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Simulation of avionics equipment is essential due to the complex nature of its development and integration process. Throughout the development process, executable component models are used to demonstrate the feasibility and the compliance of the system design with respect to its functional requirements. In later development phases, there is the need for system integration tests where a mix of real and simulated equipment is used to verify the overall system behavior. Since Boeing 777 and Airbus A380 programs, IMA1 technology has entered several civil aircraft systems. In recent programs like Boeing 787 and Airbus A350 the number of IMA components has significantly increased. In this paper we present a simulation model for a new IMA component - the common Remote Data Concentrator (CRDC)2, which is developed by Thales-Diehl for the Airbus A350 XWB. Building simulation models of IMA components is in general a challenging task due to their complexity on both software and hardware level.

Strong market growth, upcoming global competition and the impact of customer-requirements in aerospace industry demand for more productive, flexible and cost-effective machining systems. Industrial robots have already demonstrated their advantages in smart and efficient production in a wide field of applications and industries. However, their use for machining of structural aircraft components is still obstructed by the disadvantage of low absolute accuracy and adverse reaction to process loads. This publication demonstrates and investigates different methods for performance assessment and optimization of robot-based machining systems. For conventional Cartesian CNC machining systems several methods and guidelines for performance assessment and error identification are available. Due to the attributes of a common 6-axis-robot serial kinematics these methods of decoupled and separated analysis fail, especially concerning optimization of the system.

The demand for higher production rates in aircraft industry requests more flexible manufacturing solutions. A bottleneck in production is the machining of large components by vast portal machines. Time-consuming referencing processes result in non-satisfying cost-effectiveness of these high-invest-machines. Mobile robot-based solutions are able to operate simultaneously which increases the productivity significantly. However, due to the limited workspace of robots, machining tasks have to be divided and long trajectories are separated in single overlapping segments. Thus high-accuracy referencing strategies are required to achieve desired production tolerances. In this publication different advanced optical reference strategies will be discussed taking the inhomogeneous behavior of a mobile robotic machining system into account.

Due to their unique and favorable properties as well as high strength to weight ratio, composite materials are finding increasing applications in automotive, aircraft and other vehicle manufacturing industries. High demand, production rates and increasing part complexity, together with design variations require fast, flexible and fully automated assembly techniques. In automotive and aircraft manufacturing, widely used bonding and sealing processes are automated using industrial robots due to their speed, flexibility and large working volume. However, there are limitations in achieving complete automation of these processes due to the inherent inaccuracies of the industrial robots, workpiece positioning and process tolerances. Currently, the robot programs are generated in CAD/CAM environment and are adjusted manually according to the actual workpiece.

The demand for higher production rates of large parts in aircraft industry requests more flexible manufacturing solutions. High-accurate mobile robots show a promising alternative in comparison with high-invest special machines. With mobile robot-based solutions processes can be executed simultaneously which increases the productivity significantly. However, the freedom of mobility results in insufficient positioning accuracy of these machines. Hence fast and accurate referencing processes are required to achieve cost-effectiveness and meet production tolerances. In this publication a Mobile Laser Tracker (MLT) system and a holistic approach for future manufacturing systems with mobile robots will be introduced and discussed.

Structural components in fuselage barrels are joined with the help of riveting processes. Concerning the key feature of rivet drill hole size and drilling quality, a poorly executed drilling operation can lead to serious riveting defects such as rivet play or fracture due to non-uniform load distribution. Consequently, the drilling process of a rivet hole and its correct execution is of vast importance for the airworthiness of an aircraft. The condition of the drill used, i.e., the current tool wear, has a direct effect on the quality of the hole. Since conventional approaches, such as changing the tool after a predefined number of process cycles, do not reflect real tool wear, premature wear may occur, resulting in defects. Thus, the online-detection of tool wear for necessitated replacement may indicate a promising future direction in quality control.

Threaded, potted inserts are commonly used as a standard connecting element for sandwich components, which are used for aircraft interior. Since they often offer the only detachable connection, they are used in very high quantities. To ensure a material bond between the inserts and the honeycomb structure, the joint is filled with adhesive. Despite the high number of inserts, this process is performed manually. Recent research has shown new approaches for automated gripping and placement of the inserts by an industrial robot that yield high potential for cost savings and increased productivity. Automated adhesive insertion, so-called potting, has not been considered so far but is an essential contribution to the full automation of the entire process chain. The amount of adhesive varies depending on the type of insert and its position on the honeycomb structure.