There are provided a molding conditions determination assist device and a resin state estimation device including: a quality estimation unit which estimates quality of the mold products by a machine learning on the basis of a detection data; a quality transition storage unit which accumulates the estimated quality of the mold products and stores a quality transition with respect to the accumulated quality of the mold products; a tendency evaluation unit which evaluates a quality change tendency with respect to a prescribed standard quality on the basis of the quality transition; a relationship storage unit which stores a relationship between the quality change tendency and a modification amount of the molding conditions for returning the quality to the standard quality; and a modification conditions determination unit which determines the modification amount of the molding conditions on the basis of the quality change tendency and the relationship.

An injection molding assistance system includes: an information receiving unit, receiving trial molding conditions, article information, and molding defect types; a design variable determination unit, determining a part of molding conditions to be learned as design variables based on the article information and the molding defect types; a learning data generating unit, generating multiple learning data in which the design variables are uniformly distributed in a variable space; a molding control unit, causing the injection molding machine to prototype a molded product according to the trial molding conditions that reflect the learning data; an evaluation receiving unit, receiving an evaluation for the molded product; and a learned data generating unit, generating learned data for molding an article with desired quality by being reflected in the trial molding conditions based at least on the learning data and the evaluation.

In order to reduce oscillations in process variables of an injection molding process, a variable-gain proportional-integral-derivative (PID) controller is utilized to control one or more of the process variables. The injection molding system may also include a tuning controller to automatically tune at least one of the proportional, integral, or derivative gains within a mold cycle. The tuning controller may obtain sensor data that monitors the operation of the injection molding machine to determine an adjustment to at least one of the proportional, integral, or derivative gains. The tuning controller may adjust the gains of the variable-gain PID controller in accordance with the determined adjustment to the at least one of the proportional, integral, or derivative gains.

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.

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.

An injection molding machine uses a native controller and a retrofit controller to effectively control its operation. The controllers may determine and/or receive information regarding the machine's maximum load capacity, and may also determine a current operational load value of the machine. The retrofit controller may cause the machine to operate at any number of combinations of settings of operational parameters which result in the machine operating at or below the maximum load value by adjusting any number of machine parameters associated with the injection molding machine based on feedback sensors measuring real-time operating conditions of the machine.

A state determination apparatus for determining a state related to an abnormality of an injection molding machine based on an operation state of the injection molding machine includes a machine learning apparatus for learning the state related to the abnormality of the injection molding machine. The machine learning apparatus observes, as a state variable that represents a current state of an environment, injection data that indicates the operation state of the injection molding machine, acquires label data that indicates the state related to the abnormality of the injection molding machine, and performs learning by associating the observed state variable with the acquired label data.

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.

An injection molding machine includes a storage unit in which input information input via an operation unit and a molding condition under which molding is performed using a gain specified by the input information are stored in association with each other, and a control unit configured to generate a signal with a feedback-control, which uses a difference between a set value used to control an object to be controlled and an actual value measured from the object to be controlled and the gain specified by the input information associated with the molding condition, and to control a drive source using the signal to perform the molding, in a case where the molding indicated by the molding condition is performed.

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.