Session: 17-01-01: Research Posters

Paper Number: 150459

150459 - Digital Twin Enabled Layout Optimization Framework for Photogrammetry Cameras in Reconfigurable Robotic Work Cells 

Photogrammetry systems are extensively used in industrial manufacturing as an assistance measurement tool. Their non-contact nature and rapid data acquisition allow for high-precision feedback, playing a crucial role in various applications such as quality control and process inspection. However, the dynamic nature of modern manufacturing environments poses significant challenges for traditional static layouts of photogrammetry systems. Manufacturers are under pressure to improve production efficiency, shorten product lifecycles, and increase product variety, necessitating a shift towards reconfigurable manufacturing systems. To address this challenge, this paper proposes a digital twin based optimization framework that enables the exploration of reconfigurable work cells within a three-dimensional domain - a crucial gap in current research. The framework utilizes a virtual representation of the work cell constructed in Process Simulate, allowing for the learning and exploration of the work cell within a three-dimensional domain. The methodology involves defining a visibility parameter that quantifies how much the target object can be seen by the cameras. This parameter is determined by comparing point clouds generated from camera snapshots with a complete point cloud of the target object generated from a CAD model. To optimize camera positions and maximize visibility, an objective function is defined based on the degrees of freedom of the entire system. Initial numerical optimization using a grid search approach achieves a visibility of 96% within two hours. However, the time requirement for this approach renders it unfeasible for reconfigurable manufacturing systems. To address this limitation, heuristic optimization techniques, including Simulated Annealing, Genetic Algorithm, and Bayesian Optimization, are employed, reducing the time taken to find optimal solutions to under five minutes each while maintaining high visibility results of around 90%. These findings demonstrate the efficacy of the digital twin based optimization framework in addressing the challenges of photogrammetry systems in dynamic manufacturing environments. By leveraging heuristic optimization techniques, manufacturers can efficiently adjust camera positions to maximize visibility, thereby enhancing production efficiency and quality control processes. The framework is designed to be generic, making it applicable to a wide range of reconfigurable layout problems. The key advantages of the framework are the minimal time requirements for system optimization, allowing for quicker work cell adaption, and the elimination of risks associated with physical experimentation around robotics. Future research directions may involve further refinement of optimization algorithms and their application to different manufacturing scenarios, ultimately advancing the field of reconfigurable manufacturing systems.

Keywords: Digital Twin, Layout Optimization, Photogrammetry, Reconfigurable Manufacturing Systems

Presenting Author: Zi Wang University of Nottingham

Presenting Author Biography: Dr Zi Wang is a Research Fellow at the Centre for Aerospace Manufacturing, University of Nottingham. She holds a PhD in Mechanical Engineering, in the topic of aeroelastic modelling for rotorcraft design. Her research mainly focuses on reconfigurable manufacturing systems, photogrammetry system integration, assembly process simulation with high-fidelity digital twin, and design and development of intelligent tooling systems.

Authors:

Stevan Pandurevic University of Nottingham
Zi Wang University of Nottingham
David Sanderson University of Nottingham
Svetan Ratchev University of Nottingham