Fanuc Corporation

Keita Hada

Masanobu Takemoto

Kazunori Iijima

Kazuomi Maeda

Kazuhiro Satou

Hiroshi Minami

Zhenxing Li

Chao Zhi

Ryutaro Ishikawa

Takeshi Sumi

A machine learning apparatus determines a control parameter of an active vibration isolation apparatus on which an industrial machine is mounted. The industrial machine includes a movable part, a drive source that drives the movable part, and a drive source control section that controls the drive source to position the movable part at a command position. The machine learning apparatus includes: an acquiring section that acquires, as teacher data, a positional deviation, which is a difference between the command position and an actual position of the movable part; a storage section that stores a learning model that outputs the control parameter corresponding to a state quantity concerning the industrial machine; and a learning section that updates the learning model using the teacher data.

Machine learning apparatus, machine learning method, and industrial machine

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.

Thermal displacement compensator

A wire disconnection prediction device includes: a data acquisition part configured to acquire data relating to machining of a workpiece in a state where a wire is not disconnected during machining of the workpiece by a wire electric discharge machine; a preprocessing part configured to create, machining condition data of a condition relating to a machining condition commanded in machining of the workpiece, machining member data relating to a member used in the machining, and machining state data during machining of the workpiece, as state data indicating a state of the machining; and a learning part configured to generate, based on the state data created by the preprocessing part, a learning model indicating correlation between the state data and the state where the wire of the wire electric discharge machine is not disconnected.

Wire disconnection prediction device

An abnormality determination device acquires observation data observed during an operation of an industrial machine, extracts partial time-series data, including a portion representative of a feature of an operating state at a specified timing, from the observation data, calculates a statistical amount from the extracted partial time-series data, and performs processing for machine learning related to determination of operation abnormality of the industrial machine, based on the calculated statistical amount.

Abnormality determination device and abnormality determination system

A wire disconnection prediction device includes: a data acquisition part configured to acquire data relating to machining of a workpiece during machining of the workpiece by a wire electric discharge machine; a preprocessing part configured to create, based on the data acquired by the data acquisition part, machining condition data, machining member data and machining state data, as state data indicating a state of the machining; and a machine learning device configured to execute, based on the state data created by the preprocessing part, processing relating to machine learning using a learning model indicating correlation between a machining state in the wire electric discharge machine and presence/absence of a possibility of disconnection occurrence of a wire electrode in the wire electric discharge machine and a disconnection cause by a plurality of class sets.

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.

Thermal displacement compensation system

A wire electrical discharge machine includes a guide pipe arranged in a path along which a wire electrode is fed from a feed roller to a workpiece, and formed with an insertion hole through which the wire electrode is inserted, a compressed air supply device configured to supply compressed air to the insertion hole so that compressed air flows through the insertion hole along a feed direction of the wire electrode, a database configured to store data indicative of a relationship between rigidity information of the wire electrode and pressure information of compressed air, and a controller configured to set pressure of the compressed air supplied to the insertion hole, based on the rigidity information of the wire electrode fed to the workpiece and the data.

Wire electrical discharge machine

A collision position estimation device allowing extraction of data related to a torque of a section related to collision of a cushion member includes a machine learning device. The machine learning device includes a state observation unit for observing axis movement data indicating information related to movement of the axis and motor torque data indicating data related to torque of a motor driving the axis as a state variable representing a current state of an environment, a label data acquisition unit for acquiring collision position data indicating the position of the axis during collision of the cushion member as label data, and a learning unit for associating and learning the information related to movement of the axis and the data related to the torque of the motor driving the axis with the position of the axis during collision of the cushion member using the state variable and the label data.

Collision position estimation device and machine learning device

To provide a wire electric discharge machine which can appropriately perform thermal displacement correction of upper/lower guides even when the installation environment changes. Provided are a storage unit that stores temperatures of machine elements as temperature data, and a rendering unit that digitizes the installation environment and renders as environmental data. Additionally provided are a position command unit that commands a relative position of the upper/lower guides; and a relational expression calculation unit that sets the temperature data environmental data as input data, sets the relative position as training data, and calculates the relational expression by way of machine learning. Further provided are a relational expression decision unit that calculates a correction amount by substituting the temperature of the machine element into this relational expression, and in the case of error between the relative position of the upper/lower guides based on this correction amount and the relative position commanded by the position command unit being small, decides this relational expression as a formal relational expression; and a correction execution unit that performs correction on the relative position of the upper/lower guides using this relational expression.

Wire electric discharge machine

To provide a wire electric discharge machine capable of suitably and simply performing thermal displacement correction on upper and lower guides. Provided are a storage unit that stores temperatures of machine elements and actual values for relative positions of upper and lower guides to be associated with each other as associated data; and a relational expression calculation unit that infers and calculates a relational expression between the temperature of the machine element and the relative positions of the upper and lower guides by way of machine learning with this associated data as training data. Additionally provided are a position estimation unit that substitutes temperatures of the machine element into the relational expression and calculates an estimated value for the relative position of the upper and lower guides; and a correction amount calculation unit that calculates a correction amount for the upper and lower guides, based on the estimated value for the relative position. Further provided is a correction execution unit that performs correction of the relative position of the upper and lower guides based on this correction amount.

There is provided a wire electric discharge machine including a calibrating unit which carries out calibration for obtaining a detected deviation amount of the tensile force detector based on the set tensile force and a tensile force detected by the tensile force detector when the set tensile force is applied, the calibrating unit correcting output of the tensile force detector by the detected deviation amount which is obtained by the calibration, and the wire electric discharge machine carries out feedback control of the tensile force of the wire electrode based on a corrected detected tensile force which is obtained by correcting the detected tensile force of the tensile force detector by the calibrating unit.

Wire electric discharge machine having function to correct detected value of tensile force

A wire-cut EDM machine in which the position of the liquid level of machining fluid is adjusted at the time of automatic wire threading includes a shape information acquisition unit for acquiring information on the shape of an object to be machined, a wire-threading position information acquisition unit for acquiring information on a wire-threading position of a wire electrode based on relative positions of upper and lower wire guides, and a liquid level position adjustment unit for acquiring the height of the object to be machined in the vicinity of the wire-threading position based on the information on the shape of the object to be machined and adjusting the position of the liquid level of the machining fluid at the time of automatic wire threading.

Growing community of inventors

Yamanashi, Japan

Keita Hada