A method for the variothermal temperature control of an injection mould using a temperature control device, the method including at least the following steps: in a learning phase, determining a temperature control characteristic of the temperature-controllable system including at least the injection mould and the temperature control device, in order to obtain individual reference values for the system, with which the temperature control device can be controlled in order to obtain a nominal temperature profile; and in a production phase: temperature control of the injection mould with the reference values determined during the learning phase; determining deviations of an actual temperature profile of the injection mould in relation to the nominal temperature profile during the production cycle and calculating corrected reference values from these deviations; and carrying out a resulting production process using the corrected reference values.

An artificial intelligence-based injection molding system comprising a standard data extraction unit for extracting target standard data of a product produced by a mold from mold information about the mold to which a molding material is supplied; a molding condition output unit inputing the extracted target standard data into a pre-learned molding condition generation model to output a molding condition; an injection molding device, supplying the molding material to the mold according to the molding condition to produce the product; and a determination unit, comparing production standard data of the produced product and the target standard data to determine whether the molding condition is appropriate, wherein, if the determination unit determines that the molding condition is inappropriate, the molding condition output unit generates the production standard data and the molding condition as one set of feedback data, and trains the molding condition generation model with the set of feedback data.

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 model-based machine learning system for calculating optimum molding conditions includes a data storage device providing a set of training data; an injection molding process emulator producing a set of emulated sensing data according to molding conditions as inputted; an injection molding process state observation unit, determining an injection molding process state from molding conditions, sensing data and a quality state, wherein the quality state at least includes an acceptance state; and an injection molding process optimization unit including an injection molding condition optimizer, wherein a molding condition optimization model constructed in the injection molding condition optimizer is trained according to the injection molding process state as determined, and the molding condition optimization model after training is introduced into an injection molding production line.

An injection molding system includes: determining a manufacturing condition including a first mold and a first injection molding machine; checking whether a first actual production result acquired using the combination of the first mold and the first injection molding machine exists by searching an actual production result storage unit; and generating a correction molding condition for performing injection molding using the combination of the first injection molding machine and first mold based on first molding-machine-unique information acquired in advance for the first injection molding machine, second molding-machine-unique information acquired in advance for a second injection molding machine with which a second actual production result is acquired in combination with the first mold, and the second actual production result acquired from the actual production result storage unit when the first actual production result does not exist, and inputs the generated correction molding condition to the second injection molding machine.

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.

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.

There is provided a teaching method for a system for taking out a molded product. A projected image obtained by projecting an attachment mounted to an elevating frame onto a virtual plane, or a maximum-dimension virtual projected image of the projected image, is displayed as is superposed on the virtual plane, the virtual plane including a captured image of an opening end surface of a die that is opened, the captured image being captured by a single imaging device, before the attachment is advanced into the die. The range of the die that is opened in which the attachment is to be lowered is checked through visual observation.

A state determination device acquires data related to an injection molding machine, stores a learning model obtained by learning an operation state of the injection molding machine with respect to the data, and performs estimation using the learning model based on the data. Further, the state determination device acquires a correction coefficient, which is associated with a type of the injection molding machine and equipment attached to the injection molding machine and numerically converts and corrects the estimation result with a predetermined correction function to which the acquired correction coefficient is applied.

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.