Daniel Forndran
Papers
1
Total Citations
20
H-Index
1
About
Daniel Forndran is a researcher at the forefront of non-destructive testing and robotic inspection, with a primary focus on advancing computed tomography (CT) for industrial applications. His key research areas include robot-guided CT systems, trajectory optimization, and the practical deployment of inspection technologies for large-scale or complex components. Forndran’s major contribution lies in developing practical, part-specific trajectory optimization methods that enable robots to autonomously navigate around objects—such as automotive joining components within assembled cars—to capture high-quality CT scans without the constraints of traditional gantry systems. His most-cited work, "Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography" (2022, 20 citations), addresses the critical challenge of adapting inspection paths to irregular geometries, significantly improving scan efficiency and image fidelity. This innovation has direct implications for the automotive industry, allowing for the volumetric, non-destructive evaluation of parts in situ. Forndran’s research bridges robotics and imaging, offering scalable solutions for quality control in manufacturing. His achievements highlight a commitment to translating complex algorithms into real-world inspection workflows, making him a notable figure in applied robotics and industrial CT.
Research Focus
Key Achievements
Top Papers
- 1