Inspection of sandblasting defect in investment castings by deep convolutional neural network

Investment castings often have surface impurities, and pieces of shell moulds can remain on the surface after sandblasting. Identification of defects involves time-consuming manual inspections in working environments of high noise and poor air quality. To reduce labour costs and increase the health and safety of employees, automated optical inspection (AOI) combined with a deep learning framework based on convolutional neural networks (CNNs) was applied for the detection of sandblasting defects. Four classic CNN models, including AlexNet, VGG-16, GoogLeNet, and ResNet-34, were applied for training and predictive classification. A comprehensive comparison reveals that AlexNet, VGG-16, and GoogLeNet v1 could accurately determine whether there were defects. Among the four models, AlexNet and VGG-16 were the most accurate, with prediction accuracy of 99.53% and 99.07% for qualifying products and both 100% for defective products. GoogLeNet v4 and ResNet-34 did not perform as expected in defect prediction. The reasoning behind the poor performance of GoogLeNet v4 and ResNet-34 is attributed to the restrictedness of the investment casting dataset to use models with residual learning architectures. Finally, a direct detection technique based on the AOI and CNN structure with a fast and flexible computational interface was demonstrated.

Automated summary

This study successfully applied automated optical inspection (AOI) combined with convolutional neural networks (CNNs) to detect sandblasting defects in investment castings, reducing labor costs and improving employee health and safety. Among the four classic CNN models tested, AlexNet and VGG-16 showed the highest accuracy in defect detection, with a prediction accuracy of 100% for defective products.