John Angelopoulos
Papers
9
Total Citations
172
H-Index
8
About
John Angelopoulos is a leading researcher at the intersection of robotics, digital manufacturing, and Industry 4.0/5.0 paradigms. His work focuses on enhancing the reliability, flexibility, and human-centricity of modern manufacturing systems through digital twins, mixed reality, and predictive maintenance. Angelopoulos’s most cited paper, “Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance” (42 citations), introduces novel frameworks for minimizing robotic manipulator downtime—a critical challenge in automated production. His second most influential work, “Closed-Loop Robotic Arm Manipulation Based on Mixed Reality” (39 citations), pioneers collaborative manufacturing cells where human operators and robots interact seamlessly via augmented reality interfaces. Angelopoulos also contributes to engineering education, designing flexible learning factories to train “Generation 4.0 engineers” (37 citations). His broader portfolio includes innovations in 3D-printed robotic tool changers, machine vision-based quality control, and topology-optimized end effectors. With over 130 total citations and a clear trajectory toward smart, sustainable manufacturing, Angelopoulos is shaping the future of human-robot collaboration in the Industry 5.0 era.
Research Focus
Key Achievements
Top Papers
- 1
- 2Closed-Loop Robotic Arm Manipulation Based on Mixed Reality39 citations · 2022
- 3
- 4
- 5Industry 4.0 and smart manufacturing9 citations · 2022
- 6
- 7
- 8An augmented reality application for robotic cell customization8 citations · 2020
- 9