Zoi Arkouli
Papers
15
Total Citations
603
H-Index
11
About
Zoi Arkouli is a researcher specializing in digital twins, predictive maintenance, industrial robotics, and AI-driven manufacturing systems. Her work sits at the intersection of advanced physics-based modeling, machine learning, and intelligent automation, making her a notable contributor to the broader Industry 4.0 landscape. Arkouli's most influential contribution is her 2019 methodology for enabling Digital Twin technology in predictive maintenance applications, which has garnered over 230 citations and established a foundational framework widely adopted by subsequent researchers. Building on this, her work on integrating degradation curves into physics-based models for industrial robots (115 citations) addresses a critical gap in data-scarce industrial environments, offering practical prognostic tools for maximizing plant availability. Her research extends into flexible robotic manipulators, deformable object handling, and human-robot collaboration quality quantification, reflecting a broad and evolving research agenda. She has also contributed to AI-enhanced vision systems for quality control and large-part manufacturing, demonstrating applied impact across real-world production settings. With a growing body of work spanning dynamic Digital Twin methodologies, cooperative robotics, and artificial intelligence in automation, Arkouli's research consistently bridges theoretical modeling with industrial applicability, making her scholarship particularly valuable for students and practitioners navigating modern smart manufacturing challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Towards accurate robot modelling of flexible robotic manipulators30 citations · 2021
- 6
- 7On the quantification of human-robot collaboration quality27 citations · 2023
- 8
- 9Artificial Intelligence in Manufacturing Equipment, Automation, and Robots18 citations · 2023
- 102-Stage vision system for robotic handling of flexible objects17 citations · 2021