Pruefcubing of the Future: Digital Twin Development for Automotive Measurement Integration in Pre-Series Production

Abstract

This thesis details the development of a bidirectional Cyber-Physical Bridge for the BMW Group Center Measurement Room (CMR), integrating real-time physical data into a high-fidelity Unreal Engine 5 environment. By utilizing a Flask-based REST API and JSON protocols, the system transforms a static 3D model into a dynamic Digital Twin (DT) that reflects "as-manufactured" realities with sub-millimeter precision. A core technical contribution is the implementation of Atomic Write Integrity, which secures sensitive master records during data exchange and fosters user trust among engineering teams. Performance benchmarking confirms the system is industrially viable, achieving a stable 34.38 FPS and low-latency API response times of 8.7 ms on standard hardware. The framework addresses critical research gaps regarding the synchronization of heterogeneous measurement sources and the dependency on expensive physical prototypes. Furthermore, the virtual environment enables "fly-through" inspections of internal structural alignments that are physically inaccessible in the real world. Ultimately, this research provides a scalable foundation for the "Speed Cubing" initiative, aiming to accelerate automotive production cycles through enhanced virtual validation and human-system collaboration. This innovative approach demonstrates how Digital Twin technology can effectively replace resource-intensive physical procedures while maintaining the high accuracy required for automotive pre-series production.