An environmental temperature change prediction device includes an environmental temperature acquisition unit, an outside temperature acquisition unit, a plant environment pattern setting unit, a prediction model generating unit, and an environmental temperature change prediction unit. The environmental temperature acquisition unit measures a machine body temperature. The plant environment pattern setting unit defines in advance a classification rule for classifying change trends of the environmental temperature into a plurality of patterns based on data of the environmental temperature and the plant outside temperature and environmental temperature prediction models. The prediction model generating unit selects the applicable plant environment pattern and determines a parameter of the environmental temperature prediction model corresponding to the selected plant environment pattern. The environmental temperature change prediction unit predicts a change in the environmental temperature in a future by the environmental temperature prediction model generated in the prediction model generating unit.

A thermal displacement compensation system detects a state quantity indicating a state of a machine, infers a thermal displacement compensation amount of the machine from the detected state quantity, and performs a thermal displacement compensation of the machine based on the inferred thermal displacement compensation amount of the machine. The thermal displacement compensation system generates a learning model by machine learning that uses a feature quantity, and stores the generated learning model in association with a combination of specified conditions of individual difference of the machine.

A machine learning device includes a model data selection unit to select, under a change in ambient temperature of a manufacturing machine, a learned model for additional learning of a thermal displacement compensation amount in each axis included in the manufacturing machine with respect to an operation state of the manufacturing machine, and a learned model storage unit to associate and store a pattern of an ambient temperature change curve indicating a transition of a change in the ambient temperature of the manufacturing machine and the learned model that is learned under the change in the ambient temperature. Based on the ambient temperature change curve stored in the learned model storage unit, the model data selection unit selects a learned model suitable for the additional learning of the thermal displacement compensation amount in each axis included in the manufacturing machine with respect to the operation state of the manufacturing machine.

A direct pose feedback (DPF) control method applied to a DPF controlled machine is provided. The DPF control method includes a pose compensation control in addition to the position feedback control. The pose compensation control includes an initiation step, a reference system step, an actual pose calculation step and a position compensation step. The sum of the primary driving value and the compensation driving value is output to the driver of each of the motors. The advantage of the DPF control method is that the existing real-time position control loop in the controller can remain unchanged, while the pose compensation control is added to eliminate tool pose error resulted from geometric errors in the machine. The DPF controlled machine uses a pose measuring mechanism to measure the actual pose of the tool and to compensate the tool pose error. Hence, the DPF controlled machine is free of geometric errors.

A thermal displacement correction system performs thermal displacement correction in cooperation with a thermal displacement correction device and a computer connected via a network. The thermal displacement correction system that corrects thermal displacement caused by processing performed by a machine comprises: the thermal displacement correction device connected to the machine; and the computer connected to the thermal displacement correction device via the network. The computer comprises: a data acquisition unit that acquires environmental data on an external environment of the machine via the network; a correction value inference unit that calculates a correction value using the environmental data; and a correction value output unit that outputs the correction value to the network. The thermal displacement correction device comprises: a correction value acquisition unit that acquires the correction value via the network; and a correction execution unit that performs thermal displacement correction using the correction value.

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 processing apparatus includes a processing unit, a control unit, and a temperature detecting unit. The processing unit includes a cutting blade and a spindle assembly, the spindle assembly including a spindle having the cutting blade mounted on a distal end of the spindle and a spindle housing through which the spindle is inserted. A cooling fluid passage is formed in the spindle housing, the cooling fluid passage cooling the spindle assembly, and having one end connected to a cooling fluid supply source and having another end communicating with a cooling fluid outlet of the spindle housing. The temperature detecting unit detects the temperature of the spindle housing. The control unit determines whether a state of cooling of the spindle assembly is normal or abnormal on the basis of the temperature detected by the temperature detecting unit.

A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

An adjustment necessity determination device is an adjustment necessity determination device that makes a determination, after a workpiece is machined, about a necessity to make an adjustment of a deviation of the axis position of each axis of a machine tool that has performed the machining and includes: a data acquisition unit that acquires a physical quantity relating to a cause of a deviation of the axis position of each axis of the machine tool, the physical quantity observed at the time of the machining; a time-series data storage unit that stores the physical quantity as time-series data; and an adjustment necessity judgement unit that makes a judgment about a necessity to make an adjustment of a deviation of the axis position of each axis of the machine tool based on the time-series data.

A warm-up method for a machine system including a machine component and a machine sensor configured to sense temperature of the machine component is provided. The method comprises steps of; A) activating the machine component to execute a warm-up operation for warming up the machine component; B) determining whether the machine component is warmed up based on a target temperature corresponding to the machine component and a temperature of the machine component that is currently sensed by the machine sensor; and C) when it is determined that the machine component is warmed up, making the machine component not execute the warm-up operation.