铸造成型物体的合成缺陷几何模型：基于二维Voronoi镶嵌的建模
Synthetic Defect Geometries of Cast Metal Objects Modeled via 2d Voronoi Tessellations
AI总结：
本文提出基于二维Voronoi镶嵌的参数方法，用于建模铸造成型物体的合成缺陷几何，以生成高质量合成数据用于缺陷检测研究。
摘要：
在工业中，缺陷检测对于质量控制至关重要。非破坏性检测（NDT）方法被优先采用，因为它们在检查时不会影响物体的功能性。自动化缺陷检测的自动化数据评估是一个快速增长的研究领域。特别是，机器学习方法显示出有前途的结果。为了提供足够数量和质量的训练数据，可以使用合成数据。基于规则的方法能够在可控的环境中生成合成数据。因此，需要一个包含合成缺陷的被检物体的数字双胞胎。我们提出了参数方法，用于建模各种缺陷类型的3D网格物体，这些模型可以添加到物体几何中以获得合成缺陷物体。这些模型受到金属铸造中常见缺陷的启发，但也可以转移到其他加工过程，这些过程产生相似的缺陷形状。通过使用基于物理的蒙特卡洛模拟来模拟相应的检测方法，可以创建与真实检测数据相似的合成数据。使用我们的缺陷模型，可以生成变量且任意大的合成数据集，并有可能以足够数量包含罕见的缺陷。可以同时创建像素级注释。作为例子，我们将使用视觉表面检查，但该过程可以与模拟结合使用，用于任何其他NDT方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
