Abdullah Al Masum
Papers
1
Total Citations
6
H-Index
1
About
Abdullah Al Masum is a materials and mechanical engineering researcher whose work centers on tribology, surface engineering, and the application of machine learning to materials performance. His most-cited paper, "Effects of Self-Lubricant Coating and Motion on Reduction of Friction and Wear of Mild Steel and Data Analysis from Machine Learning Approach" (2021, 6 citations), addresses a critical industrial challenge: extending the lifespan of mild steel components under frictional stress. By investigating self-lubricant coatings and motion parameters, Al Masum demonstrates how surface treatments can significantly reduce wear, while integrating machine learning to analyze and predict tribological behavior. This dual approach—combining experimental coating science with data-driven modeling—highlights his contribution to smarter, more durable material design. His work has direct implications for manufacturing, automotive, and heavy machinery sectors, where friction-induced degradation is a major cost driver. Though his citation count is still growing, Al Masum’s research represents a forward-looking fusion of traditional metallurgy and modern computational analysis, positioning him as a promising voice in sustainable, high-performance materials engineering.
Research Focus
Key Achievements
Top Papers
- 1