CLAIMS

[Claim 1] An injection molding method comprising the steps of:

constructing a prediction model on the basis of an input parameter including a molding condition for a molding product and an objective variable value including a quality value that quantifies a required quality of the molding product with respect to the input parameter;

inferring a predictive distribution of the objective variable value with respect to the input parameter, using the prediction model; and

deriving such a molding condition that satisfies the required quality of the molding product, by a Bayesian optimization method utilizing a regression model for obtaining the input parameter that yields a quality value highest in evaluation of the objective variable value as compared to an initial quality value, on the basis of the predictive distribution.

[Claim 2] The injection molding method according to claim 1, wherein

at least one of a direct quality value obtained by directly measuring the molding product and an indirect quality value including a feature quantity converted from data of a sensor provided in a mold of an injection molding machine or an outer appearance image of the molding product, is used for deriving the molding condition.

[Claim 3] The injection molding method according to claim 1 or 2, wherein

the regression model is a Gaussian process regression model.

[Claim 4] A molding condition derivation device for performing adjustment of a molding condition for a molding product on the basis of the injection molding method according to any one of claims 1 to 3, the molding condition derivation device comprising:

a storage unit in which information about ‘the molding condition and a required quality of the molding product is stored in advance; and

a control processing unit, wherein the control processing unit includes

a direct quality value processing unit which obtains a direct quality value by directly measuring the molding product,

an indirect quality value processing unit which obtains an indirect quality value including a feature quantity converted from data of a sensor provided in a mold
of an injection molding machine or an outer appearance image of the molding product, and

a molding-condition adjustment unit which takes in, as a quality value, at least one of the direct quality value from the direct quality value processing unit or the indirect quality value from the indirect quality value processing unit, and derives such a molding condition that satisfies an optimum required quality of the molding product, by a Bayesian optimization method utilizing a regression model, using the quality value that has been taken in and the information about the molding condition and the required quality stored in the storage unit.

[Claim 5] The molding condition derivation device according to claim 4, wherein

the molding-condition adjustment unit includes a prediction model construction unit which constructs a prediction model on the basis of the input parameter including the molding condition for the molding product and the objective variable value including the quality value that quantifies the required quality of the molding product with respect to the input parameter,

a predictive distribution inference unit which infers a predictive distribution of the objective variable value with respect to the input parameter, using the
prediction model, and a molding condition derivation unit which derives such a molding condition that satisfies the required quality of the molding product, by the Bayesian optimization method utilizing the regression model for obtaining the input parameter that yields a quality value highest in evaluation of the objective variable value as compared to an initial quality value, on the basis of the predictive distribution.

[Claim 6] A computer-readable storage medium having stored therein a computer program configured to, when the computer program is executed by a processor, execute the steps of:

constructing a prediction model on the basis of an input parameter including a molding condition for a molding product and an objective variable value including a quality value that quantifies a required quality of the molding product with respect to the input parameter;

inferring a predictive distribution of the objective variable value with respect to the input parameter, using the prediction model; and

deriving such a molding condition that satisfies the required quality of the molding product, by a Bayesian optimization method utilizing a regression model for obtaining the input parameter that yields a quality value highest in evaluation of the objective variable value as compared to an initial quality value, on the basis of the predictive distribution.

[Claim 7] The computer-readable storage medium according to claim 6, wherein

at least one of a direct quality value obtained by directly measuring the molding product and an indirect quality value including a feature quantity converted from data of a sensor provided in a mold of an injection molding machine or an outer appearance image of the molding product, is used for deriving the molding condition.

[Claim 8] The computer-readable storage medium according to claim 6 or 7, wherein

the regression model is a Gaussian process regression model.

[Claim 9] An injection molding method comprising the steps of:

constructing a prediction model on the basis of an input parameter including a molding condition for a molding product, and objective variable values including a feature quantity of a sensor value of a sensor provided to an injection molding machine with respect to the input parameter and a similarity of the sensor value when the molding condition for the molding product is changed with respect to
a reference sensor value which is the sensor value when the molding product satisfies a required quality;

inferring predictive distributions of the objective variable values with respect to the input parameter, using the prediction model; and

deriving such a molding condition that satisfies the required quality of the molding product, by a Bayesian optimization method utilizing a regression model for obtaining the input parameter with which evaluation of the objective variable values indicates becoming closer to a feature quantity of the reference sensor value as compared to a feature quantity of an initial sensor value, on the basis of the predictive distributions.

[Claim 10] The injection molding method according to claim 9, wherein

feature quantities of the sensor value in an xl direction, an x2 direction, . . . , and an xN direction of an INI-dimensional coordinate system (N is an integer not less than 2) which are obtained from the sensor value, and the similarity of the sensor value with respect to the reference sensor value, are used for deriving the molding condition.