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Defect Detection by Image Processing
CNN + VGG16 transfer learning, 40,000 images, 98.3% test accuracy
"Detection of Defects Occurred in Assembly Line by Image Processing" — a parallel master's project alongside the predictive-maintenance thesis. Trained on a balanced dataset of 40,000 cement-surface images, 80/20 train/test split, transfer-learning from open-source VGG16, sweeps over learning rate / node count / dataset size. Final test accuracy 98.3%. The framing: prognostics for cement-based household goods — catch surface cracks before they become field failures. The intellectual bridge between the mechanical engineer I trained as and the QA engineer I became.