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In the field of automatic defect detection, one of the major challenges for 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 is costly and time-consuming. Furthermore, the collected dataset may suffer from selection bias, resulting in underrepresentation of certain defect classes. This research aims to tackle the issue of surface defect detection in titanium metal spacer rings by introducing a novel approach that leverages a digital twin framework. The behavior of the digital representation is optimized using a reinforcement learning algorithm. Subsequently, the optimized digital twin is utilized to generate synthetic data, which is then employed to train a spacer defect detection classifier. The performance of this classifier is evaluated using real-world data. The results illustrate that the model trained with synthetic data outperforms the one trained on a limited amount of real data. This work emphasizes the potential of digital twin-based synthetic data generation and reinforcement learning optimization in enhancing spacer surface defect detection and addressing the data scarcity challenge in the field. When the generated synthetic data and real data combined is used to train inspection network, the inspection background accuracy reaches 93.07% and defect detection accuracy reaches 94.2% surpassing the defect detection performance of inspection network trained only using real data.
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