In the manufacturing industry, automatic quality inspections can lead to improved product quality and productivity. Deep learning-based computer vision technologies, with their superior performance in many applications, can be a possible solution for automatic quality inspections. However, collecting a large amount of annotated training data for deep learning is expensive and time-consuming, especially for processes involving various products and human activities such as assembly. To address this challenge, we propose a method for automated assembly quality inspection using synthetic data generated from computer-aided design (CAD) models. The method involves two steps: automatic data generation and model implementation. In the first step, we generate synthetic data in two formats: two-dimensional (2D) images and three-dimensional (3D) point clouds. In the second step, we apply different state-of-the-art deep learning approaches to the data for quality inspection, including unsupervised domain adaptation, i.e., a method of adapting models across different data distributions, and transfer learning, which transfers knowledge between related tasks. We evaluate the methods in a case study of pedal car front-wheel assembly quality inspection to identify the possible optimal approach for assembly quality inspection. Our results show that the method using Transfer Learning on 2D synthetic images achieves superior performance compared with others. Specifically, it attained 95% accuracy through fine-tuning with only five annotated real images per class. With promising results, our method may be suggested for other similar quality inspection use cases. By utilizing synthetic CAD data, our method reduces the need for manual data collection and annotation. Furthermore, our method performs well on test data with different backgrounds, making it suitable for different manufacturing environments.

在制造业中，自动质量检测可以提高产品质量和生产率。基于深度学习的计算机视觉技术在许多应用中具有卓越的性能，可以成为自动质量检测的可能解决方案。然而，为深度学习收集大量带注释的训练数据既昂贵又耗时，特别是对于涉及各种产品和人类活动（例如装配）的过程。为了应对这一挑战，我们提出了一种使用计算机辅助设计 (CAD) 模型生成的合成数据进行自动装配质量检查的方法。该方法包括两个步骤：自动数据生成和模型实现。第一步，我们生成两种格式的合成数据：二维 (2D) 图像和三维 (3D) 点云。第二步，我们对数据应用不同的最先进的深度学习方法进行质量检查，包括无监督域适应，即跨不同数据分布调整模型的方法，以及迁移学习，在不同数据分布之间转移知识。相关任务。我们评估了踏板车前轮装配质量检验案例研究中的方法，以确定装配质量检验可能的最佳方法。我们的结果表明，与其他方法相比，在 2D 合成图像上使用迁移学习的方法具有优越的性能。具体来说，通过每类仅使用五个带注释的真实图像进行微调，它达到了 95% 的准确率。凭借令人鼓舞的结果，我们的方法可能会被建议用于其他类似的质量检查用例。通过利用合成 CAD 数据，我们的方法减少了手动数据收集和注释的需要。此外，我们的方法在不同背景的测试数据上表现良好，使其适用于不同的制造环境。