Component Tolerance Assessment Using Testing and Machine Learning
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Solution Overview
Problem
Existing methods for assessing the quality of manufactured gas turbine engine components rely solely on manual inspection of dimensions, which can lead to misclassification of components as unacceptable when they are actually functionally tolerant.
Innovation Solution
A dual branch assessment system combining physical testing and machine learning is employed, where data from multiple locations on the component is analyzed through a testing branch and a machine learning branch, ensuring both methods agree on the component's functional tolerance before acceptance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of dimensions is used to assess component quality, then the assessment process is simple to implement, but it leads to misclassification of components as unacceptable when they are actually functionally tolerant
Solution Approach 1:
The assessment system is segmented into two independent branches: a testing branch that performs traditional dimensional tolerance checks and a machine learning branch that evaluates functional tolerance. Each branch operates independently and contributes to the final assessment decision, allowing the system to leverage both traditional and advanced methods without requiring complete system redesign
Solution Approach 2:
The patent combines the testing branch and machine learning branch into a unified dual-branch assessment system. Both branches process the same component data but apply different assessment criteria, and their results are integrated to make the final acceptance/rejection decision, thereby improving overall assessment accuracy while maintaining manageable system complexity
2Productivity
If manual inspection methods are used for component assessment, then the implementation is straightforward, but the evaluation speed is slow and misclassification occurs
Solution Approach 1:
The patent replaces manual inspection methods with an automated machine learning-based assessment system. The machine learning branch processes component data computationally to determine functional tolerance, significantly increasing evaluation speed while improving accuracy by identifying functionally tolerant components that manual inspection would incorrectly reject
3Adaptability or versatility
If traditional dimensional tolerance checking is used, then the assessment criteria are clear and objective, but components within functional tolerance but outside nominal tolerance ranges are rejected
Solution Approach 1:
The patent introduces a second assessment criterion (functional tolerance) alongside the traditional dimensional tolerance. The machine learning branch evaluates components based on functional tolerance parameters that are less stringent than nominal dimensional tolerances, allowing components with minor dimensional variations to be accepted if they remain functionally tolerant, thereby improving acceptance reliability
Data Source
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AI summary
A method includes the steps of manufacturing and generating data at each of a plurality of locations on a component (22). Passing the data to a testing branch (25) which performs tests (28, 30) and reaches a conclusion as to whether the component (22) is functionally tolerant at each of the plurality of locations. The data is also passed to a machine learning branch (35) wherein it is compared to training data to determine whether the component (22) is of a functionally tolerant dimension at each of the plurality of locations. The manufactured component accepts should both the testing branch (25) and the machine learning branch (35) determine the component (22) is of functionally tolerant dimensions at the plurality of locations, and rejects the component (22) if either of the testing branch (25) or the machine learning branch (35) determines the component (22) fails to be functionally tolerant dimensions, respectively. A system (20) is also disclosed.