Composite Component Residual Life Prediction From NDT Defect Data
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Solution Overview
Problem
Existing methods for evaluating the residual life of composite materials require time-consuming manual processes and expert supervision, involving repetitive NDT analysis and computational methods like FEA, which are expensive and inefficient.
Innovation Solution
A method and system utilizing machine learning to analyze ultrasound and thermal camera data, employing a pre-trained classifier and model to automatically determine defect type, location, and dimension, eliminating the need for repetitive computational processing and enabling near-real-time residual life prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual NDT analysis and computational methods (FEA, FEM) are used to evaluate residual life, then measurement precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with extensive NDT data and computational mechanics knowledge before actual inspection. The pre-trained classifier and residual life prediction model are ready to provide expert-level defect characterization and residual life estimation without requiring manual FEA analysis during actual inspections, thus reducing analysis time while maintaining precision
Solution Approach 2:
The system replaces the mechanical/manual process of transferring NDT results to computational models with an automated machine learning-based system. The ML model automatically processes NDT signals, characterizes defects, and predicts residual life without manual intervention or iterative computational mechanics simulations, eliminating time-consuming manual work while preserving measurement precision
2Measurement precision
If expert supervision and manual transfer of NDT results to computational models is performed, then measurement precision and reliability are improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system implements self-service by enabling the NDT system to automatically perform defect characterization and residual life prediction without requiring expert supervision. The pre-trained machine learning models autonomously process inspection data, identify defect types, locate defects, estimate dimensions, and predict residual life, making the system easy to operate while maintaining high measurement precision through embedded expert knowledge
3Ease of operation
If automated techniques performing computational methods on NDT results are used, then ease of operation is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system replaces iterative computational mechanics methods with a single-pass machine learning inference process. The pre-trained residual life prediction model directly estimates component remaining life from NDT features without requiring repeated FEA simulations, dramatically improving productivity while maintaining ease of operation through full automation
Solution Approach 2:
The system performs preliminary action by pre-computing the complex computational mechanics relationships during the model training phase. This allows the deployed system to make rapid residual life predictions using simple ML inference, eliminating the need for time-consuming computational methods during actual inspections and thereby increasing inspection throughput
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly reduces analysis time, enhances efficiency, and enables scalable, automatic, and expert-independent evaluation of composite material components, facilitating faster decision-making in industries.
Implementation Method 1
a plurality of ultrasound signals are transmitted towards a test component and a plurality of ultrasound signals reflected by the test component are received
Implementation Method 2
scan, via a thermal camera, the location of the defect in the test component to estimate a dimension of the defect
Data Source
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AI summary
This disclosure generally relates to the field of structural health monitoring, and, more particularly, to a method and system for evaluating residual life of components made of composite materials. Existing methods require performing computational methods such as Finite Element Analysis (FEA) on the results of Non-Destructive Testing (NDT) every time a component is inspected. This makes the process expensive and time-consuming. Thus, embodiments of present disclosure provide a method wherein NDT is performed using different sensing methods such as ultrasound, ultrasound pulse echo, thermography to determine type of defect, location of defect and depth of defect in a test component which are then fed into a pre-trained machine learning model to predict residual life of the component. Testing time is greatly reduced since the pre-trained machine learning model is trained offline using results of the computational methods.