The identification and correction of manufacturing defects, particularly gaps
and overlaps, are crucial for ensuring high-quality composite parts produced
through Automated Fiber Placement (AFP). These imperfections are the most
commonly observed issues that can significantly impact the overall quality of
the composite parts. Manual inspection is both time-consuming and
labor-intensive, making it an inefficient approach. To overcome this challenge,
the implementation of an automated defect detection system serves as the
optimal solution. In this paper, we introduce a novel method that uses an
Optical Coherence Tomography (OCT) sensor and computer vision techniques to
detect and locate gaps and overlaps in composite parts. Our approach involves
generating a depth map image of the composite surface that highlights the
elevation of composite tapes (or tows) on the surface. By detecting the
boundaries of each tow, our algorithm can compare consecutive tows and identify
gaps or overlaps that may exist between them. Any gaps or overlaps exceeding a
predefined tolerance threshold are considered manufacturing defects. To
evaluate the performance of our approach, we compare the detected defects with
the ground truth annotated by experts. The results demonstrate a high level of
accuracy and efficiency in gap and overlap segmentation.

本文介绍了一种使用光学相干断层扫描和计算机视觉技术来检测和定位复合材料零件中的缝隙和重叠的新方法，通过检测每个制带的边界，我们的算法可以比较相邻制带并识别可能存在的缝隙或重叠，在预定的容差阈值以上的缝隙或重叠被认为是制造缺陷。通过将检测到的缺陷与专家标注的真实数据进行比较评估，结果显示在缝隙和重叠分割方面具有高精度和高效率。

自动化纤维放置中的间隙与重叠检测

Gap and Overlap Detection in Automated Fiber Placement

Current defect detection systems for Automated Fibre Placement (AFP) are
mostly based on end-to-end supervised learning methods requiring abundant
labelled defective samples, which are not easily generated in sufficient
numbers. To address this data scarcity problem, we introduce an
autoencoder-based approach compatible with small datasets. Fortunately, the
problem from a foundational point of view can be simplified as a binary
classification between normal and abnormal samples. The proposed approach uses
a depth map of the fibre layup surface, split into small windows aligned to
each composite strip (tow). A subset of these windows that do not contain
anomalies is passed to an autoencoder to reconstruct the input. Because the
autoencoder is trained with normal samples, it produces more accurate
reconstructions for these samples than for abnormal ones. Therefore, the value
of reconstruction error is used as a quantitative metric for whether there are
potential anomalies. These values are combined to produce an anomaly map, which
can localize the manufacturing defects in the depth map. The results show that
although the autoencoder is trained with a very limited number of scans, the
proposed approach can produce sufficient binary classification accuracy and
specify the location of the defects.

该研究提出了一种基于自编码器的方法来检测自动化纤维铺放中的制造缺陷，并通过对深度图的处理能够确定缺陷位置并达到足够的二元分类准确性，克服了数据匮乏问题。

自动纤维放置中的异常检测：数据限制下的学习

Anomaly Detection in Automated Fibre Placement: Learning with Data  Limitations

The occurrence of manufacturing defects in wind turbine blade (WTB)
production can result in significant increases in operation and maintenance
costs and lead to severe and disastrous consequences. Therefore, inspection
during the manufacturing process is crucial to ensure consistent fabrication of
composite materials. Non-contact sensing techniques, such as Frequency
Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as
they offer a full view of these complex structures during curing. In this
paper, we enhance the quality assurance of manufacturing utilizing FMCW radar
as a non-destructive sensing modality. Additionally, a novel anomaly detection
pipeline is developed that offers the following advantages: (1) We use the
analytic representation of the Intermediate Frequency signal of the FMCW radar
as a feature to disentangle material-specific and round-trip delay information
from the received wave. (2) We propose a novel anomaly detection methodology
called focus Support Vector Data Description (focus-SVDD). This methodology
involves defining the limit boundaries of the dataset after removing healthy
data features, thereby focusing on the attributes of anomalies. (3) The
proposed method employs a complex-valued autoencoder to remove healthy features
and we introduces a new activation function called Exponential Amplitude Decay
(EAD). EAD takes advantage of the Rayleigh distribution, which characterizes an
instantaneous amplitude signal. The effectiveness of the proposed method is
demonstrated through its application to collected data, where it shows superior
performance compared to other state-of-the-art unsupervised anomaly detection
methods. This method is expected to make a significant contribution not only to
structural health monitoring but also to the field of deep complex-valued data
processing and SVDD application.

本文利用频率调制连续波（FMCW）雷达作为一种非破坏性感知模式来增强制造质量保障，并开发了一种新的异常检测管道，它能在去除正常数据先验信息后专注于异常特征的属性