In order to improve the consistency of molded products as viscosity shifts throughout a run, a controller of an injection molding machine executes a calibration cycle in accordance with a mold cycle. The controller analyzes a plurality of sensed melt pressure values during the calibration cycle to determine one or more calibration metrics. The controller then uses the calibration metrics when executing each mold cycle of the run. More particularly, during each mold cycle of the run, the controller detects a plurality of sensed melt pressures prior to and during a fill phase of the mold cycle and compares the plurality of sensed melt pressures to the one or more calibration metrics to predict cavity pressure for a pack and hold phase of the mold cycle. The controller then adjusts a set point pressure for the pack and hold phase based on the predicted cavity pressure.

An injection molding machine uses a controller to effectively control its operation. The controller 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 controller also may determine a number of set points used to operate the machine. The controller may cause the machine to operate at these set points, thereby resulting in the machine operating at or below the maximum load value by adjusting any number of machine parameters associated with the injection molding machine.

An injection molding machine uses a controller to effectively control its operation. The controller 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 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.

Disclosed is an injection molding system including: a state observation section observing, when injection molding is performed, physical-amounts relating to the injection molding that is being performed; a physical-amount data storage section storing the physical-amount data; a reward-conditions setting section setting reward conditions for machine learning; a reward calculation section calculating a reward based on the physical-amount data and the reward conditions; an operating-conditions adjustment learning section performing machine learning of adjusting operating conditions based on the reward calculated by the reward calculation section, the operating conditions, and the physical-amount data; a learning-result storage section storing a learning result of the machine learning by the operating-conditions adjustment learning section; and an operating-conditions adjustment-amount output section determining and outputting an operating condition to be adjusted and an adjustment amount based on the machine learning by the operating-conditions adjustment learning section.

Provided is a control device for an injection molding machine, capable of reducing the load on a user when re-displaying a previously displayed screen. The control device (20) for the injection molding machine (1) controls the display of display images on a display unit (29). This control device (20) divides the display unit (29) into a first display area (31) and a second display area (32) and, if an instruction to scroll in the vertical direction is received when a first display image (P1) is displayed in the first display area (31) and a second display image (P2) is displayed in the second display area (32), synchronously scrolls the first display image (P1) and the second display image (P2) whilst maintaining the relative positions of the first display image (P1) and the second display image (P2).

According to one embodiment, a management apparatus includes a processor comprising hardware. The processor calculates, based on information on two or more parameters for production of a part and an evaluation value for a quality of the part produced, a prediction formula representing a relationship between the quality and the parameters. The processor calculates, based on the prediction formula, an optimum value for the parameters. The processor causes a display to display a graph indicative of a relationship between the prediction formula and the optimum value.

A method of classifying a temperature control branch of a molding tool includes producing molded parts in cycles by a portion of the molding tool by introducing heat into the molding tool, and/or cyclically activating a heating device, with introduction of heat into the molding tool, conveying temperature control medium through the temperature control branch of the molding tool to dissipate the introduced heat, measuring a temporal branch temperature profile of the temperature control medium in the temperature control branch over several production cycles, analyzing a curve behavior of the branch temperature profile and/or of a variable derived from the branch temperature profile, in particular of a branch heat flow, over several production cycles, and sorting the temperature control branch into one of at least two categories, according to greater and/or smaller influence on the heat budget of the portion of the molding tool, on the basis of the curve behavior.

A state determination device includes: a primary determination learning model that has learned an outline of a state of a manufacturing device based on a state variable acquired from a manufacturing operation of a product of the manufacturing device; and a secondary determination learning model that has learned a state of the manufacturing device based on a state variable acquired from a predetermined operation pattern set in advance and maintenance information. Then, a primary determination is made on the outline of the state of the manufacturing device using the primary determination learning model based on the state variable acquired from the manufacturing operation of the product, and further, a secondary determination is made on the state of the manufacturing device using the secondary determination learning model based on the state variable acquired from the predetermined operation pattern.

The invention relates to a method for filling a mold cavity of a molding tool in a volumetrically correct manner. A molded part/volume equivalence is ascertained during a learning phase, and production injection-molding cycles are influenced during a production phase such that the molded part/volume equivalence ascertained during the learning phase is also satisfied during the production injection-molding cycle.

A vibration sensor detects first data of a first component group and second data of a second component group. An injection molding machine performs diagnosis processing for diagnosing the first component group based on the first data in an injection mode and performs diagnosis processing for diagnosing the second component group based on the second data in a plasticization mode.