Anders Skoogh
Papers
2
Total Citations
15
H-Index
2
About
Anders Skoogh is a leading researcher in sustainable manufacturing and production system simulation, with a focus on the intersection of data-driven maintenance and emerging energy technologies. His work centers on developing predictive maintenance frameworks that leverage deep learning, exemplified by his highly cited 2022 study on applying LSTM models to robot cells, which demonstrates how industrial digitalization can reduce downtime and enhance production capacity. Skoogh has also made significant contributions to understanding battery production systems, authoring a 2023 state-of-the-art review that maps current technologies and future developments in this critical sector. His research directly addresses the challenges of Industry 4.0, showing how massive datasets can be transformed into actionable strategies for operational efficiency and sustainability. With over a decade of influence in production engineering, Skoogh’s work is essential for students and researchers interested in smart manufacturing, predictive analytics, and the green transition of industrial systems.
Research Focus
Key Achievements
Top Papers
- 1A Predictive Maintenance Application for A Robot Cell using LSTM Model11 citations · 2022
- 2Battery Production Systems: State of the Art and Future Developments4 citations · 2023