Silvester Dian Handy Permana
Papers
1
Total Citations
30
H-Index
1
About
Silvester Dian Handy Permana is a researcher advancing intelligent manufacturing through the integration of machine learning and sensor-based process monitoring. His primary research areas include tool wear prediction, abrasive belt grinding, and the application of deep learning to industrial machining processes. His most cited work, “A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data” (2021, 30 citations), introduces a novel methodology that leverages convolutional neural networks to monitor tool wear in real time. By analyzing force and vibration signatures from the grinding process, Permana addresses the challenge of unpredictable abrasive grain orientation and grit size variation—a critical issue in precision manufacturing. This contribution has significant implications for reducing downtime, improving surface quality, and enabling predictive maintenance in automated polishing systems. His work sits at the intersection of mechanical engineering and artificial intelligence, offering practical solutions for Industry 4.0. With growing citation impact and a focus on data-driven process optimization, Permana is establishing himself as a notable voice in smart manufacturing and condition monitoring research.
Research Focus
Key Achievements
Top Papers
- 1