A Deep Transfer Learning-Based Visual Inspection System for Assembly Defects in Similar Types of Manual Tool Products

This study introduces an advanced inspection system for manual tool assembly, focusing on defect detection and classification in flex-head ratchet wrenches as a modern alternative to traditional inspection methods. Using a deep learning R-CNN approach with transfer learning, specifically utilizing the AlexNet architecture, the system accurately identifies and classifies assembly defects across similar tools. This study demonstrates how a pre-trained defect detection model for older manual tool models can be efficiently adapted to new models with only moderate amounts of new samples and fine-tuning. Experimental evaluations at three assembly stations show that the AlexNet model achieves a classification accuracy of 98.67% at the station with the highest defect variety, outperforming the R-CNN model with randomly initialized weights. Even with a 40% reduction in sample size for new products, the AlexNet model maintains a classification accuracy of 98.66%. Additionally, compared to R-CNN, it improves average effectiveness by 9% and efficiency by 26% across all stations. A sensitivity analysis further reveals that the proposed method reduces training samples by 50% at 50% similarity while enhancing effectiveness by 13.06% and efficiency by 5.31%.