Identifying defective casting products using hierarchical defect recognition architecture: A computer vision approach

This paper proposes a novel approach for identifying defective casting products using a custom convolutional neural network architecture named Hierarchical Defect Recognition Architecture (HiDraNet). The HiDraNet model is designed to classify submersible pump impeller casting products into Normal and Defective categories by learning and extracting hierarchical features from a comprehensive dataset of 7348 casting product images, which includes various defect types such as fins, porosity, surface imperfections, and multiple defects. Experimental results demonstrate the superior performance of the HiDraNet model compared to several well-known deep learning models, such as AlexNet, MobileNetv2, ResNet18, GoogLeNet, ShuffleNet, and SqueezeNet, achieving the highest classification accuracy of 99.8% while exhibiting faster computation times. The proposed approach has significant implications for the manufacturing industry, as it can reduce the reliance on manual inspection methods, improve overall product quality, and minimize production costs, contributing to the broader adoption of Industry 4.0 technologies in the manufacturing sector.