End-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data
This addresses the need for adaptable and fast inline control in composite material manufacturing, offering a scalable solution compared to existing handcrafted methods, though it is incremental as it applies known deep learning techniques to a specific domain.
The paper tackles the problem of defect detection in automated fiber placement (AFP) by formulating it as an image segmentation task and using artificially generated data for training, resulting in a method that scales well to new defect types and measurement devices with minimal real-world data.
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.