Generative AI Agents for Composite Curing Process Design
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Solution Overview
Problem
Traditional methods for designing curing processes for composite materials are complex, expertise-intensive, and time-consuming, often relying on first-principles modeling, data-driven approaches, or hybrid models that struggle with predicting parameters outside their training range and adapting to dynamic conditions.
Innovation Solution
A computer-implemented system using Generative Artificial Intelligence (Gen AI) that includes multiple agents for requirement gathering, operations specialist, material specialist, design specialist, knowledge processing, experiment enabler, experiment designer, predictive model design, and predictive model optimizer to automate the design of curing processes, optimize experiment generation, and continually update predictive models in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional first-principles modeling is used for designing curing processes, then the model can capture the full complexity of curing processes, but it requires extensive domain expertise and is time and compute intensive
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the curing process that replicates the complex physical-chemical transformations. This digital twin can be simulated and optimized computationally without requiring extensive real-world experimentation, thus capturing full process complexity while reducing time and resource requirements.
Solution Approach 2:
The system performs preliminary computational simulations and optimizations in the digital twin environment before actual manufacturing. This allows extensive modeling and parameter optimization to be done virtually, reducing the need for time-consuming physical trials and expert iterative adjustments.
2Productivity
If pure data-driven approaches are used, then the models can process historical data efficiently, but they lack the flexibility to generalize across different scenarios and often fail when extrapolating beyond their training data
Solution Approach 1:
The patent merges data-driven machine learning models with physics-based principles in a hybrid framework. The ML components efficiently process historical data while the physics-based constraints ensure generalizability and reliable extrapolation to new scenarios, combining the strengths of both approaches.
Solution Approach 2:
The system dynamically adjusts model parameters and training data based on different curing scenarios and materials. This allows the data-driven models to adapt to new situations by retraining or fine-tuning with relevant data, improving generalizability across different manufacturing scenarios.
3Reliability
If hybrid models such as Physics-Informed Neural Networks are used, then the models combine the strengths of first principles and data-driven methods, but they still face challenges in predicting parameters outside their training range and adapting to real-time dynamic conditions
Solution Approach 1:
The patent implements a dynamic digital twin that continuously updates its state based on real-time sensor data from the actual curing process. This allows the hybrid model to adapt to changing conditions in real-time, improving its ability to predict parameters outside the original training range as new data becomes available.
Solution Approach 2:
The system incorporates continuous feedback loops where real-time process data is fed back into the digital twin and hybrid models. This feedback mechanism allows the models to learn from actual process variations and improve their predictions for new scenarios, enhancing adaptability to dynamic conditions.
4Measurement precision
If experiment-driven design is used, then the models can utilize real experimental data, but the process requires significant human expertise, is time-consuming, and prolongs the design cycle
Solution Approach 1:
The system performs preliminary experimental design, simulation, and optimization in the digital twin environment before conducting physical experiments. This preliminary work identifies the most critical experiments needed, reducing the overall number of physical trials required and shortening the design cycle while maintaining data quality.
Solution Approach 2:
The digital twin serves as a virtual laboratory where experiments can be simulated and tested before physical execution. This allows extensive experimental design and validation to be performed computationally, reducing the need for repeated physical experimentation and expert intervention, thus accelerating the design process.
5Reliability
If traditional expert-intensive methods are used for building and tuning predictive models, then the models can be customized for specific design dimensions, but the process is highly expert-intensive and difficult to implement online process design
Solution Approach 1:
The system implements automated model building and tuning capabilities where the digital twin and ML algorithms automatically customize models for different design dimensions without requiring extensive expert intervention. The system self-adjusts parameters and selects appropriate models based on the specific application, reducing complexity and enabling online implementation.
Solution Approach 2:
The patent creates a universal platform that handles multiple design dimensions and material types through a single integrated system. The digital twin framework and modular ML architecture allow the same system to be applied across different curing scenarios and design requirements, reducing the need for multiple specialized models and expert knowledge.
Data Source
AI summary
A method and system for designing curing processes is disclosed. The method includes obtaining design requirements using a requirement gathering agent, determining potential ranges of recipes and operating conditions with an operations specialist agent, identifying suitable material options and assessing corresponding properties with a material specialist agent, defining dimensions and shape aspects of the design using a design specialist agent, extracting and reasoning relevant information with a knowledge processing and retrieval agent, formulating a final requirement specification with an experiment enabler agent, generating a first set of experiments based on the final requirement specifications using an experiment designer, performing, by a predictive model design agent implementing a prediction model, real-time predictions for dynamic conditions, updating, by a predictive model optimizer agent implementing a continual learning framework, the prediction model in real-time; and optimizing and updating, by a process optimizer agent, the prediction model iteratively based on feedback from experiments and simulations.