A controller for an injection molding system is in communication with a melt flow control unit, a gas assist control unit, and a gas counter pressure control unit. The controller can effect real-time adjustments to gas assist pressure and/or gas counter pressure as a function of melt pressure or flow front 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.

Systems and approaches for controlling an injection molding machine and a mold forming a mold cavity and being controlled according to an injection cycle. The systems and methods include analyzing a model of at least one of the injection molding machine, the mold, and a molten material to determine initial values for one or more control parameters of the injection molding machine, and executing a run of injection cycles at the injection molding machine; measuring operation of the injection molding machine during a particular injection cycle of the run of injection cycles; determining one or more operational parameters exceed a threshold; and upon determining that the one or more operational parameters exceed the threshold, adjusting the one or more control parameters for subsequent injection cycles of the run of injection cycles.

Systems and approaches for controlling an injection molding machine and a mold forming a mold cavity and being controlled according to an injection cycle. The systems and methods include analyzing a model of at least one of the injection molding machine, the mold, and a molten material to determine initial values for one or more control parameters of the injection molding machine, and executing a run of injection cycles at the injection molding machine; measuring operation of the injection molding machine during a particular injection cycle of the run of injection cycles; determining one or more operational parameters exceed a threshold; and upon determining that the one or more operational parameters exceed the threshold, adjusting the one or more control parameters for subsequent injection cycles of the run of injection cycles.

A computer implemented method for generating a mold model for production predictive control and computer program products thereof. The method comprises receiving first parameters about molding machine sensors and second parameters about mold cavity; classifying each injection cycle of a plurality of injection cycles of a first injection molding machine considering the first and second parameters and quality or characteristics of injected given parts in the machine; processing the first and second parameters to remove undesired or irregular data values thereof; merging the first and second parameters providing a global group of processed parameters; executing a machine learning algorithm on the global group of processed parameters generating an extended mold model; and using said generated extended mold model for further monitoring and control of the mold in further injection processes in the first injection molding machine and/or for optimizing a production process of the mold in the first molding machine.

The invention relates to a method and system wherein an analysis system (6) measures derivative process parameters (7) from the derivative physical production process, wherein the precursor charge (3) is produced at a precursor production facility (8) at least 50 km distant from the physical production facility (2) through a precursor production process based on applied precursor production settings (9), wherein the precursor charge (3) is transported to the physical production facility (2), wherein the analysis system (6) measures precursor process parameters (11) from the precursor production process and precursor product parameters (12) from the precursor charge (3), wherein the analysis system (6) enters the applied derivative process settings (4), the measured derivative process parameters (7), the applied precursor production settings (9), the measured precursor process parameters (11), the measured precursor product parameters (12) as input to a process model (13), which process model (13) describes a computational relationship between derivative process settings, derivative process parameters, precursor production settings, precursor process parameters and precursor product parameters, to obtain updated derivative process settings (15).

An inspection apparatus includes an imaging device acquiring a polarization image of a molding product, and a determination unit determining quality of the molding product. The determination unit includes an image generating unit generating a pseudo image from an input image in accordance with a machine-learned learning model, and the determination unit inputs the polarization image or a calculation image obtained by calculation from the polarization image to the image generating unit as the input image and determines the quality of the molding product based on the input image and the pseudo image generated by the image generating unit.

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.

A method for simulating a fiber orientation in an injection-molded part made of a fiber-reinforced plastic. An orientation of the fibers in the injection-molded part to be manufactured that is present after the injection molding is determined via a macroscopic simulation of the injection molding. The macroscopic simulation of the injection molding takes place using macroscopic physical parameters of the fiber-reinforced plastic. In the macroscopic simulation, a temporal development of the fiber orientation tensor is determined via a combination of two macroscopic models. A first temporal development of the fiber orientation tensor is determined via a first macroscopic model based on shear flows. A second temporal development of the fiber orientation tensor is determined via a second macroscopic model based on elongation flows. The method is applied in a method for designing an injection-molded part made of a fiber-reinforced plastic.

In order to reduce oscillations in process variables of an injection molding process, an injection molding machine may be operatively connected to a model database that stores models of injection molding machines and molds. A tuning controller may set initial gain values of a variable-gain proportional-integral-derivative (PID) controller. To set the initial gains, the tuning controller may be configured to obtain, from the model database, a model for a first and second injection molding machines and a model for a mold. The tuning controller may analyze the models to determine a correlation between injection molding machine parameters and mold cycle performance for the mold. Accordingly, the tuning controller may apply the correlation to determine an initial gain value for a least one of the first, second, and third gains of the PID controller. The tuning controller may then set the initial gain values for the PID controller.