K-Fold Component Assessment for Functional Tolerance Screening
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Solution Overview
Problem
Existing methods for assessing the quality of manufactured gas turbine engine components, such as integrally bladed rotors, rely solely on geometric tolerances, which often misidentify functionally tolerant parts as unacceptable, leading to inefficiencies and potential failures.
Innovation Solution
A method combining physical testing and machine learning using K-fold validation to evaluate component quality by comparing generated data from multiple locations against training data, ensuring functional tolerance beyond nominal dimensions, and employing a conservative evaluation to determine acceptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of dimensions is used to assess component quality, then measurement precision is maintained, but productivity is reduced and misclassification of functionally tolerant parts occurs
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated system that uses coordinate measuring machine (CMM) data combined with machine learning algorithms. The CMM automatically captures dimensional data at multiple locations, and the neural network model automatically assesses functional tolerance, eliminating manual measurement while maintaining precision and dramatically improving productivity.
Solution Approach 2:
The system enables self-assessment of component quality by integrating CMM data collection with an automated neural network evaluation model. The model automatically processes dimensional data, compares it against training data from common parts, and determines functional tolerance without human intervention, allowing the system to serve itself in the quality assessment process.
2Manufacturing precision
If geometric tolerances are used for component assessment, then manufacturing precision is enforced, but reliability is reduced due to misidentification of functionally tolerant parts
Solution Approach 1:
The patent changes the assessment parameter from strict geometric tolerances to functional tolerance evaluation. Instead of checking whether dimensions fall within nominal tolerance ranges, the system uses a neural network model trained on historical CMM data to predict whether the component will function acceptably, even if dimensions are outside traditional tolerances. This parameter change resolves the contradiction by prioritizing actual functional performance over nominal dimensional compliance.
Solution Approach 2:
The system performs preliminary training using historical CMM data from common parts before actual assessment. The neural network model is trained in advance on labeled data indicating which parts were functionally acceptable, creating a predictive model that can accurately assess new components. This preliminary action enables the system to learn the relationship between dimensional variations and functional performance, improving reliability of acceptability decisions.
Data Source
AI summary
A method of assessing the quality of a manufactured component includes the steps of manufacturing a component and generating data at each of a plurality of locations through the component. The generated data is passed to a machine learning branch. The generated data is compared to training data at each of the plurality of locations and across a plurality of folds using K-fold validation to determine whether the component is of a functionally tolerant dimension at each of the plurality of locations. The training data at each of the plurality of folds at each of the plurality of locations is from a common part. A system is also disclosed.