A cutting insert for cutting, milling or drilling of metal includes a sensor for detecting a predetermined wear of the cutting insert caused by operation thereof on a metal work piece, wherein the sensor includes at least two contact regions through which the sensor is connectable to external measuring circuitry. The sensor has at least two leads, which are connected to a respective of the at least two contact regions, wherein each lead presents a respective free end positioned such that, upon the predetermined wear caused by the operation of the cutting insert on a metal work piece, the free ends will be connected to each other by the metal work piece or by a chip resulting from the operation of the cutting insert on the metal work piece.

A method of controlling a machine tool comprising the steps of: providing a data memory containing, for at least one specific working axis of the plurality of working axes, a plurality of data tuples each assigning a load value to a position value relative to the specific working axis, selecting a first data tuple and a second data tuple whose load values are different, and generating at least one control command for the machine tool, wherein the control command is adapted to perform a first tolerance measurement at a first position value and a second tolerance measurement at a second position value, and wherein, based on a first result of the first tolerance measurement and a second result of the second tolerance measurement, information about wear of a part of the machine tool is output. A corresponding device is also be disclosed.

Provided is an information processing method capable of effectively utilizing a variety of types of information collected from a machine tool. The information processing method includes the step of communicating with a plurality of machine tools (S10). The plurality of machine tools each include a sensor to sense information about the machine tool as sensed information. The step of communicating (S10) includes the step of receiving as collected data from each of the plurality of machine tools part information about a part of the machine tool and the sensed information obtained by the sensor. The information processing method further includes the steps of storing in a storage unit the collected data received from each of the plurality of machine tools (S20A, S20B); and based on the plurality of collected data stored in the storage unit, learning a correlation between part information of a machine tool and sensed information obtained by the sensor internal to that machine tool (S32).

In order to monitor replacement time for a consumable part of a machine tool, the present invention defines a time period after the power of the machine tool is turned off until the next turn-on thereof as a pause time for the consumable part, and provides a notification that the replacement time for the consumable part has come when the pause time is not less than a predetermined threshold.

An abnormality-detecting device for detecting abnormalities of a tool of a machine tool comprises: an acquiring unit for acquiring multiple measured values relating to the tool as measurement data (vibration information, cutting force information, sound information, main shaft load, motor current, power value); a normal model unit for learning the measurement data acquired during normal machining by one class machine learning and creating a normal model; an abnormality-diagnosing unit for acquiring measurement data during machining after creation of the normal model while diagnosing whether said measurement data is normal or abnormal on the basis of the normal model; and a re-diagnosing unit for re-diagnosing measurement data, which has been diagnosed to be abnormal by the abnormality-diagnosing unit, by a method different from the abnormality-diagnosing unit.

The cutting tool inspection apparatus includes a data collection unit, a training data generation unit, and an inspection data generation unit. The data collection unit acquires waveform data of current for one batch supplied to a motor that drives a cutting tool of a machine tool. During generation of training data to be compared with inspection data of the cutting tool, the training data generation unit infers a time range corresponding to a specific process of the machine tool, based on characteristics of the waveform data, and clips waveform data of the time range as the training data. The inspection data generation unit clips, as the inspection data, waveform data at a time position identical to a time position of the time range inferred by the training data generation unit, from the waveform data acquired by the data collection unit during inspection of the cutting tool.

A system and method for monitoring and predicting wear of a cutting tool used for machining a workpiece is disclosed. The system includes a cutting tool having a shank and a cutting head. The system also includes a split, modular and wireless wear detection system including one or more sensors mounted to the cutting tool for providing a data signal representative of a physical condition of the system, and a data recording and data transmitting device for recording the data signal from the one or more sensors and for generating and transmitting a data signal to a processor. The processor applies a machine learning data processing technique in real time to monitor and/or predict a condition of various components and/or parameters of the system during a metal cutting operation.

Provided is an information processing method capable of effectively utilizing a variety of types of information collected from a machine tool. The information processing method includes the step of communicating with a plurality of machine tools (S10). The plurality of machine tools each include a sensor to sense information about the machine tool as sensed information. The step of communicating (S10) includes the step of receiving as collected data from each of the plurality of machine tools part information about a part of the machine tool and the sensed information obtained by the sensor. The information processing method further includes the steps of storing in a storage unit the collected data received from each of the plurality of machine tools (S20A, S20B); and based on the plurality of collected data stored in the storage unit, learning a correlation between part information of a machine tool and sensed information obtained by the sensor internal to that machine tool (S32).

A processing system includes a cutting tool, a sensor attached to the cutting tool, an analog-to-digital (AD) converter configured to perform sampling on an analog signal output from the sensor to generate a digital signal, and a processing unit. The processing unit is configured to, based on a rotation rate [rpm] of a rotor; an upper-limit period that is an upper-limit value of a process period; and an upper-limit pitch that is an upper-limit value of an angle pitch [degree], determine the process period and a sampling frequency with which the AD converter performs sampling such that a set value of the angle pitch is equal to the upper-limit pitch or less in a time period taken by the rotor to rotate N times with the process period that is equal to the upper-limit period or shorter.

A smart cutting tool system for use in precision cutting, comprising a cutting insert (1), an upper cutter arbor (2), a lower cutter arbor (3), a first pressure sensor (4), a second pressure sensor (5), a signal processing module (6), a Bluetooth transmission module (7), and a power supply (8), wherein the cutting insert (1) is fixed to a front end of the upper cutter arbor (2), the cutting insert (1) is provided at its rear end with a microgroove, in which the first pressure sensor (4) and the second pressure sensor (5) are inserted. The cutting tool system solves the problem of mutual coupling of various cutting forces, and has higher sensitivity.