An inspection method for an injection molding device includes: a first step of acquiring a first detection value from a sensor using a test mold having the sensor, the sensor measuring a temperature or pressure of a molten material injected from a first injection molding device into a cavity section demarcated by a fixed mold and a moving mold; a second step of injecting a molten material from a second injection molding device into the cavity section and acquiring a second detection value from the sensor; and a third step of performing an inspection using the first detection value and the second detection value.

Provided is a molding support apparatus that supports production of a molded product of a composite material, and the apparatus includes: a hardware processor that calculates a Talbot feature of the molded product, based on a Talbot image acquired from an X-ray Talbot imaging apparatus that images the molded product, and identifies, using the calculated Talbot feature, an item that allows adjustment of the Talbot feature from among a plurality of types of items constituting a production process for producing the molded product.

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: 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.

A reverse rotation condition estimating apparatus includes: a learning model storage unit for storing a learning model for estimating reverse rotation conditions; an acquisition unit for acquiring a predetermined time series data set supplied from an injection molding machine at least during a pressure reducing step; and an estimation unit for estimating the reverse rotation conditions using the predetermined time series data set acquired by the acquisition unit and the learning model stored in the learning model storage unit.

A target value progression for a process parameter functioning as a setting variable is established so that an actual value progression for a selected variable has a desired property, or the desired actual value progression itself ensues. The actual value progression occurring in relation to the first configuration is predetermined as a reference value progression for the selected variable, and a target value progression for the process parameter functioning as the setting variable is established by a computer so that the reference value progression as the actual value progression with the desired property or the desired actual value progression itself ensues when the shaping machine in the second configuration operates in a production cycle in accordance with the selected target value progression for the at least one process parameter functioning as the setting variable.

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.

An image display device displays an image such that a pair of reference symmetric structure portions, e.g. a pair of guide pins, are included in a captured image displayed on a display section, the pair of reference symmetric structure portions being located symmetrically with respect to a second virtual plane which extends in the opening direction and the vertical direction and includes a virtual center line of the molding apparatus which extends in the opening direction. A teaching execution section is used to determine the lateral position of an approach frame in the take-out operation by changing the position of the approach frame such that the center of an imaging range is located at a middle position between the pair of reference symmetric structure portions included in the captured image.

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.

A method for carrying out a cyclical manufacturing process produces parts within a predefined quality tolerance. After at least one process adjustment variable is changed, a quality feature of the parts produced with a changed process adjustment variable is checked against the range of the quality tolerance of the produced parts. A process characteristic variable zone is formed in an automated manner using at least one determined process characteristic variable variant that is process-stable and for which the process adjustment variable produces acceptable parts.