A computer implemented method for generating a mold model for production predictive control and computer program products thereof. The method comprises receiving first parameters about molding machine sensors and second parameters about mold cavity; classifying each injection cycle of a plurality of injection cycles of a first injection molding machine considering the first and second parameters and quality or characteristics of injected given parts in the machine; processing the first and second parameters to remove undesired or irregular data values thereof; merging the first and second parameters providing a global group of processed parameters; executing a machine learning algorithm on the global group of processed parameters generating an extended mold model; and using said generated extended mold model for further monitoring and control of the mold in further injection processes in the first injection molding machine and/or for optimizing a production process of the mold in the first molding machine.

To provide a quality prediction system predicting a quality element of a molded item using machine learning. The quality prediction system includes a sensor disposed in the mold and configured to detect state data regarding the molten material supplied in the cavity, a learned-model storage unit configured to store a model which is a learned model generated by machine learning in which the state data detected by at least the sensor is used as a training data set and is a learned model related to the state data and a quality element of the molded item, and a quality prediction unit configured to predict the quality element of the molded item which is newly molded based on the state data newly detected by the sensor and the learned model.

The present application relates to an injection molding system, comprising: an input condition storage section 23 to store an input plasticization condition; an ideal plasticization state storage section 24 to store information of an ideal state of plasticization; an input condition simulation section 29 for the plasticization condition based on data stored in the input condition storage section 23; a comparison section 25 to compare the information stored in the ideal plasticization state storage section 24; an optimal condition analysis and calculation section 26 to calculate the optimal plasticization condition when the data stored in the input condition storage section 23 is decided not to be the ideal state of plasticization by the comparison section 25 using the data of the input condition storage section 23 and the information stored in the ideal plasticization state storage section 24; and a condition change indicating section 28 to display information necessary to make a change to the optimal plasticization condition on a display section 22 based on a result of calculation by the optimal condition determination section 26.

A model-based machine learning system for calculating optimum molding conditions includes a data storage device providing a set of training data; an injection molding process emulator producing a set of emulated sensing data according to molding conditions as inputted; an injection molding process state observation unit, determining an injection molding process state from molding conditions, sensing data and a quality state, wherein the quality state at least includes an acceptance state; and an injection molding process optimization unit including an injection molding condition optimizer, wherein a molding condition optimization model constructed in the injection molding condition optimizer is trained according to the injection molding process state as determined, and the molding condition optimization model after training is introduced into an injection molding production line.

A method for the variothermal temperature control of an injection mould using a temperature control device, the method including at least the following steps: in a learning phase, determining a temperature control characteristic of the temperature-controllable system including at least the injection mould and the temperature control device, in order to obtain individual reference values for the system, with which the temperature control device can be controlled in order to obtain a nominal temperature profile; and in a production phase: temperature control of the injection mould with the reference values determined during the learning phase; determining deviations of an actual temperature profile of the injection mould in relation to the nominal temperature profile during the production cycle and calculating corrected reference values from these deviations; and carrying out a resulting production process using the corrected reference values.

A state determination device acquires data on an injection molding machine and stores conditions for classifying the acquired data on the injection molding machine and a plurality of learning models. The state determination device further classifies the acquired data based on the stored classification conditions and settles a learning model to which the classified data are applied, among the plurality of stored learning models. Subsequently, the state determination device performs machine learning for the learning model settled as an application destination, based on the classified data.

A failure cause diagnostic device of the present invention receives input of internal and external state data on injection molding machines and diagnoses failure cause of the injection molding machines by means of a machine learning device. An internal parameter of the machine learning device is obtained by performing machine learning using the state data obtained from the injection molding machines subject to failure cause and the state data obtained from the injection molding machines free of failure cause.

A state determination device is capable of assisting maintenance for various injection molding machines. The state determination device acquires data related to an injection molding machine, performs numeric conversion for extracting a feature in a temporal direction or an amplitude direction, with respect to time-series data of physical quantity in the acquired data, and performs machine learning using the data obtained through numeric conversion so as to generate a learning model.

A state determination device that determines an operation state of an injection molding machine stores respective specification data of a reference injection molding machine and an injection molding machine that is different from the reference injection molding machine, and acquires data related to the injection molding machine. Then, the state determination device converts the acquired data into yardstick data by a conversion formula set for every type of data, by using the stored specification data of the reference injection molding machine and the stored specification data of the injection molding machine and performs machine learning using the yardstick data obtained through the conversion so as to generate a learning model.

A device for assisting molding condition determination is used with a molding method that molds an article by feeding molten material into a mold. The device includes a learning model generating unit, an input unit, and an output unit. The learning model generating unit creates a learning model through machine learning in which a plurality of molding condition element items used to mold the article and a plurality of quality element items of the molded article are used as learning data. The learning model relates to a degree of influence of each molding condition element item on each quality element item. The input unit receives input of a subject quality element item to be checked, selected from the quality element items. The output unit outputs, using the learning model, the multiple molding condition element item that has the degree of influence on the subject quality element item.