Enhancing titanium spacer defect detection through reinforcement learning-optimized digital twin and synthetic data generation

Abstract

In the field of automatic defect detection, a major challenge in training accurate classifiers using supervised learning is the insufficient and limited diversity of datasets. Obtaining an adequate amount of image data depicting defective surfaces in an industrial setting can be costly and time-consuming. Furthermore, the collected dataset may suffer from selection bias, resulting in an underrepresentation of certain defect classes. Our research aims to address surface defect detection in titanium metal spacer rings by introducing an approach leveraging a digital twin framework. The behavior of the digital twin is optimized using a reinforcement learning (RL) algorithm. The optimized digital twin is then used to generate synthetic data, which is employed to train a spacer defect detection classifier. The classifier's performance is evaluated using real-world data. The results show that the model trained with synthetic data outperforms the one trained on a limited amount of real data. Our work highlights the potential of digital twin-based synthetic data generation and RL optimization in enhancing spacer surface defect detection and addressing the data scarcity challenge in the field. When the inspection network is trained solely using generated synthetic data, it achieves an inspection precision of 96.10%, with a recall of 85.77%. Incorporating real data alongside synthetic data for training further enhances performance, yielding an inspection precision of 93.07% and a recall of 94.20%. This surpasses the defect detection capabilities observed when training the inspection network exclusively with real data.