Mochamad Denny Surindra
Papers
1
Total Citations
27
H-Index
1
About
Mochamad Denny Surindra is a researcher at the forefront of intelligent manufacturing, specializing in machine learning applications for industrial process monitoring and optimization. His work focuses on predictive maintenance and quality control in robotic machining, particularly abrasive belt grinding—a critical process in automated manufacturing. Surindra’s most-cited paper (2024, 27 citations) pioneers the use of machine learning models to predict abrasive belt wear during robotic arm grinding, addressing a longstanding challenge where coarse grain deterioration compromises workpiece integrity. By developing data-driven approaches for real-time condition monitoring, his research bridges the gap between traditional wear analysis and modern AI-driven automation. This work has immediate implications for reducing downtime, improving surface quality, and extending tool life in smart factories. Surindra’s contributions are notable for their practical relevance to Industry 4.0, demonstrating how predictive algorithms can transform maintenance strategies in high-precision manufacturing. His growing citation record reflects the increasing demand for intelligent monitoring systems that enhance efficiency and reliability in automated production environments.
Research Focus
Key Achievements
Top Papers
- 1