Pulse-Echo Subsurface Detection for Automated Defect Assessment
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Solution Overview
Problem
Existing methods for detecting internal defects in aerospace engine components, such as porosity and inclusions, are labor-intensive, time-consuming, and prone to errors due to manual interpretation of ultrasonic sensor signals.
Innovation Solution
An automated system using immersion pulse-echo inspection technology with a flaw detection algorithm and machine learning-based confidence assessment to identify subsurface defects, integrating C-scan and A-scan data analysis for accurate defect recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interpretation of ultrasonic sensor signals is used, then flexibility and adaptability in handling complex defect patterns are maintained, but inspection time and labor intensity increase significantly
Solution Approach 1:
The inspection system segments the defect detection process into distinct stages: data acquisition, signal processing, defect identification, and validation. Each stage is handled by specialized algorithms that can be independently optimized and configured for different defect types, maintaining flexibility while automating the process to reduce inspection time.
Solution Approach 2:
The system employs dynamic algorithm selection and configuration based on the specific inspection requirements and defect characteristics. The processing pipeline can be dynamically adjusted to handle different defect patterns, materials, and inspection criteria, providing both automation efficiency and adaptability.
2Productivity
If automated defect recognition algorithms are implemented, then inspection speed and consistency are improved, but system complexity and development effort increase
Solution Approach 1:
The automated recognition system is designed with universal algorithms that can detect multiple types of defects (porosity, inclusions, cracks) across different engine components. The same core processing pipeline handles various inspection scenarios by adjusting parameters and criteria, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system introduces intermediate processing layers including signal preprocessing, feature extraction, and confidence assessment that mediate between raw sensor data and final defect decisions. These intermediary steps simplify the overall decision-making process and reduce the complexity of direct pattern recognition.
3Reliability
If comprehensive signal analysis is performed to ensure high detection accuracy, then defect identification reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary signal processing and filtering operations before detailed defect analysis. By preprocessing the ultrasonic signals to enhance relevant features and suppress noise early in the pipeline, the subsequent detection algorithms work with cleaner data, improving reliability while reducing the computational burden and processing time of later stages.
Solution Approach 2:
The system applies partial analysis to signals that clearly meet or fail inspection criteria, performing comprehensive analysis only on borderline cases that require detailed examination. This selective approach maintains high detection reliability for critical cases while reducing overall processing time for routine inspections.
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 system provides efficient, accurate, and cost-effective detection of subsurface defects with minimized human intervention, ensuring high-quality inspection results.
Implementation Method 1
transmitting ultrasonic energy to the part and receiving the ultrasonic energy from the part
Data Source
Figure 1~2
Figure 3~5(b)
Figure 6~7
AI summary
A system for detecting a sub-surface defect (28) comprising a transducer (14) fluidly coupled to a part (26) located in a tank (22) containing a liquid (24) configured to transmit ultrasonic energy (20), the transducer (14) configured to scan the part (26) to create scan data (30) of the scanned part (26); a pulser/receiver (12) coupled to the transducer (14) configured to receive and transmit the scan data (30); a processor (32) coupled to the pulser/receiver (12), the processor (32) configured to communicate with the pulser/receiver (12) and collect the scan data (30); and the processor (32) configured to detect the sub-surface defect (28) and the processor (32) configured to have a sub-surface defect (28) confidence assessment (114) and a prioritization for further human evaluation.