A device for assisting molding condition determination includes a molding state data adjustment amount obtaining unit and a molding condition element adjustment amount obtaining unit. The molding state data adjustment amount obtaining unit obtains, using a first learning model, a molding state data adjustment amount having a value equivalent to a difference between molding state data detected by a sensor and a molding state data target value. The molding condition element adjustment amount obtaining unit obtains, using a second learning model, an adjustment amount for a molding condition element corresponding to the molding state data adjustment amount.

A method for controlling an injection molding process based upon an actual plastic melt pressure including identifying an optimal actual plastic melt pressure curve over time based on a baseline cycle, and adjusting in a subsequent cycle an injection pressure in order to cause a monitored pressure of the molten thermoplastic material to follow the optimal actual plastic melt pressure curve over time. A method for controlling an injection molding process based upon an plastic melt pressure setpoint adjusted using a cavity pressure, wherein the cavity pressure is multiplied by a process factor. Further, a method for controlling an injection molding process using real time cavity pressure including identifying an optimal cavity pressure curve over time based on a baseline cycle and adjusting, in a subsequent cycle, an injection pressure in order to cause a monitored cavity pressure to follow the optimal cavity pressure curve over time.

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.

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 method for operating an injection-molding machine, including the steps: a) in a current injection-molding cycle, after an accepted-part reference injection-molding cycle learned in a learning phase: detecting a compound pressure change k.sub.1 relative to an accepted-part reference compound pressure p.sub.masse, ref during at least part of an injection phase of the current injection-molding cycle by measuring a current compound pressure p.sub.masse, act and comparing the current compound pressure p.sub.masse,act with the accepted-part reference compound pressure p.sub.masse,ref, b) determining a target mold internal pressure curve p.sub.wkz,soll (t) for a holding-pressure phase of the current injection-molding cycle, wherein for this purpose a mold internal pressure curve p.sub.wkz,ref (t) of the accepted-part reference injection-molding cycle is adjusted at least in dependence on the compound pressure change k.sub.1 detected in step a), and c) traveling the holding-pressure curve p.sub.masse, Hld, act (t) of the current injection-molding cycle in such a way that an actual mold internal pressure curve pwkz,act (t) of the current injection-molding cycle runs at least more closely along the target mold internal pressure curve p.sub.wkz,soll (t) than a mold internal pressure curve p.sub.wkz,ref(t) that is unchanged in comparison with the accepted-part reference cycle.

Control method, control system, computer-implemented method for determining a predicted weight value of a product produced by an injection molding device and a computer-implemented method for training a machine learning (ML) via an ML method, wherein the trained ML model is configured to determine the predicted weight value of the product produced via the injection molding device, where the method comprises recording and/or determining first production parameters of the injection molding device during production of a first product, recording and/or determining predecessor production parameters of the injection molding device during production of at least one predecessor product and each predecessor weight value of the at least one predecessor product, recording and/or determining a first weight value for the first product, and training the ML model, via a supervised learning method, with the first product parameters, further product parameters, at least one predecessor weight value, and the first weight value.

An injection molding condition generation system improves the quality of injection molding. The injection molding condition generation system acquires a material property value of a resin material and an injection molding condition using the first resin material based on a target value of a quality parameter related to a quality of a molded article, the first material property value, and a predetermined relational expression. The predetermined relational expression indicates a relation among the material property value of the resin material, a plurality of injection molding conditions input to an injection molding machine, and a quality parameter related to the quality of the molded article molded by the injection molding machine based on the material property value and the injection molding conditions, and is generated based on data accumulated in the memory in association with the material property value of the resin material, the injection molding conditions, and the quality parameter.

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.

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 abnormality detection system includes an abnormality detection apparatus that detects an abnormality of a manufacturing device, a control device that performs operation control of the manufacturing device and transmits operating data, and a sensor that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs sensor value data. The abnormality detection apparatus receives operating data transmitted from the control device, acquires sensor value data output from the sensor, calculates statistics of the sensor value data acquired, determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received, and transmits a determination result and the statistics to the control device.