Acoustic Inspection System Surface Profiling via Machine Learning
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Solution Overview
Problem
Conventional acoustic inspection techniques, such as the total focusing method (TFM), face challenges when applied to components with complex surfaces, as they require known surface profiles to generate accurate results, often necessitating additional equipment for surface profiling.
Innovation Solution
An acoustic inspection system that uses machine learning models to generate and represent surface profiles from encoded acoustic images, eliminating the need for external equipment by integrating surface profiling within the inspection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional acoustic inspection techniques (TFM) are used on components with complex surfaces, then accurate inspection results can be obtained, but additional equipment for surface profiling is required
Solution Approach 1:
The patent combines surface profiling and acoustic inspection functions into a single integrated system. The acoustic inspection system simultaneously performs both surface profiling and inspection tasks, eliminating the need for separate external surface profiling equipment while maintaining accurate inspection results.
Solution Approach 2:
The acoustic inspection system is designed to perform multiple functions: it can profile complex surfaces and conduct acoustic inspection using the same device. This multi-functional approach allows the system to generate surface profiles and perform inspections without requiring additional specialized equipment.
2Measurement precision
If additional external equipment is used for surface profiling, then accurate surface profiles can be generated, but system complexity and operational convenience are reduced
Solution Approach 1:
The patent merges surface profiling and acoustic inspection into one integrated system, allowing users to perform both functions with a single device. This eliminates the complexity of coordinating multiple separate equipment systems and improves operational convenience while maintaining accurate surface profile generation.
3Adaptability or versatility
If conventional TFM techniques are used without known surface profiles, then inspection of complex components is possible, but accurate results cannot be generated
Solution Approach 1:
The system performs surface profiling as a preliminary step before conducting acoustic inspection. By first generating an accurate surface profile of the complex component, the system establishes the necessary geometric information required for subsequent TFM inspection to produce accurate results.
Solution Approach 2:
The system uses the generated surface profile as feedback information to improve the accuracy of acoustic inspection results. The surface profile data is fed back into the inspection process to correct for geometric variations and enhance the precision of flaw detection in complex components.
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
Enables efficient and accurate surface profiling and inspection of complex components without additional equipment, improving convenience and efficiency by applying trained machine learning models to encoded acoustic images for generating surface representations.
Implementation Method 1
Inhomogeneities on or within the structure under test can generate scattered or reflected acoustic signals in response to a transmitted acoustic pulse
Implementation Method 2
Inhomogeneities on or within the structure under test can generate scattered or reflected acoustic signals in response to a transmitted acoustic pulse
Implementation Method 3
generating, using the acoustic imaging data and a time-of-flight (TOF) delay associated with a medium, a first encoded acoustic image of the object
Data Source
AI summary
An acoustic inspection system can be used to generate a surface profile of a component under inspection, and then can be used to perform the inspection on the component. The acoustic inspection system can obtain acoustic imaging data, e.g., FMC data, of the component. Then, the acoustic inspection system can apply a previously trained machine learning model to an encoded acoustic image, such as a TFM image, to generate a representation of the profile of one or more surfaces of the component. In this manner, no additional equipment is needed, which is more convenient and efficient than implementations that utilize additional components that are external to the acoustic inspection system.