Fanuc Corporation

Kenjirou Shimizu

Atsushi Horiuchi

Hiroyasu Asaoka

Haruhiko Nakao

A diagnostic apparatus includes a control unit configured to control a diagnostic operation for driving a belt, a first tension calculation unit configured to perform, based on data obtained from the diagnostic operation, a calculation to estimate a first belt tension value that is a tension value of the belt when the belt is not worn, a second tension calculation unit configured to calculate a second belt tension value in a case where a tension reduction factor of the belt and a wear factor of the belt are included, and a third tension calculation unit configured to calculate the degree of wear of the belt based on the first belt tension value and the second belt tension value. Accordingly, the diagnostic apparatus can support estimation of the degree of wear of a belt or abnormality diagnosis.

Diagnostic apparatus

A state determination device is capable of assisting maintenance for various injection molding machines. The state determination device acquires data related to an injection molding machine, performs numeric conversion for extracting a feature in a temporal direction or an amplitude direction, with respect to time-series data of physical quantity in the acquired data, and performs machine learning using the data obtained through numeric conversion so as to generate a learning model.

State determination device and state determination method

A control device for an injection molding machine including a cylinder into which a resin is supplied and a screw that moves forward and rearward and rotates inside the cylinder, includes a metering control unit that performs metering of the resin inside the cylinder by controlling forward rotation and rearward movement of the screw until the screw has been moved rearward to a predetermined metering position, based on predetermined metering conditions, a rearward movement speed acquisition unit acquiring a rearward movement speed of the screw, a speed determination unit that determines a suck back speed for causing a resin pressure to reach a target pressure, based on the rearward movement speed acquired during metering by the rearward movement speed acquisition unit, and a suck back control unit causing the screw to move further rearward based on the suck back speed, after the screw has reached the predetermined metering position.

Control device and control method for injection molding machine

An injection molding machine includes: a first driving device rotating a screw provided inside a heating cylinder; a second driving device moving the screw forward and backward; a metering control section configured to, by controlling the first driving device and the second driving device, to meter resin while melting the resin, and thereafter rotate the screw in reverse to thereby reduce the pressure of the resin; a first sensor unit for detecting the pressure; a second sensor unit for detecting one or more kinds of physical quantities that affect the change of the pressure; and a prediction section predicting decompressing rotation information based on the pressure detected by the first sensor unit and the one or more kinds of physical quantities detected by the second sensor unit. The metering control section controls the first driving device based on the decompressing rotation information predicted by the prediction section.

Injection molding machine

A state determination device acquires data related to an injection molding machine, stores a learning model obtained by learning an operation state of the injection molding machine with respect to the data, and performs estimation using the learning model based on the data. Further, the state determination device acquires a correction coefficient, which is associated with a type of the injection molding machine and equipment attached to the injection molding machine and numerically converts and corrects the estimation result with a predetermined correction function to which the acquired correction coefficient is applied.

A state determination device includes: a primary determination learning model that has learned an outline of a state of a manufacturing device based on a state variable acquired from a manufacturing operation of a product of the manufacturing device; and a secondary determination learning model that has learned a state of the manufacturing device based on a state variable acquired from a predetermined operation pattern set in advance and maintenance information. Then, a primary determination is made on the outline of the state of the manufacturing device using the primary determination learning model based on the state variable acquired from the manufacturing operation of the product, and further, a secondary determination is made on the state of the manufacturing device using the secondary determination learning model based on the state variable acquired from the predetermined operation pattern.

Growing community of inventors

Yamanashi, Japan

Kenjirou Shimizu