Automated quality control in rotogravure printing has been hindered by the lack of robust deep learning models, which are necessary for detecting surface defects. A newly proposed synthetic data generation framework aims to address this issue by providing a reliable means of training models such as YOLO or Vision Transformers1. The framework's development is crucial, as manual inspection methods are time-consuming, expensive, and prone to human error. By leveraging synthetic data, the framework can generate high-quality training data, enabling the creation of more accurate deep learning models. This, in turn, can lead to improved defect detection and enhanced overall print quality. The implications of this framework extend beyond the printing industry, as similar techniques can be applied to other fields where automated quality control is essential. Therefore, the successful implementation of this framework can have significant consequences for industries relying on automated inspection and quality control, making it a critical development for practitioners to monitor.