Fracture Section Image Analysis for Origin and Progress Detection
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Solution Overview
Problem
Existing fracture section image analysis methods lack accuracy in identifying the fracture origin position and progress directions, particularly in distinguishing between different fracture modes such as ductile, fatigue, and brittle fractures.
Innovation Solution
A fracture section image analysis device and method that utilizes an acquisitor to capture images and a processor to perform image analysis, including a first process to derive fracture progress directions and a second process to determine the fracture origin position using machine learning models and regression processors, while filtering based on certainty factors and common coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis methods are used, then the analysis process is simple, but the accuracy in identifying fracture origin position and progress directions is insufficient
Solution Approach 1:
The image analysis is divided into multiple independent processes: first process to derive fracture progress directions, second process to derive fracture origin positions, and third process to determine fracture modes. Each process operates independently on specific features, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
A machine learning model is introduced as an intermediary between the fracture section images and the analysis results. The model processes images through multiple stages, deriving intermediate features (fracture progress directions, origin positions) before final classification, thereby improving measurement precision without requiring a monolithic complex system.
2Measurement precision
If conventional analysis methods are used, then the analysis process is fast, but the ability to distinguish between different fracture modes is limited
Solution Approach 1:
The system performs preliminary derivation of fracture progress directions and origin positions before final fracture mode classification. By pre-processing the images to extract these key features, the subsequent classification process becomes more efficient and accurate, reducing overall analysis time while improving distinction capability.
Solution Approach 2:
The machine learning model incorporates feedback mechanisms where the derived fracture progress directions and origin positions are used to refine the fracture mode classification. This iterative feedback process allows the system to distinguish between fracture modes more accurately while maintaining fast processing speeds through optimized learning algorithms.
3Measurement precision
If detailed image analysis is performed, then the analysis accuracy is high, but the processing complexity and computational resources required increase
Solution Approach 1:
The system extracts only the essential features from the fracture section images: fracture progress directions, fracture origin positions, and fracture mode characteristics. By focusing analysis on these key extracted features rather than processing every pixel and detail, the system achieves high accuracy while reducing processing complexity and computational resource requirements.
Data Source
AI summary
According to one embodiment, a fracture section image analysis device includes an acquisitor configured to acquire a first image including a fracture section of a component, and a processor configured to perform image analysis for the first image. The image analysis includes a first process and a second process. The first process includes deriving a plurality of fracture progress directions in the fracture section. One of the plurality of fracture progress directions corresponds to one of a plurality of positions included in the fracture section. The second process includes deriving a fracture origin position in the fracture section based on at least a part of the plurality of fracture progress directions.