A time variation of an internal pressure of the molding cavity of a multi-phase injection molding machine is detected and represented as an internal pressure graph. An internal pressure graph recorded during a production cycle that produced an injection molded part satisfying a predefined quality characteristic is used as a reference graph. If the internal pressure graph of the current production cycle exceeds a predefined threshold value, then a current machine parameter is changed so as to adapt an internal pressure graph of a subsequent production cycle to the reference graph. Each phase of the production cycle is assigned its own machine parameter determined to have a significant impact on the quality of the parts produced. The assigned machine parameters are changed in a predefined order in a plurality of production cycles wherein exactly one assigned machine parameter is changed per production cycle.

Disclosed are a system and method tuning PET raw material processing process by employing real time process management and machine learning steps and reactive addition of dosing of homogenizing composition for impregnating chain extenders and compatibilizing agents in thermoplastic resin using the liquid additive as a carrier into the process for modifying material performance. The properties of PET blend are no longer fixed once dry-blending and melting is complete.

Provided is a glue injection device, comprising: a glue melting unit (400), the glue melting unit (400) comprising a rotatable turntable (402) and a fixing member (403), wherein a feeding groove is provided on the surface of the turntable (402) facing the fixing member (403) and the turntable (402) melts the raw material in the feeding groove into glue when rotating with respect to the fixing member (403); and a glue injection unit (500) connected with the glue melting unit (400). The glue is injected into the glue injection unit (500) when the turntable (402) is rotated, and the glue injection unit (500) is capable of injecting the glue therein into a mold.

A method of determining a target value of a state variable includes preparing a mixture of the molding material and the gas with different test values of the state variable, and the mixture is respectively compressed or decompressed. In the compression or decompression step, the state variable of the mixture and/or a further state variable of the mixture is measured directly or indirectly, and determination values of a compression behavior variable characteristic of the compression behavior of the mixture are determined from the measurement values. A criterion directed to a solution state of the gas is checked, and a lowest or highest value of the state variable is deduced at which the gas is in solution in the molding material. The lowest or highest value of the state variable at which the gas is in solution in the molding material is used as the target value.

An injection mold comprising a first mold half and a second mold half which during operation are displaceable with respect to each other in a first direction between an open position and a closed position in which the first mold half and the second mold half interact with each other. The first mold half comprises a cavity plate with a front face with a recess in which at least one cavity block comprising at least one first cavity half is arranged forming in a closed position of the injection mold with a corresponding second cavity half arranged at the second mold half a cavity suitable to receive plasticized plastic material to form a part therefrom. At least one cable interconnects the at least one cavity block in a mounted position with a connection box.

An injection molding device includes an injection machine, a molding die, and a high frequency oscillation device. The injection machine injects a resin material containing a dielectric heat generating material while keeping fluidity by temperature control. The molding die includes a cavity being a channel of flow of the resin material, and a pair of electrodes, each of which faces the cavity, the pair of electrodes being disposed to sandwich the resin material therebetween in a direction crossing a direction of the flow. The high frequency oscillation device applies a high frequency alternate-current voltage to the pair of electrodes.

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.

An injection molding apparatus (10) comprising a signal converter (1500) interconnected to a machine controller (MC) of an injection molding machine (IMM) that generates standardized signals (VPS), the signal converter (1500) receiving and converting the standardized signals (VS) to a command signal (MOPCS, PDCVS) that is compatible with a signal receptor or interface of an electrically powered actuator (940e, 941e, 942e) or a signal receptor, interface or driver of a proportional directional control valve (V, V1, V2) that drives a fluid driven actuator (940p, 941p, 942p) to respectively operate the electrically powered actuator (940e, 941e, 942e) or the proportional directional control valve (V, V1, V2) to move in a direction that operates to either begin an injection cycle and to end an injection cycle.

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.

A sensing module includes a hollow body, a first photo sensor, and a second photo sensor. The hollow body includes a cavity portion and an insertion portion connected to each other. The insertion portion has a first channel and a second channel. The first photo sensor is disposed in the cavity portion of the hollow body and corresponds to the first channel to sense an ambient temperature and a test object temperature. The second photo sensor is disposed in the cavity portion of the hollow body and corresponds to the second channel to sense the ambient temperature.