Fanuc Corporation

Mitsunori Watanabe

Kazuomi Maeda

Keisuke Watanabe

Takenori Ono

Takayuki Tamai

A machine learning device includes: a measured data acquisition unit that acquires a measured data group; a thermal displacement acquisition unit that acquires a thermal displacement actual measured value about a machine element; a storage unit that uses the measured data group acquired by the measured data acquisition unit as input data, uses the thermal displacement actual measured value about the machine element acquired by the thermal displacement acquisition unit as a label, and stores the input data and the label in association with each other as teaching data; and a calculation formula learning unit that performs machine learning based on the measured data group and the thermal displacement actual measured value about the machine element, thereby setting a thermal displacement estimation calculation formula used for calculating the thermal displacement of the machine element based on the measured data group.

Machine learning device and thermal displacement compensation device

An inspection apparatus includes a group data creation unit configured to create group data obtained by dividing a plurality of samples into a plurality of groups, a statistical state calculation unit configured to perform a statistical process for the samples divided into each of the groups to calculate data indicating a statistical state of a predetermined data item between the groups, and a user interface unit configured to display the statistical state in a display format enabling recognition of the statistical state between the groups on the basis of the data indicating the statistical state of the predetermined data item between the groups calculated by the statistical state calculation unit.

Method for displaying, user interface unit, display device and inspection apparatus

In a controller and a machine learning device capable of suppressing an influence of an abnormal value based on noise, etc., the machine learning device included in the controller includes a state observation unit for acquiring input data including at least one of internal data and external data of the manufacturing machine controlled by the controller, an input safety circuit for detecting an abnormality in the input data and outputting safe input data, a machine learning unit for executing learning of a learning model and inference using the learning model based on the safe input data and outputting inference data as an inference result, an output safety circuit for detecting an abnormality in the inference data and outputting safe inference data, and an output unit for outputting output data based on the safe inference data.

Controller and machine learning device

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.

Thermal displacement correction system and computer

A calculation formula learning unit sets a coefficient relating to a time lag element in a thermal displacement estimation calculation formula by machine learning while fixing a coefficient relating to measured data except the coefficient relating to the time lag element at a predetermined value based on a difference between a thermal displacement estimated value about a machine element calculated by substituting a measured data group into the thermal displacement estimation calculation formula and a thermal displacement actual measured value about the machine element; sets the coefficient relating to the measured data except the coefficient relating to the time lag element in the thermal displacement estimation calculation formula by machine learning based on the difference while fixing the coefficient relating to the time lag element at a predetermined value; and repeats the machine learning.

A numerical controller looks ahead and analyzes commands indicated by a block contained in a program, and identifies a travel direction of a control target for each of the commands to calculate a time constant based on the identified travel direction. The numerical controller then sets a time constant for filter processing based on the time constant for each of the commands, and performs filter processing on command data subjected to a linear acceleration and deceleration process, based on the set time constant. The numerical controller then calculates movement of each axis for each interpolation period, based on the command data subjected to the filter processing.

Numerical controller dynamically switching time constant for acceleration and deceleration filter

A numerical control device includes a command analysis unit, an acceleration calculation unit in reversing operation, a velocity control unit, and an interpolation unit. The acceleration calculation unit in reversing operation is configured to calculate the acceleration of the drive shaft before and after the reversing operation when reversal of the commanded path of the drive shaft based on the commanded data is detected by the velocity control unit.

Growing community of inventors

Yamanashi, Japan

Mitsunori Watanabe