Abstract
Quality control of assembly processes is essential in manufacturing to ensure not only the quality of individual components but also their proper integration into the final product. To assist in this matter, automated assembly control using computer vision methods has been widely implemented. However, the costs associated with image acquisition, annotation, and training of computer vision algorithms pose challenges for integration, especially for small- and medium-sized enterprises (SMEs), which often lack the resources for extensive training, data collection, and manual image annotation. Synthetic data offers the potential to reduce manual data collection and labeling. Nevertheless, its practical application in the context of assembly quality remains limited. In this work, we present a novel approach for easily integrable and data-efficient visual assembly control. Our approach leverages simulated scene generation based on computer-aided design (CAD) data and object detection algorithms. The results demonstrate a time-saving pipeline for generating image data in manufacturing environments, achieving a mean Average Precision (mAP@0.5:0.95) up to 99,5% for correctly identifying instances of synthetic planetary gear system components within our simulated training data, and up to 93% when transferred to real-world camera-captured testing data. This research highlights the effectiveness of synthetic data generation within an adaptable pipeline and underscores its potential to support SMEs in implementing resource-efficient visual assembly control solutions.
Abstract (translated)
装配过程中的质量控制在制造业中至关重要，不仅确保了各个组件的质量，还保证了它们能够正确地集成到最终产品中。为了协助这一目标，广泛实施了基于计算机视觉方法的自动化装配控制系统。然而，图像获取、注释以及训练计算机视觉算法的相关成本对整合带来了挑战，尤其是对于资源有限的小型和中型企业（SMEs），这些企业通常缺乏进行大量培训、数据收集及手动图像标注的能力。合成数据提供了减少手动数据采集和标记潜力的可能性，但其在装配质量上下文中的实际应用仍然较为有限。 在此研究工作中，我们提出了一种新型的方法，旨在实现易于集成且高效的数据驱动视觉装配控制。我们的方法利用基于计算机辅助设计（CAD）数据的仿真场景生成，并结合了目标检测算法。实验结果表明，这为制造业环境中图像数据的生成提供了一个节省时间的工作流程，在针对合成行星齿轮系统组件实例的模拟训练数据上实现了高达99.5%的平均精度（mAP@0.5:0.95），而在转移到实际相机捕捉到的测试数据时，这一数字达到了最高93%。 这项研究强调了在具有适应性工作流程中生成合成数据的有效性，并突显了其支持SMEs实施资源高效视觉装配控制系统解决方案的巨大潜力。
URL
https://arxiv.org/abs/2509.13089
