Wrinkle and boundary detection of fiber products in robotic composites manufacturing

Kurzfassung

The paper focuses on a vision-based approach to advance the automated process of manufacturing Airbus A350’s pressure bulkhead. The proposed method utilizes information of RGB and infrared images as well as depth measurements to identify the wrinkles and boundary edge of semi-finished dry fiber products on the double-curved surface of a flexible modular gripper used for laying the fabric. The work is a sequential advancement over two of the previous projects namely AZIMUT [1] and a collaboration between the University of British Columbia (UBC), Canada and German Aerospace Center (DLR), Germany [2] towards increased automation of composite aircraft manufacturing. The proposed technique implements a simple and practical image processing solution utilizing a sequence of pixel-wise binary masks on an industrial scale setup; it bridges the gap between laboratory experiments and real-world execution. The setup enables automated deformation and draping of a fiber textile on a form-variable endeffector. During the draping process, tensions developed in the fibers of the textile cause wrinkles on the surface, which are highly detrimental to the production process, material quality and strength. The proposed method automates the identification and detection of the wrinkles and the textile on the gripper surface. The efficacy of the technique is demonstrated via experiments in the proposed work.