Browse Topic: Factory automation
Dufour Aerospace designs and manufactures an automated tilt-wing aircraft for critical cargo delivery missions. Emphasizing operational efficiency, the platform integrates path generation and tracking techniques tailored for the unique dynamics of tilt-wing flight and builds upon the existing lower level control. While there exist a myriad of methods for high-level aircraft automation ranging from PID to MPC, they often require a trade-off between complexity and the capability to handle non-linear dynamics of the system they are controlling. Hence, a lightweight, deterministic geometric path generation approach using clothoid-based transitions between three waypoints and a robust SO(3)- based path tracking controller adapted for tilt-wing dynamics are presented. Additionally, a high-level automation framework is introduced that includes failure mode handling for GNSS loss and communication breakdowns. This system ensures mission continuity and operational safety while supporting flexible mission planning. The methods are validated through extensive flight testing on both small and large-scale aircraft. The latter prove the scalability, safety, and reliability of the presented solution for tilt-wing aircraft automation and enhance the aircraft's capabilities in real-world emergency response and complex operational scenarios.
The Autoclave processing is commonly used in manufacturing high-performance fibre-reinforced thermoset composite components in the aerospace industry. Variations in the cure cycle, sometimes even apparently minor deviations from the prescribed cure cycle, can harm the laminate properties. Given the costly and time-consuming autoclave manufacturing process, there is a strong need to cure the maximum number of parts in the shortest possible time without compromising quality. In order to achieve high-rate automated manufacturing with the optimized autoclave process, it is important to construct a digital twin modelling approach to mirror the physical composite curing process in the virtual domain based on the integration of high-fidelity multi-physics models. The resulting digital twin includes a thermal CFD model, a thermo-chemo-mechanical module, and an efficient and accurate block coupling between these two modules. The customized Abaqus driven by local and spatial variation of the turbulence-induced heat transfer coefficient (HTC) imposed through one-way coupling determines the thermo-mechanical response in composite parts. Using the developed digital twin tool (SMARTCLAVE), HTC's spatial and temporal variation can be generated digitally without invoking an expensive and time-consuming experimental approach. The predicted local boundary conditions are used in SMARTCLAVE to determine the cure kinetics, temperature distribution, and thermal-mechanical response that drives the residual stress and distortion of composite parts after curing. The accuracy of the digital twin for autoclaving is demonstrated first using a benchmark problem followed by the capability demonstration with a single-part L-beam assembly. The benefits of using the digital twin tool are illustrated via the optimal placement of multiple parts in an autoclave to balance the throughput and quality.
Robomotive and other humanoid robots equipped with 3D vision sensors are playing an increasing role in industrial automation, including in smaller binpicking set-ups where parts are constantly changing.
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