of an abnormal event, the neural net using an adjustable bias parameter for biasing correct classification of inside and outside data during training and for forming an acceptable class boundary about said inside data and outside data, said trained neural net using said acceptable class boundary to classify said plurality of features as at least one of a break event, a non-break event, and an abnormal event.

Claim 6

A method for detecting tool break events while machining a workpiece comprising the steps of; generating electrical signals representing vibrations at an interface between a tool and said workpiece; producing from said signals, unipolar, signal energy-versus-time analog waveforms; sampling and digitizing said unipolar output signals; screening said sampled and digitized signals for alerting spikes; calculating a plurality of feature vectors for digitized signals at, preceding, and following said alerting spikes, said plurality of feature vectors including mean values, slopes of mean values, standard deviations, and slopes of standard deviations; inputting said plurality of feature vectors to an input layer of a neural net, said neural net trained iteratively by applying inside data representative of a normal event and outside data representative of an abnormal event, the neural net using an adjustable bias parameter for biasing correct classification of inside and outside data during training and for forming an acceptable class boundary about said inside data and outside data; and using said acceptable class boundary for classifying said plurality of feature vectors as at least one of a break event, a non-break event, and an abnormal event.

Claim 7

A method for detecting tool break events while machining a workpiece comprising the steps of; generating electrical signals representing vibrations at an interface between a tool and said workpiece; producing from said signals, unipolar, signal energy-versus-time analog waveforms; sampling and digitizing said unipolar output signals; screening said sampled and digitized signals for alerting spikes; calculating a plurality of feature vectors for digitized signals at, preceding, and following said alerting spikes, said plurality of feature vectors including mean values, slopes of mean values, standard deviations, and slopes of standard deviations; inputting said plurality of feature vectors to an input layer of a neural net, said neural net trained iteratively by applying inside data representative of a normal event and outside data representative of an abnormal event, the neural net using an adjustable bias parameter for biasing correct classification of inside and outside data during training and for forming an acceptable class boundary about said inside data and outside data; and using said acceptable class boundary for classifying said plurality of feature vectors as at least one of a break event, a non-break event, and an abnormal event.