A hot runner process controller configured to monitor the status and operation of a hot runner system to autonomously generate information to improve the quality of injection molding process of a hot runner system having an inlet nozzle, one or more manifolds and one or more nozzles with actuator or without actuator, and one or more heating elements, the hot runner process controller is self-operating, and independent from the injection molding machine, includes: one or more sensors located on, in or at the hot runner system to detect the status and/or the operation of the hot runner system, and a processing unit and a memory. The processing unit is connected to the one or more sensors, wherein the memory stores data and program codes. The processing unit is configured to load and execute the program code to compare sensor information with the stored data and to determine if the hot runner system is in an operable status, and in case the hot runner system is in an operable status, configured to generate status information to activate the one or more heating elements and/or the one or more actuators enabling a production operation of the injection molding machine. In case the hot runner system is not in an operable status, configured to generate status information to deactivate the one or more heating elements and/or close or deactivate the one or more actuators disabling a production operation of the injection molding machine.

First training data including a set value related to a molding machine, a measured value obtained by measuring a physical quantity related to molding, and a degree of quality of a molded product generated by the molding machine is collected, a first learning model for outputting a degree of quality of a molded product when a set value and a measured value are input is generated by machine learning based on collected first training data, second training data including a defect degree for each defect type of a molded product, a measured value, and a set value capable of reducing the defect degree is collected, and a second learning model for outputting a set value capable of reducing a defect degree when a defect degree and a measured value are input is generated by machine learning based on collected second training data and a degree of quality output from the first learning model.

An abnormality detection apparatus for detecting an abnormality of a ball screw provided in an injection molding machine includes a current sensor configured to detect an alternating current of a motor for applying a driving force to the ball screw; and a computation unit configured to detect an abnormality of the ball screw based on information related to a distribution of frequency components obtained by frequency analysis of an alternating current detected by the current sensor, and information related to a distribution of frequency components obtained by frequency analysis of an alternating current detected when the ball screw, which is normal, operates.

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 machine management device that is able to couple to a molding machine includes a storage unit configured to store, in chronological order, numerical value information on a molding process when a molded product is molded by the molding machine and four-factor information on four factors of production which includes a man, a machine, a material, and a method when the molded product is produced, and an output control unit configured to classify the numerical value information into a plurality of groups for periods divided according to time when the four factors of production changes, and output the numerical value information classified into the plurality of groups to an output unit in group units.

An injection molding system includes: an injection molding machine configured to operate in accordance with a command generated using a first language, and inject a molten material into a mold to mold a molded object; a robot configured to operate in accordance with a command generated using a second language, and convey the molded object; and a control device configured to control the injection molding machine and the robot. When determining that an error occurs in at least one of the injection molding machine and the robot, the control device transmits, to the injection molding machine, a command generated using the first language and for causing the injection molding machine to execute a return operation, and transmits, to the robot, a command interpretable by the second language and for causing the robot to execute a return operation, so as to cause the injection molding machine and the robot to execute the return operations.

A controller of an injection molding machine includes a detection part configured to detect whether or not a process in each process performed in the injection molding machine has started based on preset conditions in the process, and an output part configured to output waveform information representing a change of a performance value detected in the process or a subsequent process that is subsequent to the process when the detection part detects that the process has started.

An injection molding machine management system SYS according to an embodiment includes an injection molding machine and a management device provided outside the injection molding machine and that monitors an abnormality in the injection molding machine, in which in a case where an abnormality occurs in the injection molding machine, the management device automatically reboots the injection molding machine in a safe mode having a more limited function than a normal boot mode. In addition, an injection molding machine according to another embodiment includes a CPU and an FPGA that monitors an abnormality in a predetermined software executed by the CPU, in which in a case where an abnormality occurs in a system software executed by the CPU, the FPGA automatically reboots the system software of the CPU in a safe mode having a more limited function than the normal boot mode.

An abnormality detection apparatus for detecting an abnormality of a ball screw provided in an injection molding machine includes: a vibration sensor configured to detect vibration of the ball screw; and a computation unit configured to detect an abnormality of the ball screw based on information related to a distribution of frequency components obtained by frequency analysis of a vibration intensity detected by the vibration sensor, and information related to a distribution of frequency components obtained by frequency analysis of a vibration intensity detected when the ball screw, which is normal, operates.

An operation quantity determination device for determining an operation quantity related to a molding machine, includes an observation unit configured to acquire observation data obtained by observing a physical quantity related to molding by the molding machine when the molding is executed, a state expression unit configured to generate a state expression map expressing a state of the molding machine based on the observation data acquired by the observation unit, and an operation quantity output unit configured to output the operation quantity based on the state expression map generated by the state expression unit.