A molding machine includes an injection device to inject a molding material into a mold and a control device to control the injection device. The injection device includes a heating cylinder filled with the molding material and a screw moving forward/backward inside the cylinder to inject the molding material, and can move forward/backward between a communication orientation where a distal end of the cylinder communicates with the clamped mold and a separation orientation where the distal end separates from the mold and can pivot on a pivot shaft between the separation orientation not allowing insertion/removal of the screw and a pivot orientation allowing the insertion/removal. The molding machine further includes a sensor to detect these orientations. The control device determines that the screw has been replaced by the sensor detecting that the injection device pivots from the separation orientation to the pivot orientation and returns to the separation orientation.

An injection molding system including an injection molding device including an injection molding section that has a fixed section to which a fixed molding die and a movable molding die are attached and fixed and that is configured to mold a molded article by injecting a material containing a metal powder into a cavity, and a first movement section for moving the movable molding die between a molding die closed position and a molding die open position, a cleaning device that performs cleaning of the molded article by blowing air, and a control device, wherein when the movable molding die is not at the molding die closed position, the control device does not cause the cleaning device to blow air, and when the movable molding die is at the molding die closed position, the control device causes the cleaning device to perfom blowing of air.

A learned model includes an input layer; intermediate layers connected to the input layer; and an output layer connected to the intermediate layer. The learned model causes a computer to function to perform machine learning based on first data and ground truth information, the first data indicating a detection result or a measurement result of the molding product, for each first value set or detected with respect to a predetermined item for producing the molding product by an injection molding machine, and the ground truth information indicating a setting of the predetermined item derived based on evaluation information of the molding product when the first value is set or detected, and output, from the output layer, information relating to a setting of the predetermined item, when second data is input from the input layer, the second data indicating a detection result or a measurement result of the molding product.

A method for regulating an injection molding process in which process setting variables of an injection molding machine are controlled via a regulating module, which receives data from a process-internal sensor system of the injection molding machine, data on the fabricating sequence of the injection molding component parts from an external sensor system and/or data on the quality of a fabricated injection molded component part from an online component part control, evaluates these data in a quality prognosis module and, as a function of the data evaluation, performs a change in the process setting variables of the injection molding machine, because of the change in the process setting values, the working point of the injection molding machine being changed so that the quality features of the injection molded component parts fabricated using the changed working point lie within the specified tolerances of the quality of the injection molded component parts.

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.

An injection molding machine system includes an injection molding machine, a cloud server, and a terminal apparatus. Cloud server is configured to communicate with injection molding machine, and stores data indicative of an operating state of injection molding machine. Terminal apparatus is configured to communicate with cloud server, and display information from cloud server. Injection molding machine is configured to transmit the data to cloud server at a predetermined timing and when injection molding machine is not performing a molding operation.

An injection molding machine management system, which manages an injection molding unit including an injection molding machine and a peripheral device that performs a previous step or a subsequent step of a molding step, includes a first display unit that displays a first screen for displaying a state of the molding step of the injection molding machine. The first screen includes a first screen area and a second screen area. The first screen area displays a first indicator related to quality in the molding step of the injection molding machine, and the second screen area displays a second indicator related to quality in the previous step of the molding step or the subsequent step of the molding step.

A molding management system including a server and managing production of a product in a production process including an injection molding process of the product by an injection molding device, wherein the server is communicably connected to a terminal device and configured to cause the terminal device to display production plan information indicating a production plan for a designated first product in response to an operation received from the terminal device and the production plan information includes first operating time period information indicating a first operating time period in which the injection molding device is operated to produce the first product and inoperable time period information indicating at least an inoperable time period during which the injection molding device cannot be operated to produce the first product.

In one aspect there is disclosed a method of improving shot repeatability in a multilayer reciprocating screw injection molding apparatus by preventing pressure communication or cross-talk between melt channels. In the first aspect an outlet nozzle valve is closed prior to closing a check valve within an injection unit of the multilayer reciprocating screw injection molding apparatus. In another aspect there is disclosed a method of improving shot repeatability in a reciprocating screw injection molding apparatus by recording a first position of a screw within the barrel of an injection unit of a reciprocating screw injection molding apparatus, the first position corresponding to a volume of melt within the barrel of the material injection unit. A second position of the screw is then calculated based on the first position, the second position corresponding to a transition position of the screw within the barrel of the injection unit of the reciprocating screw injection molding apparatus.

A method for monitoring a cyclic manufacturing process in which at least one piece good is manufactured in each cycle includes in a first process step for at least one cycle, at least one temporal sequence of sensor data of the manufacturing process, wherein the sensor data are in an effective relationship with a stability and an anomaly of the manufacturing process. In a second process step, the sensor data are marked as stable sensor data once the manufacturing process is determined to attain stability. In a third process step, the stable sensor data are reduced dimensionally to point data. In a fourth process step, a density distribution of the point data is formed with at least one stability area of the point data and at least one anomaly area of the point data that corresponds to an anomaly of the manufacturing process.