A Novel Compact Convolutional Neural Network for Real-Time Nondestructive Evaluation of Metallic Surfaces

Due to heightened concerns about quality assurance in the industry, the inspection of high-volume metallic parts has gained significant attention. In this article, we present a compact convolution neural network (CNN)-based optical nondestructive inspection scheme for high-volume metallic components. Our approach enables us to identify surface flaws as small as 2000th of an inch while discriminating them from flaw-like features, such as glue spots, dust, stains, and scratches. First, we capture the images of the metallic parts with a pixel size of 7000 × 2500 and label the images according to flawed and unflawed regions with the help of human experts. Second, we preprocess these images and form more than 120000 tiles of size 224 × 224 × 3, which, subsequently, are fed into our compact CNN. Finally, we train the neural network by fine-tuning the hyperparameters to reduce overfitting of the data and to obtain maximum accuracy. The quantitative evaluation of this network shows that it can achieve almost 99% precision and recall of over 60000 test images. The total image acquisition and inference time for one part-under-test is approximately 10 s. As a result, this novel compact CNN for optical inspection of surface flaws can outperform visual/manual inspection of high-volume metallic parts with very high accuracy and within a reasonable evaluation time.