The aerospace industry has established the Automated Fiber Placement process as a common technique for manufacturing fibre reinforced components. In this process multiple composite tows are placed simultaneously onto a tool. Currently in such processes manual testing requires often up to 50% of the manufacturing duration. Moreover, the accuracy of quality assurance varies significantly with the inspector in charge. Thus, inspection automation provides an effective way to increase efficiency. However, to achieve a proper inspection performance, the segmentation of layup defects need to be examined. In order to improve such defect detection systems, this paper performs a comprehensive ranking of segmentation techniques. Thus, 29 statistical, spectral and structural algorithms from related work were evaluated based on nine substantial criteria as assessed from literature and process requirements. For reasons of determinism and easy technology transferability without the need of much training data, the development of new Machine Learning algorithms is not part of this paper. Afterwards, seven of the most auspicious algorithms were studied experimentally. Therefore, laser line scan sensor depth maps from fibre placement defects were utilised. Furthermore noisy images were generated and applied for testing algorithm robustness. The test data contained five defect categories with 50 samples per class. It was concluded that Adaptive Thresholding and Cell Wise Standard Deviation Thresholding work best yielding detection accuracies mostly \(> 97\)%. Noteworthy is that influenced input data can affect the detection results. Feasible algorithms with sensible parameter settings were able to perform reliable defect segmentation for layed material.

航空航天业已将自动纤维铺放工艺确立为制造纤维增强部件的常用技术。在此过程中，将多个复合丝束同时放置到工具上。当前，在这样的过程中，手动测试通常需要多达制造时间的50％。此外，质量保证的准确性因主管检查员的不同而有很大差异。因此，检查自动化提供了一种提高效率的有效方法。但是，为了获得适当的检查性能，需要检查叠层缺陷的分割。为了改进这种缺陷检测系统，本文对分割技术进行了综合排名。因此，有29个统计数据，根据文献和工艺要求评估的九项重要标准，评估了相关工作的光谱和结构算法。出于确定性和易于技术转让的原因，而无需大量训练数据，新的机器学习算法的开发不在本文的范围之内。之后，对7种最吉祥的算法进行了实验研究。因此，利用了来自纤维放置缺陷的激光线扫描传感器深度图。此外，产生了噪声图像，并将其用于测试算法的鲁棒性。测试数据包含五个缺陷类别，每个类别有50个样本。结论是 新的机器学习算法的开发不是本文的一部分。之后，对7种最吉祥的算法进行了实验研究。因此，利用了来自纤维放置缺陷的激光线扫描传感器深度图。此外，产生了噪声图像，并将其用于测试算法的鲁棒性。测试数据包含五个缺陷类别，每个类别有50个样本。结论是 新的机器学习算法的开发不是本文的一部分。之后，对7种最吉祥的算法进行了实验研究。因此，利用了来自纤维放置缺陷的激光线扫描传感器深度图。此外，产生了噪声图像，并将其用于测试算法的鲁棒性。测试数据包含五个缺陷类别，每个类别有50个样本。结论是自适应阈值处理和明智的标准偏差阈值处理最能产生检测精度，最大为\（> 97 \）％。值得注意的是，受影响的输入数据可能会影响检测结果。具有合理参数设置的可行算法能够对铺设的材料执行可靠的缺陷分割。