Tanmay Pharlia
Papers
1
Total Citations
4
H-Index
1
About
Tanmay Pharlia is a researcher focused on the intersection of industrial robotics, precision manufacturing, and applied machine learning. His work centers on enhancing the performance and reliability of robotic systems through data-driven optimization. In his most cited study, "Performance analysis of accuracy and repeatability of IRB1410 industrial robot using taguchi analysis with machine learning approach" (2023), Pharlia pioneered a novel hybrid methodology that combines Taguchi robust design with machine learning algorithms to systematically evaluate and improve the positional accuracy and repeatability of the IRB1410 robot. This contribution is significant for industries requiring high-precision automation, such as aerospace and electronics assembly, where even micron-level deviations can lead to costly defects. By demonstrating how statistical experimental design can be integrated with predictive modeling, his work provides a practical framework for optimizing robotic performance without extensive physical trials. With 4 citations to date, this paper has already attracted attention from researchers in manufacturing engineering and robotics, establishing Pharlia as an emerging voice in the field of intelligent automation and quality control.
Research Focus
Key Achievements
Top Papers
- 1