A thermal displacement compensator measures a temperature of an environment in which a machine is installed and a temperature of each part of the machine, and calculates a temperature difference between at least two temperatures among measured temperatures. Furthermore, the thermal displacement amount of the machine is acquired. Then, based on teacher data using the measured temperatures and the calculated temperature difference as input data and using the acquired thermal displacement amount as output data, a thermal displacement compensation model that estimates the output data from the input data is created by machine learning.

A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, includes a state observation unit, and a learning unit. The state observation unit observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool, and the learning unit learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

A method for detecting defects in working CNC tools in real time, implemented in an electronic device, includes acquiring sounds of operation of a tool during a cutting or other operation process and dividing the acquired cutting sounds into a plurality of recordings of audio according to a preset time interval. Time-frequency features of the plurality of recordings of audio are acquired according to multiple feature transformation methods and a fusion feature image of the cutting sound is formed according to the extracted time-frequency features. A tool detection model is generated by training the fusion feature image, and any defects of the tool and any defect types the tool has are detected according to the tool detection model.

Provided is a thermal displacement correction method using a machine learning method but making it possible to, on a user side, calculate a thermal displacement amount appropriate to a machine tool of the user and correct the thermal displacement. In a machine tool on a target user side, a thermal displacement amount between workpiece and tool corresponding to a temperature at a preset measurement point is calculated based on a parameter defining a relation between the temperature and the thermal displacement amount, and a positioning position for workpiece and tool is corrected in accordance with the calculated thermal displacement amount. On a manufacturer side, operational status information of the machine tool on the target user side is obtained, an operational status identical to the obtained operational status on the target user side is reproduced with a machine tool of a same type as the machine tool on the target user side based on the obtained operational status information, a temperature at a measurement point identical to the measurement point on the machine tool on the target user side and a thermal displacement amount between workpiece and tool are measured during reproduction, and the parameter is calculated by machine learning based on the measured temperature and thermal displacement amount. The parameter in the machine tool on the target user side is updated with the calculated parameter.

Provided is a thermal displacement correction method using a machine learning method but making it possible to, on a user side, calculate a thermal displacement amount appropriate to a machine tool of the user and correct the thermal displacement. In a machine tool on a target user side, a thermal displacement amount between workpiece and tool corresponding to a temperature at a preset measurement point is calculated based on a parameter defining a relation between the temperature and the thermal displacement amount, and a positioning position for workpiece and tool is corrected in accordance with the calculated thermal displacement amount. On a manufacturer side, operational status information of the machine tool on the target user side is obtained, an operational status identical to the obtained operational status on the target user side is reproduced with a machine tool of a same type as the machine tool on the target user side based on the obtained operational status information, a temperature at a measurement point identical to the measurement point on the machine tool on the target user side and a thermal displacement amount between workpiece and tool are measured during reproduction, and the parameter is calculated by machine learning based on the measured temperature and thermal displacement amount. The parameter in the machine tool on the target user side is updated with the calculated parameter.

A method for monitoring a machining process in which tooth flanks of pre-toothed workpieces (23) are machined with a finishing machine (1) is disclosed. As part of the method, a plurality of measurement values are recorded while a finishing tool (16) is in machining engagement with a workpiece. Among them are values of a power indicator which indicates a current power consumption of the tool spindle during the machining of the tooth flanks of the workpiece. A normalization operation is applied to at least some of the measurement values or to values of a quantity derived from the measurement values in order to obtain normalized values. The normalization operation depends on at least one of the following parameters: geometrical parameters of the finishing tool, in particular its outside diameter, geometrical parameters of the workpiece and setting parameters of the finishing machine, in particular radial infeed and axial feed.

A numerical control system detects a state amount indicating a state of an injection operation of an injection molding machine, generates a characteristic amount that characterizes the state of the injection operation from the state amount, and infers an evaluation value of the state of the injection operation from the characteristic amount. The numerical control system detects an abnormal state on the basis of the evaluation value, generates or updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the injection operation.

A numerical control system detects a state amount indicating an operation state of a machine tool, creates a characteristic amount that characterizes the state of a machining operation from the detected state amount, infers an evaluation value of the operation state of the machine tool from the characteristic amount, and detects an abnormality in the operation state of the machine tool on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.

A thermal displacement compensator measures a temperature of an environment in which a machine is installed and a temperature of each part of the machine, and calculates a temperature difference between at least two temperatures among measured temperatures. Furthermore, the thermal displacement amount of the machine is acquired. Then, based on teacher data using the measured temperatures and the calculated temperature difference as input data and using the acquired thermal displacement amount as output data, a thermal displacement compensation model that estimates the output data from the input data is created by machine learning.

A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation from the detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.