In the aerospace industry, the Automated Fiber Placement process is an established method for producing composite parts. Nowadays the required visual inspection, subsequent to this process, typically takes up to 50% of the total manufacturing time and the inspection quality strongly depends on the inspector. A Deep Learning based classification of manufacturing defects is a possibility to improve the process efficiency and accuracy. However, these techniques require several hundreds or thousands of training data samples. Acquiring this huge amount of data is difficult and time consuming in a real world manufacturing process. Thus, an approach for augmenting a smaller number of defect images for the training of a neural network classifier is presented. Five traditional methods and eight deep learning approaches are theoretically assessed according to the literature. The selected conditional Deep Convolutional Generative Adversarial Network and Geometrical Transformation techniques are investigated in detail, with regard to the diversity and realism of the synthetic images. Between 22 and 166 laser line scan sensor images per defect class from six common fiber placement inspection cases are utilised for tests. The GAN-Train GAN-Test method was applied for the validation. The studies demonstrated that a conditional Deep Convolutional Generative Adversarial Network combined with a previous Geometrical Transformation is well suited to generate a large realistic data set from less than 50 actual input images. The presented network architecture and the associated training weights can serve as a basis for applying the demonstrated approach to other fibre layup inspection images.

在航空航天工业中，自动纤维铺放工艺是生产复合零件的既定方法。如今，在此过程之后，所需的目视检查通常会占用总制造时间的50％，并且检查质量很大程度上取决于检查员。基于深度学习的制造缺陷分类可能会提高过程效率和准确性。但是，这些技术需要数百或数千个训练数据样本。在现实世界的制造过程中，获取如此大量的数据既困难又耗时。因此，提出了一种用于训练神经网络分类器的，增加少量缺陷图像的方法。根据文献，理论上评估了五种传统方法和八种深度学习方法。选定的关于合成图像的多样性和真实性，详细研究了条件深度卷积生成对抗网络和几何变换技术。测试使用了来自六个常见光纤放置检查案例的每个缺陷类别的22到166激光线扫描传感器图像。GAN-Train GAN-Test方法用于验证。研究表明，条件深层卷积生成对抗网络与先前的几何变换结合使用，非常适合从少于50个实际输入图像生成大型现实数据集。提出的网络体系结构和相关的训练权重可以作为将演示的方法应用于其他光纤铺层检查图像的基础。