Material Degradation Prediction Without Destructive Testing
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Solution Overview
Problem
Existing methods for evaluating metal material degradation in power plants and mechanical facilities require destructive testing, which is inefficient and costly, and existing prediction methods lack accuracy in estimating degradation indices without environmental information.
Innovation Solution
A degradation prediction device and method that uses a non-parametric clustering model to analyze microstructure images from destructive testing, extracting features and environmental information to predict a degradation index and Larson-Miller Parameter (LMP) value without destroying the material.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If destructive testing is used to evaluate metal material degradation, then measurement precision of degradation index is improved, but loss of substance and productivity deteriorate
Solution Approach 1:
The patent creates a virtual copy of the material's microstructure through image processing and uses this digital replica for analysis instead of physically destroying the actual material. The system captures microstructure images and processes them through clustering algorithms to predict degradation, replacing physical destructive testing with virtual analysis of copied visual data.
Solution Approach 2:
The patent replaces mechanical/physical destructive testing methods with an information-processing system based on image analysis and machine learning. Instead of physically examining and destroying material samples, the system uses computational algorithms to analyze microstructure images and predict degradation, substituting mechanical destruction with intelligent data processing.
2Reliability
If destructive testing is performed to obtain degradation data, then reliability of degradation assessment is improved, but productivity and time consumption worsen
Solution Approach 1:
The patent performs preliminary actions by capturing microstructure images and training the prediction model in advance using data from destructive testing. Once the model is trained, it can rapidly assess degradation without requiring new destructive tests, thereby improving productivity while maintaining reliability through the pre-established predictive capability.
Solution Approach 2:
The system enables self-service by allowing the prediction model to automatically assess degradation of new materials based on their microstructure images and environmental data, without requiring expert manual analysis or new destructive testing. The model serves itself by continuously learning and improving its predictive accuracy.
3Measurement precision
If expert visual examination is used for degradation assessment, then measurement precision is improved, but device complexity and cost worsen
Solution Approach 1:
The system replaces expert human analysis with an automated prediction model that performs degradation assessment independently. The model processes microstructure images and environmental data to generate degradation predictions without requiring expert intervention, thereby reducing device complexity and operational costs while maintaining or improving measurement precision.
Solution Approach 2:
The patent substitutes the mechanical process of expert visual examination with an automated computational system. Instead of relying on human experts to visually inspect and evaluate material degradation, the system uses machine learning algorithms to automatically analyze images and predict degradation, simplifying the evaluation process and reducing dependency on specialized human expertise.
4Device complexity
If environmental information is not incorporated into degradation prediction, then device complexity is reduced, but measurement precision of degradation index deteriorates
Solution Approach 1:
The patent creates a universal prediction model that integrates multiple types of input data (microstructure images, environmental conditions, material properties) to perform comprehensive degradation assessment. This multi-functional approach allows the single model to handle various prediction tasks with high precision, making the added complexity worthwhile by significantly improving measurement accuracy.
Solution Approach 2:
The system improves prediction precision by incorporating additional parameters such as environmental conditions (temperature, humidity, stress) alongside microstructure features. By changing the input parameters from simple image data to a comprehensive set including environmental factors, the model achieves more accurate degradation predictions, justifying the increased system complexity.
Data Source
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AI summary
Disclosed is a degradation prediction device predicting a lifetime of a target material, including: at least one processor, and the at least one processor is configured to train a degradation index prediction model based on the representative value of a degradation index of a material by environmental conditions and the environmental information of a material subjected to a destructive testing, train a LMP (Larson-Miller Parameter) value prediction model based on the representative value of a degradation index of a material by environmental conditions and a theoretical value of LMP at a destructive testing, predict a degradation index for a target material using the degradation index prediction model for which training is completed based on environmental information of the target material and predict a LMP value of the target material using the LMP value prediction model for which training is completed based on the predicted degradation index.