Multi-Modal Composite Property Prediction for Variable Material Behavior
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Solution Overview
Problem
Existing computational models for composites, such as soft tissues and hard materials, fail to accurately depict complex underlying mechanics due to high variability in material properties and experimental setup, leading to inadequate prediction of composite properties.
Innovation Solution
A multi-modal modeling approach that incorporates mechanical, morphological, and electrical data types into physics-based and data-driven models, utilizing finite element methods and generative ML/AI techniques to predict composite properties, enabling accurate simulation and synthesis of composite materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-mode computational models are used for composite materials, then the model complexity is low, but the prediction accuracy of composite properties deteriorates due to high variability in material properties and experimental setup
Solution Approach 1:
The patent combines multiple data modalities (mechanical, morphological, electrical, biochemical) into a unified multi-modal computational framework. This merging of diverse data types enables the model to capture complex composite material behavior that single-mode models cannot represent, directly resolving the contradiction by improving prediction accuracy through integrated multi-source information
Solution Approach 2:
The patent applies composite modeling principles by treating the computational model itself as a composite system that integrates multiple physics-based sub-models and data-driven components. This composite approach to model architecture enables accurate prediction of composite material properties by combining strengths of different modeling paradigms
2Reliability
If multi-modal data types are integrated into physics-based models, then the simulation fidelity improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the multi-modal computational framework into distinct modular components: mechanical property modules, morphological analysis modules, electrical property modules, and integration layers. This segmentation allows each module to process specific data types independently using appropriate algorithms, improving simulation fidelity while managing computational complexity through divide-and-conquer strategy
Solution Approach 2:
The patent introduces intermediary computational layers that transform and normalize multi-modal data before integration into the physics-based model. These intermediary processing stages act as mediators that reconcile different data formats and scales, enabling high-fidelity simulation while reducing the direct computational burden of handling raw multi-modal data
Data Source
AI summary
Embodiments perform multi-modal prediction of composite properties. A first mode input representing mechanical characteristic(s) of a composite sample is (i) transformed into material property definition(s) of physics-based model(s) or (ii) used to encode material property definition(s) in input variable(s) of a machine learning (ML) model. A second mode input representing morphological characteristic(s) of the sample is (i) transformed into phase volume parameter(s) of the physics-based model(s) or (ii) used to encode phase volume parameter(s) in the input variable(s) of the ML model. A third mode input associated with the sample is (i) transformed into electrical conductivity parameter(s) of the physics-based model(s) or (ii) used to encode electrical conductivity parameter(s) in the input variable(s) of the ML model. Using the physics-based model(s) or the ML model, property(ies) of the sample are predicted.