Structured Preparation of Aerospace Nacelle Production for Robotic Sanding Integration

Abstract

In aerospace nacelle manufacturing, manual sanding during surface preparation is a key task. The sanding of nacelle components is both ergonomically demanding and time-intensive, which highlights its potential for automation. While automation is common in aerospace production, surface preparation has remained largely manual due to the high sensory precision required. Prior research has mainly addressed the technical aspects of sanding automation, whereas the preparatory steps necessary for successful system integration have been overlooked. This thesis therefore investigates how nacelle surface preparation in aerospace can be systematically prepared to enable the efficient integration of an advanced sensor-based robotic sanding system. To address the research goal, a multi-level approach was applied. An initial activity sampling revealed sanding as the most time-consuming task in the surface preparation department. Building on this, a pre-feasibility study indicated that automation is economically viable, particularly as a semi-automated solution. In the following concept phase, five key integration challenges were identified and systematically addressed through process standardization, workflow streamlining, and layout optimization. The resulting preliminary concept included a modular fixture design and an optimized integration layout, offering a cost-effective integration foundation. Finally, a structured partner selection process, based on interviews and a scoring model, identified the most suitable integrator. This thesis thus contributes to closing the research gap in production preparation for automation in aerospace surface preparation. The developed method combines economic, technical, and organizational perspectives and is transferable beyond the specific case of nacelle sanding, for example to aerospace structures or interior surface preparation. The results provide practical insights into how preparatory measures can enable efficient robotic integration.

Date of Award	2025
Original language	English
Supervisor	Holger Gröning (Supervisor)