An injection molding machine includes a mold clamping unit that clamps a mold unit, an injection unit that fills the mold unit clamped by the mold clamping unit with a molding material, and an ejector unit that takes out a molding product from the mold unit after the molding material with which the mold unit is filled by the injection unit is cooled and solidified, in which a communication cycle in which data is exchanged in at least one path of between two internal devices and between the injection molding machine and an external device is shorter than a control cycle in which predetermined control is performed using received data.

When an injection molding machine performs molding, the mold clamping force on the mold is adjusted on the basis of a mold displacement of the mold or the injection peak pressure and injection foremost position so that the molding is performed without causing flash and by an appropriate mold clamping force with which energy can be reduced. The amount of mold displacement and also the injection peak pressure and the injection foremost position are monitored during automatic operation. If there occurs no mold displacement change exceeding a threshold or if there occurs no injection peak pressure anomaly or injection foremost position anomaly exceeding thresholds, the automatic operation is continued. If the mold displacement change occurs or if the injection peak pressure anomaly and the injection foremost position anomaly occur, the operation of the injection molding machine is stopped.

An injection molding system includes a measuring unit to measure a movement of a line of sight of a worker observing a molded article, a line-of-sight data storage unit to store line-of-sight information representing movements of the worker's line of sight and a measurement time, an identifying unit to identify a focus area of the molded article, a focus area storage unit to store an image of the identified focus area, a molding defect type input unit to input or select a molding defect type, and a machine learning device to machine learn the molding defect type from the image of the focus area. The machine learning device inputs a type of a molding defect that has occurred in the molded article and carries out machine learning to learn and automatically recognize a feature quantity of the molding defect from the image of the focus area.

According to one embodiment, an operating state management device includes a first memory, a processor, and a second memory. The first memory is configured to store, in each of a plurality of operating state management target devices, feature patterns of detection states of a plurality of sensors that detect states of individual components in association with operating states. The processor is configured to compare patterns corresponding to actual detection states of the plurality of sensors with the feature patterns stored in the first memory, and determine operating states. The second memory is configured to store the determined operating states in time series.

A resin detection system for closing an automated valve to prevent resin entering a vacuum line portion of an outlet line is described. The resin detection system comprises a support (300) to a pneumatically-actuated device (320), a capacitive sensor (380) and a valve (410) are mounted. The sensor is configured to provide at least one signal indicative of the presence of resin in the outlet line which is used to close the valve automatically once the presence of resin is sensed by the capacitive sensor. The resin detection system forms part of an injection moulding apparatus and receives signals from a processor thereof to ensure that, if too much resin is injected, the valve is automatically closed even in the event that the sensor does not detect the presence of resin in the outlet line.

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 method of diagnosing a state of at least one component of a molding machine is performed such that a state of the at least one component is determined on the basis of a vibration measured at the molding machine. A drive unit of the molding machine accelerates and/or decelerates at least one movable element provided at the molding machine in such a way that the vibration is stimulated in a predetermined frequency range.

An injection molding machine having vent mechanism includes a plastication device, an injection device, a junction, a vent-up sensor, and a controller. The vent-up sensor detects vent-up. When the vent-up is detected, the controller switches from normal conditions to vent-up suppression conditions. The vent-up suppression conditions have at least one of the followings: increasing a rotational speed of the plastication screw to be higher than that in the normal conditions; using retreat-metering when the retreat-metering is not performed in the normal conditions, wherein in the retreat-metering, in addition to a pressure of the resin material, a force in a retreat direction is applied to the injection plunger to move the injection plunger at a predetermined retreat speed in the metering; and increasing the retreat speed of the injection plunger to be higher than that in the normal conditions when the retreat-metering is performed in the normal conditions.

An injection molding system includes a measuring unit to measure a movement of a line of sight of a worker observing a molded article, a line-of-sight data storage unit to store line-of-sight information representing movements of the worker's line of sight and a measurement time, an identifying unit to identify a focus area of the molded article, a focus area storage unit to store an image of the identified focus area, a molding defect type input unit to input or select a molding defect type, and a machine learning device to machine learn the molding defect type from the image of the focus area. The machine learning device inputs a type of a molding defect that has occurred in the molded article and carries out machine learning to learn and automatically recognize a feature quantity of the molding defect from the image of the focus area.

A state determination device includes an acquisition unit acquiring the number of productions and data related to a physical quantity as data indicating a state related to an injection molding machine, a first calculator calculating a feature amount indicating a feature of a state of the injection molding machine based on the data related to the physical quantity, a second calculator calculating a statistic as statistical data according to a statistical condition including a statistical function for calculating a statistic from a feature amount based on the calculated feature amount, an analyzer performing regression analysis using a regression formula based on the statistical data and the number of productions, and calculating a coefficient of the regression formula, and a determination unit determining the number of productions or a date and time using the regression formula, a warning value being reached at the number of productions or the date and time.