CNN-Based Material Defect Characterization for Automated Inspection
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Solution Overview
Problem
Conventional methods for identifying and classifying defects in materials during automotive manufacturing are time-consuming and prone to human errors, lacking efficiency and robustness.
Innovation Solution
An AI model, preferably a CNN, is used to identify and characterize defects such as ductile, brittle, and fatigue defects in materials by creating a feature map that classifies these defects based on extracted features from images, utilizing a trained neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and classification of defects is used, then human expertise can be applied to understand defect characteristics, but the process becomes highly time consuming and prone to human errors
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated image processing system using convolutional neural networks (CNNs). The system automatically extracts features from material images and classifies defects into categories such as ductile, brittle, and fatigue defects, eliminating the need for manual visual inspection while maintaining high accuracy through trained AI models.
Solution Approach 2:
The inspection system performs self-service by automatically analyzing defect characteristics without human intervention. The CNN-based model independently processes images, extracts relevant features, and classifies defects based on learned patterns from training data, enabling the system to serve itself in the inspection task while improving efficiency and consistency.
2Productivity
If automated image processing is implemented, then inspection speed and consistency are improved, but the system requires complex AI models and training data infrastructure
Solution Approach 1:
The patent segments the defect classification task into distinct processing stages: image acquisition, feature extraction using CNNs, and classification based on extracted features. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex AI process into manageable modules that can be implemented and maintained separately.
Solution Approach 2:
The system performs preliminary action by pre-training CNN models with large datasets of labeled defect images before deployment. This preliminary training phase prepares the AI model to automatically recognize and classify various defect types, reducing the complexity of real-time decision-making during actual inspection operations and enabling faster, more accurate classifications.
Data Source
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AI summary
The present disclosure relates to a method for automated characterisation of defects in an image of a material. In particular, it relates to an automated method for identifying and characterizing one or more defects of a material from an image of the material obtained using imaging modalities used, for example, during quality control or defective material analysis in an automotive product manufacturing process.