Composite Structure Damage Analysis Using Dent-Depth Meta-Models

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Solution Overview

Problem

Current methods for demonstrating damage tolerance in composite structures are subjective and lack precision due to the complexity of composite materials, making it difficult to predict dent depth and underlying damage extent, leading to costly and time-consuming physical testing.

Innovation Solution

An analytical approach using a dent-depth meta-model informed by a machine learning algorithm and historical physical test data to estimate impact energy for simulating dent depth in composite components, reducing the need for physical testing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional finite-element analysis is used for composite structures, then metallic structures can be analyzed accurately, but the complexity of composite structures limits the digital model's ability to predict dent depth and underlying damage extent

Engineering Contradiction:

Improvedent depth prediction accuracyVSAvoidcomposite structure complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary machine learning model that acts as a bridge between complex composite structure geometry and damage prediction. This model processes the complex geometric inputs and transforms them into reliable damage predictions, resolving the contradiction by mediating between structural complexity and prediction accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional mechanics-based finite-element analysis with a machine learning-based predictive system. This substitution enables accurate damage prediction for complex composite structures without being constrained by the limitations of traditional mechanical modeling approaches.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If visual inspection with pre-defined distance is used, then damage can be detected, but inherent uncertainty requires adoption of threshold dent depth to quantify acceptable initial damage state

Engineering Contradiction:

Improvedamage detection reliabilityVSAvoiddent depth measurement precision

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent replaces subjective visual inspection with an objective machine learning-based digital prediction system. This substitution eliminates the inherent uncertainty and subjectivity of visual methods, providing precise and reliable dent depth predictions without requiring arbitrary threshold definitions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If extensive physical testing is conducted to demonstrate damage tolerance, then certification requirements can be met, but development costs and time increase significantly

Engineering Contradiction:

Improvedamage tolerance certificationVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent creates a digital copy (virtual model) of the composite structure that replicates its damage tolerance behavior. This digital twin can be tested and analyzed repeatedly without physical prototypes, significantly reducing development time and costs while maintaining certification reliability through accurate virtual testing.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent performs damage tolerance analysis preliminarily through machine learning predictions before committing to extensive physical testing. This preliminary digital assessment allows for early validation of design concepts and identification of potential issues, reducing the need for iterative physical testing and accelerating the certification process.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If subjective structural testing process is used, then damage tolerance can be demonstrated, but the process is time-consuming and costly

Engineering Contradiction:

Improvedamage tolerance demonstrationVSAvoidanalysis efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces the subjective mechanical testing process with an objective machine learning-based digital prediction system. This substitution maintains the reliability of damage tolerance demonstration while dramatically improving productivity by eliminating the need for time-consuming physical tests for each design iteration.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12553862B2Method and apparatus for analyzing a composite structure

Publication Date: 2026.02.17 THE BOEING CO

AI summary

A method of analyzing a composite structure for damage tolerance. The method includes obtaining a structural configuration and determining at least one metric of interest for the structural configuration. A dent depth meta-model is developed based on the structural configuration, the at least one metric of interest, and information obtained from a machine learning algorithm. The at least one metric of interest provides a constraint for the dent depth meta-model and the machine learning algorithm is trained based on a historical physical test dataset of a plurality of composite components. The dent depth meta-model is utilized to estimate an impact energy to establish a threshold dent depth in the structural configuration. A computer-based simulation is performed with the structural configuration utilizing the impact energy from the dent depth meta-model. At least one design value is determined for the structural configuration based on output data generated from the computer-based simulation.