Resin transfer molding in composite manufacturing

1 . A resin transfer molding system comprising:

a resin transfer mold configured to mold a composite material via resin injection and curing;

at least one pressure sensor configured to measure an in-mold pressure of the resin transfer mold;

at least one dielectric sensor configured to measure an in-mold degree of cure value of the injected resin;

at least one resistance circuit configured to measure an in-mold flow front position of the injected resin; and

a molding process control module configured to:

determine a molding process anomaly status by comparing a specified anomaly threshold to at least one of pressure data obtained from the at least one pressure sensor, degree of cure data obtained from the at least one dielectric sensor, or flow front position data obtained from the at least one resistance circuit, wherein determining the molding process anomaly status includes supplying the pressure data, the degree of cure data, and the flow front position data to a trained machine learning model, and wherein the determination of the molding process anomaly status is based on an output of the trained machine learning model;

reduce an injection flow rate of the resin in response to a determination of a molding process anomaly; and

maintain the injection flow rate in response to a determination of normal molding process operation without the molding process anomaly.

2 . The resin transfer molding system of claim 1 , wherein the molding process control module is configured to, subsequent to reducing the injection flow rate:

determine the molding process anomaly status by comparing the specified anomaly threshold to at least one of the pressure data, degree of cure data, or flow front position data; and

increase the injection flow rate in response to a determination of normal molding process operation without the molding process anomaly.

3 . The resin transfer molding system of claim 1 , wherein determining the molding process anomaly status includes comparing the specified anomaly threshold to at least two of the pressure data, degree of cure data, or flow front position data.

4 . The resin transfer molding system of claim 1 , wherein determining the molding process anomaly status includes comparing the specified anomaly threshold to all three of the pressure data, degree of cure data, and flow front position data.

5 . The resin transfer molding system of claim 1 , wherein the molding process control module is configured to:

obtain a training data set including multiple mold quality index values each corresponding to at least one of pressure data obtained from the at least one pressure sensor, degree of cure data obtained from the at least one dielectric sensor, or flow front position data obtained from the at least one resistance circuit; and

train a machine learning model, using the training data set, to generate the trained machine learning model.

6 . The resin transfer molding system of claim 5 , wherein each of the multiple mold quality index values is obtained via at least one of an X-ray image or a thermography image of a composite material subsequent to curing.

7 . The resin transfer molding system of claim 1 , wherein:

the at least one pressure sensor includes a pressure sensor flush mounted in a cavity of the resin transfer mold;

the at least one dielectric sensor is configured to measure an ionic viscosity of the resin in the cavity; and

the at least one resistance circuit includes resistance circuitry extending across at least eighty percent of a longitudinal direction of the cavity.

8 . The resin transfer molding system of claim 7 , wherein the molding process control module is configured to:

close a press of the resin transfer mold to a mold position, and apply tonnage to the press;

inject an epoxy or a polyurethane as the resin into the cavity of the resin transfer mold; and

execute a curing cycle subsequent to injecting the epoxy or polyurethane into the cavity of the resin transfer mold.

9 . The resin transfer molding system of claim 1 , further comprising a human-machine interface configured to display a training mode option and an operation mode option for selection by a user, wherein:

the human-machine interface is configured to receive an input to set the specified anomaly threshold in response to selection of the operation mode option; and

the human-machine interface is configured to receive an input to specify one or more sensor sources to train the trained machine learning model in response to selection of the training mode option.

10 . A method for controlling a resin transfer molding process, the method comprising:

executing a resin transfer molding process to mold a composite material via resin injection and curing in a resin transfer mold;

measuring, by at least one pressure sensor, an in-mold pressure of the resin transfer mold;

measuring, by at least one dielectric sensor, an in-mold degree of cure value of the injected resin;

measuring, by at least one resistance circuit, an in-mold flow front position of the injected resin;

determining a molding process anomaly status by comparing a specified anomaly threshold to at least one of pressure data obtained from the at least one pressure sensor, degree of cure data obtained from the at least one dielectric sensor, or flow front position data obtained from the at least one resistance circuit, wherein determining the molding process anomaly status includes supplying the pressure data, the degree of cure data, and the flow front position data to a trained machine learning model, and wherein the determination of the molding process anomaly status is based on an output of the trained machine learning model;

reducing an injection flow rate in response to a determination of a molding process anomaly; and

maintaining the injection flow rate in response to a determination of normal molding process operation without the molding process anomaly.

11 . The method of claim 10 , further comprising, subsequent to reducing the injection flow rate:

determining the molding process anomaly status by comparing the specified anomaly threshold to at least one of the pressure data, degree of cure data, or flow front position data; and

increasing the injection flow rate in response to a determination of normal molding process operation without the molding process anomaly.

12 . The method of claim 10 , wherein determining the molding process anomaly status includes comparing the specified anomaly threshold to at least two of the pressure data, degree of cure data, or flow front position data.

13 . The method of claim 10 , wherein determining the molding process anomaly status includes comparing the specified anomaly threshold to all three of the pressure data, degree of cure data, and flow front position data.

14 . The method of claim 10 , further comprising:

obtaining a training data set including multiple mold quality index values each corresponding to at least one of pressure data obtained from the at least one pressure sensor, degree of cure data obtained from the at least one dielectric sensor, or flow front position data obtained from the at least one resistance circuit; and

training a machine learning model, using the training data set, to generate the trained machine learning model.

15 . The method of claim 14 , wherein each of the multiple mold quality index values is obtained via at least one of an X-ray image or a thermography image of a composite material subsequent to curing.

16 . The method of claim 10 , wherein:

the at least one pressure sensor includes a pressure sensor flush mounted in a cavity of the resin transfer mold;

the at least one dielectric sensor is configured to measure an ionic viscosity of the resin in the cavity; and

the at least one resistance circuit includes resistance circuitry extending across at least eighty percent of a longitudinal direction of the cavity.

17 . The method of claim 16 , further comprising:

closing a press of the resin transfer mold to a mold position, and apply tonnage to the press;

injecting an epoxy or a polyurethane as the resin into the cavity of the resin transfer mold; and

executing a curing cycle subsequent to injecting the epoxy or polyurethane into the cavity of the resin transfer mold.

18 . The method of claim 10 , further comprising:

displaying, on a human-machine interface, a training mode option and an operation mode option for selection by a user;

receiving an input to set the specified anomaly threshold in response to selection of the operation mode option; and

receiving an input to specify one or more sensor sources to train the trained machine learning model in response to selection of the training mode option.