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 coinjection molded multi-layer container includes an inner layer, an outer layer, and a barrier layer. The inner layer includes a first polymeric material and forms an inside surface of the container. The outer layer includes the first polymeric material and forms an outside surface of the container. The barrier layer is located between the inner layer and the outer layer and includes a second polymeric material less permeable to gas than the first polymeric material. The barrier layer is biased toward the inside surface or the outside surface such that the inner layer and the outer layer have different thicknesses.

A coinjection molded multi-layer container includes an inner layer, an outer layer, and a barrier layer. The inner layer includes a first polymeric material and forms an inside surface of the container. The outer layer includes the first polymeric material and forms an outside surface of the container. The barrier layer is located between the inner layer and the outer layer and includes a second polymeric material less permeable to gas than the first polymeric material. The barrier layer is biased toward the inside surface or the outside surface such that the inner layer and the outer layer have different thicknesses.

The present application relates to a method for selecting an injection-molded article and a method for manufacturing an injection-molded article. An injection-molded article having excellent crack stability can be produced using the material selected using the described selection method without having to manufacture test articles through injection molding.

A system for setting injection-molding conditions and a method for setting actual molding conditions of an injection-molding machine are disclosed. The system includes a computer and an injection-molding equipment. The computer is configured to simulate, via computer-aided simulation software, a virtual molding using a plurality of design parameters to generate a plurality of provisional molding conditions. The injection-molding equipment is associated with the computer and configured to perform at least one trial molding using the provisional molding conditions to obtain a plurality of intermediate molding conditions. The computer optimizes the provisional molding conditions to obtain actual molding conditions in accordance with the intermediate molding conditions.

Non-time dependent measured variables are used to effectively determine an optimal ejection time of a part from a mold cavity. A system and/or approach may first measure at least one non-time dependent variable during an injection molding cycle. The part is ready to be ejected from the mold upon the measured variable reaching a threshold value indicative of, for example, a part temperature dropping below an activation temperature.

A method for manufacturing a wind turbine blade, including the step of monitoring a process of infusing and/or curing a fiber lay-up with resin in a mold, wherein the monitoring is based on sensor data obtained from the resin infusion and/or curing process displayed in an augmented reality device, is provided. Displaying sensor data obtained from the resin infusion and/or curing process in an augmented reality device allows to better monitor the resin infusion and/or curing process. Thus, the quality of the manufactured wind turbine blade can be improved.

Non-time dependent measured variables are used to effectively determine an optimal ejection time of a part from a mold cavity. A system and/or approach may first measure at least one non-time dependent variable during an injection molding cycle. The part is ready to be ejected from the mold upon the measured variable reaching a threshold value indicative of, for example, a part temperature dropping below an activation temperature.

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.