End-to-end defect detection in automated fiber placement based on artificially generated data

Abstract

Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network based on recent architectures designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.

Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

“…As they have suggested, we applied a GAN in this paper for artificial data augmentation, which they had briefly mentioned in their publication. Similar to Zambal et al (2019b;2019a), we demonstrated the possibility of generating synthetic AFP inspection topology data. Furthermore, we theoretically evaluated different data augmentation techniques and investigated selected approaches in detail.…”

“…The sufficiency of such a small data set is a key requirement for practical applications, in their opinion. In contrast to Zambal et al (2019b;2019a) they stated that the U-Net architecture from Ronneberger et al (2015) performs much worse for defect segmentation. Luo et al (2020) investigated various GAN based methods to generate synthetic training data especially for unbalanced or very small training data sets for deep learning fault diagnosis systems for produc-tion machines.…”

“…Furthermore they add the fact that a GAN generates artificial data sets as part of its operating principle. Zambal et al (2019a;2019b) introduced an end-to-end deep learning defect detection and segmentation approach for the AFP process inspection considering synthetically generated training data. Therefore, they applied a U-Net CNN structure, which Ronneberger et al (2015) have introduced in 2015.…”

“…In their investigations the classification results from their self developed ANN are more accurate than those of the pre-trained ANN. Within the previously mentioned work from Zambal et al (2019b) and Zambal et al (2019a), they trained their CNN with 5000 synthetic defect samples. Joshi et al (2018) pointed out the disadvantages of individual classifiers such as SVM or ANN for the part inspection.…”

Defect Characteristics and Online Detection Techniques During Manufacturing of FRPs Using Automated Fiber Placement: A Review

“…They used convolutio ural networks (CNNs) to classify the thermal images of the (Carbon Fiber Reinforced Plastic FRP material, which can identify several prepreg materials and different material defects durin e AFP process. Similar work has also been performed in [124]. Zambal et al proposed formulatin Denkena et al [32] presented an online AFP process monitoring method based on a thermal camera with image processing.…”