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About

Konstantinos Georgoulias is a prominent researcher specializing in Digital Twin technology, predictive maintenance, and advanced manufacturing systems. His work sits at the intersection of physics-based modeling, prognostics and health management (PHM), and industrial robotics, where he has made significant contributions to how modern manufacturing facilities anticipate and prevent equipment failures. Georgoulias is perhaps best known for his pioneering methodologies that bridge Digital Twin concepts with real-world predictive maintenance applications. His 2019 paper on Digital Twin for predictive maintenance has accumulated over 411 citations, establishing him as a leading voice in the field, while a companion methodology paper from the same year has garnered an additional 233 citations — a remarkable dual impact that underscores the foundational nature of his contributions. His subsequent work on integrating degradation curves into physics-based models for industrial robots (115 citations) extended these frameworks to address the persistent challenge of limited historical data in manufacturing environments. More recently, Georgoulias has advanced the concept of dynamic Digital Twins capable of evolving virtual models in real time. Collectively, his research has reshaped how engineers approach equipment lifecycle management, offering manufacturers practical, physics-informed tools to maximize plant availability and operational efficiency.