# US 2024/0201640 A1 · Real Time Characteristic Prediction for Unconsolidated Composite Materials > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0201640 A1 - Document ID: US20240201640A1 - Status: Published application - Kind: A1 - Application number: 18/416,467 - Filed: 2024-01-18 - Published: 2024-06-20 - Earliest claimed: 2022-07-08 - Assignees: The Boeing Company - Applicants: The Boeing Company - Inventors: David Williamson Shahan; Rashmi Sundareswara; Tiffany Allison Stewart; Yuksel Can Yabansu; Andrew L. Bauer; Gwen Marie Lanphere Gross - Web page: https://patentgrep.com/patent/US20240201640A1 ## CPC - G05B 13/048: {using a predictor} - G05B 13/0265: {the criterion being a learning criterion} - G05B 13/042: {in which a parameter or coefficient is automatically adjusted to optimise the performance} - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract A method, apparatus, system, and computer program product for predicting a number of characteristics of an unconsolidated composite material. Sensor data is received in real time from a composite material manufacturing system during manufacturing of an unconsolidated composite material. Initial predictions are generated for a number of characteristics of the unconsolidated composite material in a completed form in real time during manufacturing of the unconsolidated composite material in the composite material manufacturing system using a number of physics-based models and a number of machine learning models trained using data. A final prediction is determined in real time for the number of characteristics based on the initial predictions. ## Claims ### Claim 1 A characteristic prediction system comprising: a computer system; and prediction manager located in the computer system, wherein the prediction manager is configured to: generate initial predictions for a number of characteristics of an unconsolidated composite material in a completed form in real time during manufacturing of the unconsolidated composite material in the composite material manufacturing system using sensor data received in real time from a sensor system for a composite material manufacturing system during manufacturing of the unconsolidated composite material, a number of physics-based models, and a number of machine learning models, wherein the number of machine learning models is trained to generate a number of the initial predictions using the sensor data; determine uncertainties for the initial predictions for the number of characteristics of the unconsolidated composite material in the completed form from the number of physics-based models and the number of machine learning models in real time during manufacturing of the unconsolidated composite material; associate weights with the initial predictions for the number of characteristics using the uncertainties; and determine a final prediction for the number of characteristics based on the initial predictions and the weights associated with the initial predictions. ### Claim 2 (depends on 1) The characteristic prediction system of claim 1 further comprising: a controller that is configured to adjust a number of processing conditions for the composite material manufacturing system based on the final prediction. ### Claim 3 (depends on 2) The characteristic prediction system of claim 2, wherein the number of processing conditions is selected from at least one of a material input to the composite material manufacturing system, a setting for the composite material manufacturing system. ### Claim 4 (depends on 1) The characteristic prediction system of claim 1, wherein the number of physics-based models is selected from at least one of thermal model, an infiltration model, a rheological model, a permeability model that calculates a level of resin filtration, a thickness model that calculates a reduction of a thickness of a composite prepreg, or a resin infiltration model that calculates the level of resin filtration. ### Claim 5 (depends on 1) The characteristic prediction system of claim 1, wherein the number of machine learning models is selected from at least one of a first machine learning model trained using historical sensor data from a number of composite material manufacturing systems during manufacturing of unconsolidated composite materials, a second machine learning model trained using the historical sensor data and historical processing conditions, or a third machine learning model trained using the historical processing conditions and a number of historical characteristics. ### Claim 6 (depends on 1) The characteristic prediction system of claim 1, wherein the uncertainties are generated by the number of machine learning models as part of generating the initial predictions. ### Claim 7 (depends on 1) The characteristic prediction system of claim 1, in determining the final prediction, the prediction manager is configured to: determine the final prediction for the number of characteristics based on the initial predictions and the weights associated with the initial predictions using at least one of a voting regression, a stacking regression, or a bagging estimator. ### Claim 8 (depends on 1) The characteristic prediction system of claim 1, wherein the number of characteristics is selected from at least one of a fiber areal weight, a resin content, a prepreg thickness, a prepreg infiltration level, a prepreg tack level, a prepreg fiber areal weight, a resin film areal weight, a resin film thickness, a release liner areal weight, a release liner thickness, or a laminate structural property. ### Claim 9 (depends on 1) The characteristic prediction system of claim 1, wherein the sensor data comprises characteristics data and production data. ### Claim 10 (depends on 1) The characteristic prediction system of claim 1, wherein the composite material manufacturing system is one of a carbon fiber prepreg coating line, carbon fiber prepreg coating line, a reinforcing fiber resin impregnation line, a resin mix process system, a resin filming line, a prepreg system, and a slitting process system. ### Claim 11 A characteristic prediction system comprising: a computer system; and prediction manager located in the computer system, wherein the prediction manager is configured to: receive sensor data in real time from a sensor system for a composite material manufacturing system during manufacturing of an unconsolidated composite material; generate initial predictions for a number of characteristics of the unconsolidated composite material in a completed form in real time during manufacturing of the unconsolidated composite material in the composite material manufacturing system using a number of physics-based models and a number of machine learning models trained to generate the initial predictions using the sensor data; and determine a final prediction in real time for the number of characteristics based on the initial predictions. ### Claim 12 (depends on 11) The characteristic prediction system of claim 11, wherein the prediction manager is configured to: receive uncertainties for the initial predictions for the number of characteristics of the unconsolidated composite material from the number of physics-based models and the number of machine learning models in real time during manufacturing of the unconsolidated composite material; and associate weights with the initial predictions for the number of characteristics using the uncertainties, wherein in determining the final prediction, the prediction manager is configured to: determine the final prediction for the number of characteristics based on the initial predictions and the weights associated with the initial predictions. ### Claim 13 (depends on 11) The characteristic prediction system of claim 11, wherein the number of physics-based models is selected from at least one of thermal model, an infiltration model, a rheological model, a permeability model that calculates a level of resin filtration, a thickness model that calculates a reduction of a thickness of a composite prepreg, or a resin infiltration model that calculates the level of resin filtration. ### Claim 14 (depends on 11) The characteristic prediction system of claim 11, wherein the number of machine learning models is selected from at least one of a first machine learning model trained using historical sensor data from a number of composite material manufacturing systems during manufacturing of unconsolidated composite materials, a second machine learning model trained using the historical sensor data and historical processing conditions, or a third machine learning model trained using the historical processing conditions and a number of historical characteristics. ### Claim 15 A method for predicting a number of characteristics of an unconsolidated composite material, the method comprising: generating initial predictions for a number of characteristics of the unconsolidated composite material in a completed form in real time during manufacturing of the unconsolidated composite material in the composite material manufacturing system using sensor data received in real time from a sensor system for a composite material manufacturing system during manufacturing of the unconsolidated composite material, a number of physics-based models, and a number of machine learning models, wherein the number of machine learning models is trained to generate a number of the initial predictions using the sensor data; determining uncertainties for the initial predictions for the number of characteristics of the unconsolidated composite material in the completed form from the number of physics-based models and the number of machine learning models in real time during manufacturing of the unconsolidated composite material; associating weights with the initial predictions for the number of characteristics using the uncertainties; and determining a final prediction for the number of characteristics based on the initial predictions and the weights associated with the initial predictions. ### Claim 16 (depends on 15) The method of claim 15 further comprising: adjusting a number of processing conditions for the composite material manufacturing system based on the final prediction. ### Claim 17 (depends on 16) The method of claim 16, wherein the number of processing conditions is selected from at least one of a material input to the composite material manufacturing system, a setting for the composite material manufacturing system. ### Claim 18 (depends on 15) The method of claim 15, wherein the number of physics-based models is selected from at least one of thermal model, an infiltration model, a rheological model, a permeability model that calculates a level of resin filtration, a thickness model that calculates a reduction of a thickness of a composite prepreg, or a resin infiltration model that calculates the level of resin filtration. ### Claim 19 (depends on 15) The method of claim 15, wherein the number of machine learning models is selected from at least one of a first machine learning model trained using historical sensor data, a second machine learning model trained using the historical sensor data and historical processing conditions, or a third machine learning model trained using the historical processing conditions and a number of historical characteristics. ### Claim 20 (depends on 19) The method of claim 19, wherein the historical sensor data comprises historical data and production data. ### Claim 21 (depends on 15) The method of claim 15, wherein the uncertainties are generated by the number of machine learning models as part of generating the initial predictions. ### Claim 22 (depends on 15) The method of claim 15, wherein determining the final prediction comprises: determining the final prediction for the number of characteristics based on the initial predictions and the weights associated with the initial predictions using at least one of a voting regression, a stacking regression, or a bagging estimator. ### Claim 23 (depends on 15) The method of claim 15, wherein the number of characteristics is selected from at least one of a fiber areal weight, a resin content, a prepreg thickness, a prepreg infiltration level, a prepreg tack level, a prepreg fiber areal weight, a resin film areal weight, a resin film thickness, a release liner areal weight, a release liner thickness, or a laminate structural property. ### Claim 24 (depends on 15) The method of claim 15, wherein the sensor data comprises characteristics data and production data. ### Claim 25 (depends on 15) The method of claim 15, wherein the composite material manufacturing system is a carbon fiber prepreg coating line, a reinforcing fiber resin impregnation line, a resin mix process system, a resin filming line, a prepreg system, and a slitting process system. ### Claim 26 A method for predicting a number of characteristics of an unconsolidated composite material, the method comprising: receiving sensor data in real time from a composite material manufacturing system during manufacturing of an unconsolidated composite material; generating initial predictions for a number of characteristics of the unconsolidated composite material in a completed form in real time during manufacturing of the unconsolidated composite material in the composite material manufacturing system using a number of physics-based models and a number of machine learning models trained using data; and determining a final prediction in real time for the number of characteristics based on the initial predictions.