Rapid advancements of unmanned aerial vehicles (UAVs) have increased the demand for these systems. Recently a new system is appearing on the market called Drone in a Box, and it has been recognised that these systems can potentially be applied across multiple different industries. However, the current systems often lack the necessary modularity to adapt the drone to different use-cases. Therefore, it is necessary to explore how a Drone in a Box system can quickly be adapted for a different industry and reduce the often time-consuming and iterative process of going from the identification of a new requirement to a production-ready configuration.

This master's thesis investigates the efficacy of Knowledge-Based Engineering (KBE) and design automation in accelerating the design phase of a complex, multidisciplinary product. The project simultaneously executed mechanical concept development of a modular Drone in a Box system and the creation of automation tools. The mechanical design followed Liedholm’s product development process to establish a robust, parametrised baseline. The MOKA methodology was applied to capture engineering knowledge, resulting in the development of Python-based automation tools integrated with CATIA V5.

The automation tools were used to develop three different drone configurations, an Inspection Drone, a Cargo Delivery Drone and a Water Sampling Drone, demonstrating the system's adaptability. To automate the configuration process, several KBE tools were created, including a system Configurator, an Airfoil Designer, automated fastener placement, and Bill of Materials (BOM) generation. User testing revealed that many of these tools yielded significant time savings, such as the fastener instantiation where the automated system for placing fasteners on the drone was 13.8 times faster than doing it manually.

Finally, the study concludes that applying design automation to a parametrised, modular architecture drastically reduces the lead time required to adapt a system to unforeseen requirements. By automating geometric updates and downstream manufacturing preparations, the developed tools minimise the risk of human error and significantly accelerate the iterative CAD-CAE loop. Ultimately, this thesis demonstrates the critical synergy between mechanical product development and software automation when achieving an agile engineering design cycle.