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 machine learning device acquires, as a label, a command output by a servo control apparatus to a control target device so as to drive and control the control target device. The machine learning device acquires, as input data, an output of the control target device driven based on the command, and constructs a learning model relating to feedforward control for correcting the command, by performing supervised learning by use of a set of the label and the input data serving as teaching data.

A non-transitory computer readable information recording medium stores an evaluation program for operating first and second motor control units, for evaluating operation characteristics related to a control device including a first motor control unit configured to control a first motor driving a first axis, and a second motor control unit configured to control a second motor driving a second axis. The evaluation program operates the first and second motor control units so that a shape of a movement path of a control target moved by the first and second axes includes at least a cornered shape in which both rotation directions of the first and second motors do not invert, and an arc shape in which one of the first and second motors rotates in one direction, and a rotation direction of the other of the first and second motors inverts.

A non-transitory computer readable information recording medium stores an evaluation program for operating first and second motor control units, for evaluating operation characteristics related to a control device including a first motor control unit configured to control a first motor driving a first axis, and a second motor control unit configured to control a second motor driving a second axis. The evaluation program operates the first and second motor control units so that a shape of a movement path of a control target moved by the first and second axes includes at least a cornered shape in which both rotation directions of the first and second motors do not invert, and an arc shape in which one of the first and second motors rotates in one direction, and a rotation direction of the other of the first and second motors inverts.

To easily perform adjustment relating to feedforward control and also to improve command follow-up performance. A machine learning device includes label acquisition means for acquiring, as a label, a command output by a servo control apparatus to a control target device so as to drive and control the control target device, input data acquisition means for acquiring, as input data, an output of the control target device driven based on the command, and learning means for building a learning model relating to feedforward control for correcting the command, by performing supervised learning by use of a set of the label and the input data serving as teacher data.