Yong Jian Samuel Tan
Papers
1
Total Citations
7
H-Index
1
About
Yong Jian Samuel Tan is a researcher at the forefront of intelligent manufacturing, specializing in the automation of industrial robotic systems. His primary research focuses on automatic toolpath generation (ATG) and the application of deep learning to optimize manufacturing processes. Tan’s most cited work introduces a novel deep learning framework that recommends optimal toolpath patterns for diverse industrial applications—including polishing, deburring, and masking—without requiring manual programming. This contribution directly addresses a critical bottleneck in industrial automation: the time-consuming and expertise-dependent task of toolpath design. By leveraging data-driven models, his system enables robots to adaptively generate customized trajectories, significantly improving efficiency and reducing human error. With 7 citations on his landmark 2021 paper, Tan’s work is gaining traction among researchers and engineers seeking to bridge the gap between artificial intelligence and practical manufacturing. His achievements highlight a promising trajectory in smart robotics, where deep learning meets real-world industrial challenges, making him a notable emerging voice in the field of automated manufacturing and robotic process optimization.
Research Focus
Key Achievements
Top Papers
- 1