Documentation to Digital Model: A Python Based Simulation of a Drone Assembly Line

Sammanfattning

Digital simulation models serve as the foundational component of Digital Twins for enablingreal-time monitoring, predictive analysis and data driven decision making acrossproduction systems. As industries aim to evolve from Industry 4.0, the demand for flexible,transparent and cost efficient simulation tools continues to grow. Traditional DESplatforms offer strong modelling capabilities, but are often limited by reduced customizabilityand challenges in integrating real-time data streams. To address these limitations,this thesis develops a Python based digital model of a semi-automated drone assemblyline and evaluates its feasibility as a foundation for a future Digital Twin. The model wasimplemented using a modular architecture that replicates the physical system’s conveyor,docking-station logic, timing dependencies and PLC document workflows. Verificationwas performed against the factory’s PLC documentation and validation was conductedthrough scenario based experiments and cross comparison with a similar model in SiemensPlant Simulation. The python based digital model developed successfully reproducesboth structural and behavioural characteristics of the real system, achieving an averageaccuracy of 87% similarity in production time performance and 84% average accuracy instation cycle time, though station cycle time performance varied between 60% to 98% invarious scenarios. These findings demonstrate that Python provides a customizable andtransparent platform for developing Digital Twin simulation models in discrete manufacturingenvironments.