Abstract: A method for controlling injection molding using a neural network in a control device of an injection molding machine. A measurement monitor value is acquired in a measurement step during test injection molding and an injection monitor value is acquired in an injection step. The acquired measurement monitor value is designated as an input term and the injection monitor value is designated as an output term. A prediction function that incorporates the measurement monitor value is then determined using the neural network. A first value corresponding to the injection monitor value is predicted by substituting into the prediction function a measurement monitor value acquired at completion of a measurement step during mass-production injection molding. On the basis of the predicted first value, a second value corresponding to an injection condition is determined. Injection control and pressure maintenance control are then implemented on the basis of the second value corresponding to the injection condition.

Abstract: A support apparatus of an injection-molding machine has a neural network that receives test molding data corresponding to molding conditions and a quality value obtained by measuring a non-defective molded article, and that determines a quality prediction function based on the received test molding data. A computer calculates a predicted value of the quality value using the quality prediction function. An input apparatus inputs into the neural network fixed values for the molding conditions except for a selected at least one of the molding conditions, and inputs a target value of the quality value. A graph generator generates a graphical relationship between the selected at least one molding condition and the predicted value. A graph correction unit corrects the graphical relationship generated by the graph generator on the basis of the target value.

Abstract: The present invention relates to a conductive resin molded product having an insulating skin produced by a composite comprising a non-conductive resin and conductive material. The invention also provides a method for producing the conductive resin molded product.

Abstract: An apparatus for supporting a worker involved in operating an injection-molding machine includes a neural network in which a prediction function is determined based on test molding. The worker enters a portion of a plurality of molding conditions in the form of a fixed value using a first input apparatus, thereby obtaining a relational graph of the remaining portion of the plurality of molding conditions and the predicted quality value in which the quality of a molded article is predicted.

Abstract: An injection molding control method that uses a neural network is disclosed. The control method determines an injection condition according to a prediction function, a first value, and a second value. The prediction function is implemented in test molding, and the first value and second value are implemented in a measurement step that has a time margin. The injection condition predicted for mass-production molding is determined prior to the injection step, and the need for computation during the injection step is thereby eliminated.

Abstract: A provisional temperature estimating mathematical expression is set in accordance with which plasticizing conditions used as terms are to be multiplied by unknown coefficients. Then, a given number of experiments not smaller than the number of the unknown coefficients are conducted while the plasticizing conditions are varied, so as to acquire various experimental data. The unknown coefficients are converted into known coefficient in accordance with the acquired experimental data so as to provide a complete temperature estimating mathematical expression, on the basis of which a plasticized resin temperature and noise-outlet resin temperature are determined arithmetically.

Abstract: To control the thickness of the skin layer of a composite resin molded product by a viscosity difference obtained by varying the viscosity by adding another ultra-minute substance to a raw resin material. The composite resin molded product comprising the skin layer formed of a first thermoplastic resin and the core layer formed of a second thermoplastic resin is molded by injection. The viscosity difference between the resins is controlled by adding a carbon nanomaterial to one or the other of the thermoplastic resins. The thickness of the skin layer is controlled by the viscosity difference. The first thermoplastic resin and the second thermoplastic resin are of the same kind or different kinds of resins.

Abstract: Test molding and mass-production molding are performed by an injection molding machine that includes a control apparatus in which neural networks are used. A quality prediction function determined based on the test molding is revised as necessary during mass-production molding.

Abstract: A manufacturing method is provided to be used in place of a conventional mechanical alloying method. A powdered metal and a nanocarbon material are placed in an empty metal mill vessel containing no balls, and a mixture in which the powdered metal is coated with this nanocarbon material is obtained by shaking in three dimensions.

Abstract: Test molding and mass-production molding are performed by an injection molding machine that includes a control apparatus in which neural networks are used. A quality prediction function determined based on the test molding is revised as necessary during mass-production molding.

Abstract: A carbon nano material and a resin binder are plasticized and injection molded to form a preliminarily molded member. The preliminarily molded member is degreased by a heat treatment and made to a preliminarily molded porous member composed of the carbon nano material. The preliminarily molded porous member is inserted into a cavity of a product mold. A molten low melting point metal is injected into and fills the cavity. The preliminarily molded porous member is impregnated with the low melting point metal by injection pressure, thereby a composite metal product composed of the low melting point metal material integrally composited with the carbon nano material is molded. With the above arrangement, the characteristics of the carbon nano material are applied to the composite metal product to improve the functions thereof.

Abstract: A low melting point metal material is made to a thixotropic state in which liquid phases and solid phases coexists. In the thixotropic state of the low melting point metal material, a carbon nano material is kneaded with the low melting point metal material and forms a composite material. Thus obtained composite material is supplied to a metal molding machine and injected into a mold in a thixotropic state or a completely molten state of the metal so that the composite material fills the mold, thereby the composite material is molded to a composite metal product. With the above process, it is possible to injection mold the composite metal product to which the characteristics of the carbon nano material are applied.

Abstract: To control the thickness of the skin layer of a composite resin molded product by a viscosity difference obtained by varying the viscosity by adding another ultra-minute substance to a raw resin material.

Abstract: To make the surface of a conductive resin molded product nonconductive with the resin by adopting a carbon nano material as a conductive compounding material, and, accordingly, to make it possible to expand the use of the conductive resin molded product and use it as a base material of electronic equipment.

Abstract: A provisional temperature estimating mathematical expression is set in accordance with which plasticizing conditions used as terms are to be multiplied by unknown coefficients. Then, a given number of experiments not smaller than the number of the unknown coefficients are conducted while the plasticizing conditions are varied, so as to acquire various experimental data. The unknown coefficients are converted into known coefficient in accordance with the acquired experimental data so as to provide a complete temperature estimating mathematical expression, on the basis of which a plasticized resin temperature and noise-outlet resin temperature are determined arithmetically.

Abstract: A carbon nano material is mixed with a metal material in a powder state. A resultant mixed material is compressed by a hot press and molded to granules. The metal in the granules are melted, blended and thus obtained composite material is injected into and fill a mold to form a composite metal product comprising the carbon nano material and the metal material. With the above process, it is possible to injection mold said granules to obtain the composite metal product to which the characteristics of the carbon nano material are applied.

Abstract: A carbon nano material and a resin binder are plasticized and injection molded to form a preliminarily molded member. The preliminarily molded member is degreased by a heat treatment and made to a preliminarily molded porous member composed of the carbon nano material. The preliminarily molded porous member is inserted into a cavity of a product mold. A molten low melting point metal is injected into and fills the cavity. The preliminarily molded porous member is impregnated with the low melting point metal by injection pressure, thereby a composite metal product composed of the low melting point metal material integrally composited with the carbon nano material is molded. With the above arrangement, the characteristics of the carbon nano material are applied to the composite metal product to improve the functions thereof.

Abstract: Molding conditions of an injection molding machine are set through performance of virtual molding by CAE. First, actual molding is performed under provisional molding conditions in order to obtain an actual profile showing variation in load pressure actually measured during at least an injection step of the actual molding. Further, virtual molding is performed by CAE under the provisional molding conditions in order to obtain a virtual profile showing variation in load pressure simulated during at least an injection step of the virtual molding. The provisional molding conditions are changed by CAE in such a manner that the virtual profile coincides with the actual profile, to thereby obtain intermediate molding conditions. Subsequently, the intermediate molding conditions are optimized so as to obtain molding conditions for the injection molding machine.

Abstract: In a method for controlling an injection molding machine, a screw is advanced from an injection start position by means of velocity control, and when the screw reaches a control changeover point, pressure control is effected in place of the velocity control to apply pressure to the screw. Preliminary molding is performed to obtain an acceptable product; a total charged amount of resin during the preliminary mold is obtained on the basis of a movement distance over which the screw has moved before reaching the control changeover point during the preliminary mold; and the total charged amount is stored as a changeover target value. During ordinary molding, a total charged amount of resin is obtained on the basis of a movement distance over which the screw advances from the injection start position; and pressure control is started when the total charged amount reaches the changeover target value.

Abstract: Molding conditions of an injection molding machine are set through performance of virtual molding by CAE. First, actual molding is performed under provisional molding conditions in order to obtain an actual profile showing variation in load pressure actually measured during at least an injection step of the actual molding. Further, virtual molding is performed by CAE under the provisional molding conditions in order to obtain a virtual profile showing variation in load pressure simulated during at least an injection step of the virtual molding. The provisional molding conditions are changed by CAE in such a manner that the virtual profile coincides with the actual profile, to thereby obtain intermediate molding conditions. Subsequently, the intermediate molding conditions are optimized so as to obtain molding conditions for the injection molding machine.