Abstract: 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.

Abstract: A diagnostic device is a device for estimating the tension of a belt of an industrial machine for transmitting power, and is provided with a control unit configured to control a diagnostic operation in which the belt is driven, a data acquisition unit configured to acquire at least feedback data at the time of the diagnostic operation, a preprocessing unit configured to analyze frequency-gain characteristics of the feedback data and create, as input data, a range including a resonant frequency and an anti-resonant frequency in the characteristics, and a machine learning device configured to perform processing related to machine learning, based on the data created by the preprocessing unit. The diagnostic device supports inference or abnormality detection of the value of the belt tension.

Abstract: A state determination device includes: a data acquirer configured to acquire data related to an industrial machine; an estimator configured to perform estimation using a learning model based on the acquired data; and a statistical data calculator configured to calculate a statistical quantity in accordance with a predetermined statistical condition and uses the calculated statistical quantity to calculate a statistical estimation value corrected from the estimation value estimated by the estimator and, accordingly, can adapt a state determination result calculated by the learning model to a change in the operation status or the like of the industrial machine.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A numerical control system detects a state amount indicating a state of an injection operation of an injection molding machine, generates a characteristic amount that characterizes the state of the injection operation from the state amount, and infers an evaluation value of the state of the injection operation from the characteristic amount. The numerical control system detects an abnormal state on the basis of the evaluation value, generates or updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the injection operation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A diagnostic device is a device for estimating the tension of a belt of an industrial machine for transmitting power, and is provided with a control unit configured to control a diagnostic operation in which the belt is driven, a data acquisition unit configured to acquire at least feedback data at the time of the diagnostic operation, a preprocessing unit configured to analyze frequency-gain characteristics of the feedback data and create, as input data, a range including a resonant frequency and an anti-resonant frequency in the characteristics, and a machine learning device configured to perform processing related to machine learning, based on the data created by the preprocessing unit. The diagnostic device supports inference or abnormality detection of the value of the belt tension.

Abstract: A control device for an injection molding machine is equipped with a pressure acquisition unit configured to acquire a resin pressure, a pressure reduction control unit configured to, after the screw has reached a predetermined metering position, reduce the resin pressure to a target pressure by performing at least one of reverse rotation and sucking back of the screw, and a standby pressure control unit configured to, after the resin pressure has reached the target pressure, keep the resin pressure within a predetermined range by causing the screw to be rotated in a state in which a position of the screw in an axial direction of the cylinder is maintained.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In an injection molding system, bolt position related information recognizing unit which recognizes an attachment position of a bolt for each attachment is provided. Based on the attachment position of the bolt, or the attachment position of the bolt and a phase of the bolt stored in the bolt position storage unit, bolt attaching and detaching unit attaches and detaches the bolt to and from the attachment. Thus, even if the position of the bolt varies in accordance with the attachment, it is possible to attach and detach the bolt by recognizing the attachment position of the bolt.

Abstract: 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.

Abstract: A numerical control system detects a state amount indicating a state of an injection operation of an injection molding machine, generates a characteristic amount that characterizes the state of the injection operation from the state amount, and infers an evaluation value of the state of the injection operation from the characteristic amount. The numerical control system detects an abnormal state on the basis of the evaluation value, generates or updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the injection operation.

Abstract: A state determination apparatus for determining a state related to an abnormality of an injection molding machine based on an operation state of the injection molding machine includes a machine learning apparatus for learning the state related to the abnormality of the injection molding machine. The machine learning apparatus observes, as a state variable that represents a current state of an environment, injection data that indicates the operation state of the injection molding machine, acquires label data that indicates the state related to the abnormality of the injection molding machine, and performs learning by associating the observed state variable with the acquired label data.