Convolutional neural network-based wire-cut image recognition and defect detection research

Abstract This paper presents an advanced defect detection system for wire Electrical Discharge Machining (EDM), utilizing Convolutional Neural Networks (CNNs) to automatically identify, classify, and localize defects such as cracks, notches, and burrs. Wire EDM is a precision manufacturing process critical for cutting conductive materials, where defect detection plays a vital role in ensuring product quality. The proposed method incorporates a modified ResNet-50 architecture, optimized specifically for defect detection in wire EDM. The architecture leverages deep residual learning to enhance feature extraction, allowing the system to detect minute defects effectively. A dataset of 10,000 RGB images (224 × 224 pixels) was used for training, with the model achieving an impressive 95.3% accuracy, 94.2% recall, 95.8% precision, and 94.7% F1 score on the test set. The system demonstrated excellent performance in detecting cracks, though it showed slightly lower performance on deformation-related defects. A comprehensive comparison with traditional defect detection methods and other deep learning models underscores the superiority of the proposed approach in terms of both accuracy and robustness. The results indicate that this CNN-based system offers a reliable and efficient solution for quality control in wire EDM processes. Future research will focus on further optimizing the network for real-time defect detection and extending the approach to incorporate multi-modal data, such as sensor and acoustic signals, to improve overall detection performance.