For the purpose of enabling high-accuracy detection as to whether a molded resin product is a non-defective product or a defective product and advance detection of a molded resin product that may suffer deformation or the like in the future, the present invention relates to an inspection method and a manufacturing method for a molded resin product as well as an inspection device and a manufacturing device for a molded resin product, wherein, in an inspection of a joint interface of a molded resin product divided into a plurality of members, the height positions of defect candidates are measured from the results of detecting X rays radiated via at least two paths when the X rays are transmitted through the molded resin product, which makes it possible to detect a defect with high accuracy.

To provide a quality prediction system predicting a quality element of a molded item using machine learning. The quality prediction system includes a sensor disposed in the mold and configured to detect state data regarding the molten material supplied in the cavity, a learned-model storage unit configured to store a model which is a learned model generated by machine learning in which the state data detected by at least the sensor is used as a training data set and is a learned model related to the state data and a quality element of the molded item, and a quality prediction unit configured to predict the quality element of the molded item which is newly molded based on the state data newly detected by the sensor and the learned model.

A controller includes a mold movement control unit that moves a movable mold between a first position separated from a fixed mold and a second position at which mold closing is completed; a determining unit determines the presence of abnormalities in at least one of the movable mold and the fixed mold on the basis of inspection information acquired from at least one of the movable mold and the fixed mold and outputs a confirmation signal related to the presence of abnormalities; and a mold stopping unit that stops movement of the movable mold on the basis of whether the confirmation signal output from the determining unit is acquired while a safety confirmation condition is satisfied after the mold movement control unit starts moving the movable mold from the first position toward the second position.

A state determination device acquires data on an injection molding machine and stores conditions for classifying the acquired data on the injection molding machine and a plurality of learning models. The state determination device further classifies the acquired data based on the stored classification conditions and settles a learning model to which the classified data are applied, among the plurality of stored learning models. Subsequently, the state determination device performs machine learning for the learning model settled as an application destination, based on the classified data.

An injection molding system includes: an injection molding machine; a physical quantity acquisition unit configured to obtain a physical quantity regarding the injection molding; a molded product image acquisition unit configured to obtain an image of a molded product; a molded product image storage unit configured to store an image of a molded product; an image storage condition setting unit configured to set a condition used for determining whether to store an image of a molded product corresponding to the physical quantity obtained by the physical quantity acquisition unit; and an image storage determination unit configured to determine whether to store the image of the molded product corresponding to the physical quantity, according to the physical quantity obtained by the physical quantity acquisition unit and the condition set by the image storage condition setting unit, and store the image in accordance with the determination result.

An abnormality detection device for detecting an abnormality of a movable part provided in an injection molding machine includes: a through hole formed in the movable part or in an adjacent member arranged adjacent to the movable part; and a microphone installed inside the through hole to collect sound generated by the movement of the movable part.

A failure cause diagnostic device of the present invention receives input of internal and external state data on injection molding machines and diagnoses failure cause of the injection molding machines by means of a machine learning device. An internal parameter of the machine learning device is obtained by performing machine learning using the state data obtained from the injection molding machines subject to failure cause and the state data obtained from the injection molding machines free of failure cause.

An injection molding machine includes: a screw inserted in an injection cylinder and configured to be movable in the axial direction; a motor configured to move the screw; a torque detection unit configured to detect the torque of the motor; a position detection unit configured to detect the position of the screw; a motor drive control unit configured to drive the motor while imposing torque limitation so that the motor's torque will not exceed a limit torque, to thereby advance the screw to the foremost position in the direction of injection; and a determination unit configured to determine that unmelted resin remains inside the injection cylinder if the moving speed of the screw becomes lower than or equal to a predetermined speed while the screw is advancing to the foremost position.

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.

A state determination device that determines an operation state of an injection molding machine stores respective specification data of a reference injection molding machine and an injection molding machine that is different from the reference injection molding machine, and acquires data related to the injection molding machine. Then, the state determination device converts the acquired data into yardstick data by a conversion formula set for every type of data, by using the stored specification data of the reference injection molding machine and the stored specification data of the injection molding machine and performs machine learning using the yardstick data obtained through the conversion so as to generate a learning model.