Manufacturing Defect Clustering with CNN Features and PCA

The patent applies preliminary action by using pre-trained convolutional neural networks (CNNs) that have already learned general image features from large datasets. These pre-trained models serve as feature extractors that can be immediately applied to defect detection without requiring extensive task-specific training, thus enabling rapid adaptation to new defect types while maintaining high classification accuracy for known defects.

The patent employs parameter changes by transforming the classification problem into a clustering problem in a reduced-dimensional feature space. By applying principal component analysis (PCA) to reduce the dimensionality of CNN-extracted features and using silhouette metrics to evaluate cluster quality, the system adapts its parameters (feature representation, dimensionality) to handle both known and novel defect patterns effectively.

The patent applies self-service by implementing an unsupervised clustering approach that automatically discovers defect patterns without requiring human experts to manually label training data. The system uses silhouette metrics to automatically evaluate and select optimal cluster configurations, enabling the model to improve its own classification reliability through self-organized learning rather than time-consuming manual annotation.

The patent uses pre-trained CNN models as feature extractors that have already undergone extensive training on general image data. This preliminary training provides a strong foundation that can be immediately applied to defect detection tasks, eliminating the need for time-consuming task-specific training while maintaining high reliability through the transfer of learned features.

The patent applies the extraction principle by separating the feature extraction task (performed by pre-trained CNNs) from the clustering task. By extracting meaningful features in advance using established CNN architectures and then applying relatively simple clustering algorithms (k-means, GMM, DBSCAN) to these extracted features, the system achieves high clustering accuracy without the need for complex iterative optimization, thus reducing computational complexity while maintaining precision.

The patent employs dimensionality change by applying principal component analysis (PCA) to transform high-dimensional CNN features into a lower-dimensional space that preserves the most important variance. This dimensionality reduction simplifies the subsequent clustering task, allowing standard clustering algorithms to achieve high accuracy with reduced computational complexity compared to operating in the original high-dimensional space.