Composite Component Residual Life Prediction from NDT and ML
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Solution Overview
Problem
Existing methods for evaluating the residual life of composite material components are time-consuming and require repetitive manual work due to the need for expert supervision and frequent computational analysis, such as Finite Element Analysis, making the process expensive and inefficient.
Innovation Solution
A method and system utilizing ultrasound signals to determine defect type and location, combined with thermal imaging and machine learning, to automatically assess defect dimensions and predict residual life, eliminating the need for repetitive computational processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NDT methods with expert supervision and computational analysis (FEA, FEM) are used to evaluate residual life, then measurement precision and reliability are improved, but loss of time and productivity deteriorate due to repetitive manual work and frequent computational processing
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models (classifiers and regression models) using historical NDT data and computational analysis results. Once trained, these models can automatically evaluate residual life without requiring repeated computational analysis for each new inspection, thus reducing evaluation time while maintaining precision through the use of pre-validated algorithms
Solution Approach 2:
The patent replaces the mechanical system of manual expert supervision and iterative computational analysis with an automated machine learning-based system. The ML models automatically process NDT signals, classify defects, and predict residual life, substituting the time-consuming manual workflow with an automated computational approach that maintains measurement precision while dramatically reducing evaluation time
2Ease of operation
If automated techniques performing computational methods on NDT results are used, then ease of operation is improved by reducing manual work, but loss of time and productivity worsen due to expensive and time-consuming processing
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models using comprehensive datasets that include various defect types, locations, and residual life scenarios. This pre-training enables the automated system to handle diverse inspection cases without requiring additional computational resources for each new evaluation, thereby improving both ease of operation and productivity simultaneously
Solution Approach 2:
The patent changes the operational parameters by transitioning from traditional computational methods (FEA, FEM) that require significant processing time to machine learning models that have been optimized for rapid inference. The ML models use learned parameters from training data to make quick predictions, maintaining automation benefits while dramatically improving evaluation throughput and productivity
3Reliability
If traditional NDT analysis with computational modeling is used, then reliability of residual life prediction is improved, but device complexity and ease of manufacture worsen due to requiring multiple sensors and complex processing systems
Solution Approach 1:
The patent applies merging by integrating multiple NDT techniques (ultrasound, thermal imaging, eddy current) and their respective data processing methods into a unified machine learning framework. The ML model combines features from different sensor types and processing methods to make residual life predictions, maintaining high reliability through multi-modal data fusion while reducing overall system complexity by providing a single integrated analysis platform
4Measurement precision
If frequent manual transfer of NDT results to computational models is performed, then measurement precision is maintained through expert supervision, but loss of time and productivity deteriorate due to repetitive manual operations
Solution Approach 1:
The patent replaces the manual mechanical process of transferring NDT results to computational models with an automated machine learning system. The ML models automatically ingest NDT signals, extract features, classify defects, and predict residual life without manual intervention, maintaining measurement precision through algorithmic consistency while improving productivity by eliminating repetitive manual operations and enabling higher 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 provides a faster, scalable, and efficient evaluation of residual life, reducing dependence on human experts and enabling on-spot near real-time estimation through portable instruments.
Implementation Method 1
transmitting a plurality of ultrasound signals towards a test component comprising a defect and receiving the plurality of ultrasound signals reflected by the test component
Implementation Method 2
a location of the defect comprised in the test component is determined based on time of flight analysis of the plurality of ultrasound signals
Implementation Method 3
the location of the defect in the test component is scanned using a pulse echo ultrasound signal to determine depth of the defect comprised in the test component
Implementation Method 4
the location of the defect in the test component is scanned by a thermal camera to estimate a dimension of the defect
Data Source
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.