Date of Award
Master of Science
Electrical and Computer Engineering
Quality inspection is an indispensable part of the production process of screws for hardware manufactories. In general, hardware manufactories do the quality test of screws by using an electric screwdriver to twist screws. However, there are some limitations and shortcomings in the manual inspection. Firstly, the efficiency of manual inspection is low. Second, manual inspection is difficult to achieve continuous working for 24 hours, which will make a high wage cost. In this thesis, in order to enhance the inspection efficiency and save test costs, we propose to use the image recognition technology of memristor neural networks to check the quality of screws. Here, we discuss different training models of neural networks, namely: convolutional neural networks, one-layer memristor neural network with fixed learning rates. By using the dataset of 8,202 screw head images, experimental results show that the classification accuracy of CNNs and memristor neural networks can achieve 96% and 90%, respectively, which prove the effectiveness of the proposed method.
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