In industry, defect detection is crucial for quality control. Non-destructive testing (NDT) methods are preferred as they do not influence the functionality of the object while inspecting. Automated data evaluation for automated defect detection is a growing field of research. In particular, machine learning approaches show promising results. To provide training data in sufficient amount and quality, synthetic data can be used. Rule-based approaches enable synthetic data generation in a controllable environment. Therefore, a digital twin of the inspected object including synthetic defects is needed. We present parametric methods to model 3d mesh objects of various defect types that can then be added to the object geometry to obtain synthetic defective objects. The models are motivated by common defects in metal casting but can be transferred to other machining procedures that produce similar defect shapes. Synthetic data resembling the real inspection data can then be created by using a physically based Monte Carlo simulation of the respective testing method. Using our defect models, a variable and arbitrarily large synthetic data set can be generated with the possibility to include rarely occurring defects in sufficient quantity. Pixel-perfect annotation can be created in parallel. As an example, we will use visual surface inspection, but the procedure can be applied in combination with simulations for any other NDT method.

翻译：在工业领域，缺陷检测对质量控制至关重要。无损检测方法因其在检测过程中不影响物体功能而备受青睐。面向自动缺陷检测的自动化数据评估正成为日益重要的研究领域。机器学习方法尤其展现出广阔前景。为提供充足且高质量的训练数据，可采用合成数据。基于规则的方法能够在可控环境中实现合成数据生成。为此，需要建立包含合成缺陷的被检测物体数字孪生体。本文提出参数化方法对多种缺陷类型的三维网格物体进行建模，这些缺陷模型可融入物体几何结构以生成合成缺陷物体。模型设计灵感来源于金属铸造常见缺陷，但可推广至其他产生相似缺陷形状的机械加工过程。通过采用基于物理的蒙特卡洛模拟对应检测方法，可生成与真实检测数据相似的合成数据。利用本文缺陷模型，能够生成规模可变且任意扩展的合成数据集，并可包含足量罕见缺陷样本。同时可生成像素级精确标注。本文以视觉表面检测为例进行说明，但该方法可与任何其他无损检测方法的模拟相结合使用。