Collision Prediction Framework Using 3D Geometrical Digital Twins and Smart Manufacturing Middleware

This paper presents a framework for predicting and preventing machine collisions using key concepts of smart manufacturing, including middleware and digital twins. The proposed approach integrates trajectory prediction, collision detection in 3D geometrical digital twins, and real-time feedback to machines. A digital twin interfaced with MTConnect middleware was used to reconstruct 3D geometries of the shop floor machine, and the Oriented Bounding Box (OBB) algorithm was employed to detect potential collisions. Future machine trajectories were predicted using a cross-correlation method that could compare the current trajectory with a reference motion cycle. By simulating these trajectories within the digital twin environment, potential collision points were identified in advance, enabling proactive avoidance strategies. The framework was validated through experimental scenarios, demonstrating its ability to accurately predict collision events and mitigate risks. Additionally, this approach can lead to enhanced fail-safe environments, streamlined production lines, and boosted productivity in smart factories by eliminating downtime caused by collisions and improving overall operational reliability.