GANs fostering data augmentation for automated surface inspection with adaptive learning bias.

In manufacturing, visual inspection of parts' surface is traditionally an important examination before the parts can proceed to the next manufacturing step. For example, the timely detection of minor surface defects, such as dents and scratches, in small-sized airfoils of aircraft engine, is typically the final stage of quality assurance before acceptance for assemblage. While such a process is critically important, current practices rely heavily on human operator's judgment, which is subjective and labor-intensive. In this study, we establish an automated, image-based inspection system that utilizes robotic automation to acquire high-resolution images of the parts under inspection and employs a specifically tailored machine learning technique to facilitate decision-making of inspection. Leveraging deep learning as the underlying methodology, we address a key challenge in the flexible automation of surface inspection, i.e., the scarcity of labeled data during the initial training process. In other words, we tackle the challenge of limited samples with known defects. Specifically, we synthesize an adaptive semi-supervised learning framework, building upon the residual neural network (ResNet) and the deep convolutional generative adversarial network (DCGAN) to extract features from both ground truth and synthetic data. This approach can overcome the shortcomings of the current approaches, leading to more objective and accurate defect detection right from the beginning of implementation with a small labeled dataset. Our results show that the overall classification accuracy on this challenging dataset reaches 92.30%, a 27.79% improvement over the baseline model achieved through optimal use of synthetic and ground truth data. The system also investigates the impact of synthetic data, providing guidelines for integrating it effectively into iterative training. This approach offers a robust solution for surface inspection and quality assurance in diverse manufacturing applications. [ABSTRACT FROM AUTHOR]