S. Ragu Nathan

About

S. Ragu Nathan is a researcher specializing in the automation and optimization of robotic gas metal arc welding (GMAW). His work focuses on enhancing weld quality and productivity through empirical modeling and process parameter optimization. Nathan’s key contributions include developing MATLAB-based linear regression models to predict weld bead geometry from critical parameters like arc current, voltage, and welding speed. He also applied Taguchi techniques to optimize these parameters for IS 2062 E250 BR steel, achieving superior joint quality. His review on machine vision for robotic welding highlights the potential of vision systems to automate and improve shop-floor manufacturing. Though his citation counts are modest (ranging from 3 to 4 per paper), Nathan’s research provides foundational insights for advancing robotic welding precision and efficiency. His work is particularly valuable for students and engineers seeking practical, data-driven approaches to weld automation and quality control in industrial applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Robotics GMAW-weld Bead Geometry Modeling Using MATLAB Script Approach

   4 citations · 2015
2. 2
   Optimization of Process Parameters of Robotic GMAW of IS 2062 E250 BR Using Taguchi Techniques

   3 citations · 2014
3. 3
   Development of empirical models for prediction of weld bead geometry in robotic - GMAW

   3 citations · 2014
4. 4
   Automation of robot welding using Machine vision - A review

   3 citations · 2010

Key Collaborators

Contact & Links