An image display device displays an image such that a pair of reference symmetric structure portions, e.g. a pair of guide pins, are included in a captured image displayed on a display section, the pair of reference symmetric structure portions being located symmetrically with respect to a second virtual plane which extends in the opening direction and the vertical direction and includes a virtual center line of the molding apparatus which extends in the opening direction. A teaching execution section is used to determine the lateral position of an approach frame in the take-out operation by changing the position of the approach frame such that the center of an imaging range is located at a middle position between the pair of reference symmetric structure portions included in the captured image.

The present invention discloses a model-free optimization method of process parameters of injection molding to solve the problems of frequent tests required and performing adaptive adjustment on different parameters in the existing optimization method. The method need not build a surrogate model between a product quality index and a process parameter to render the process parameter to converge nearby the optimal solution by an on-line iteration method. The present invention calculates the gradient direction of a current point by an iterative gradient estimation method, and uses adaptive moment estimation algorithm to allocate an adaptive step for each parameter. The method can significantly reduce the cost and time required in the process parameter, which greatly helps improving the optimization efficiency of process parameters of injection molding.

A method for the automatic process monitoring and/or process diagnosis of a piece-based process, in particular a production process, in particular an injection-molding process, including the steps: a) performing an automated reference finding in order to obtain reference values (r.sub.1 . . . r.sub.n) from values (x.sub.0 . . . x.sub.j) of at least one process variable; b) performing an anomaly detection on the basis of the reference values (r.sub.1 . . . r.sub.n) found in step (a); c) performing an automated cause analysis and/or an automated fault diagnosis on the basis of a qualitative model of process relationships and/or on the basis of dependencies of various process variables on each other.

Provided is an injection molding machine system (1) that performs control of molding conditions in an injection molding machine (2) by an agent (6) including a machine learning device which performs reinforcement learning. In the present learning, physical data obtained from the injection molding machine (2) and a defect type indicating the type of a molding defect in a molded article are used as states, molding conditions are used as actions, and a defect state indicating the defect level of the molding defect is used as a reward.

A method and system for co-injection molding of two molten plastic materials that allows monitoring and utilization of injection pressure and optionally melt pressure and/or flow front pressure during an injection run. A controller alters the injection pressure so as to achieve and maintain optimal or desired ratios of injection pressure, and optionally melt pressure and/or flow front pressure, of the two molten plastic materials. This allows for more precise part manufacture, including reducing the thickness of a skin or shell layer compared to a core layer of a molded part.

Predicting a state of a mold after molding upon injection molding. A machine learning device includes: an input data acquiring unit that acquires input data including any molding condition including at least a type of resin, a type of additive, a blending ratio of the additive, and a temperature of the resin in molding any article molded by any injection molding machine, and state information indicating a wear amount of a mold before molding at the molding conditions; a label acquiring unit that acquires label data indicating state information of the mold after molding at the molding conditions included in the input data; and a learning unit that executes supervised learning using the input data acquired by the input data acquiring unit and the label data acquired by the label acquiring unit, and generates a learned model.

A method of generating the analysis conditions of an injection molding machine by using at least one computer, the at least one computer executing the steps of: selecting one of injection molding machines, each being associated with a predetermined correction amount of injection molding; generating a second analysis condition for the selected injection molding machine on the basis of an acquired first analysis condition and the predetermined correction amount of the selected injection molding machine; and outputting the generated second analysis condition.

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.

A state determination device acquires data on an industrial machine, creates a plurality of pieces of partial time-series data obtained by sliding time-series data on physical quantities out of the data on the industrial machine in the direction of a time axis, based on the acquired data, extracts a plurality of pieces of data for learning including the plurality of pieces of partial time-series data, and performs machine learning using the extracted learning data, thereby generating a learning model.

A state determination device acquires data on an industrial machine, extracts data used for processing related to machine learning from the acquired data, out of the acquired data, according to an extraction condition for extracting the data, and executes the processing related to the machine learning using the extracted data.