Supachoke Manigpan

University of the Thai Chamber of Commerce

Papers

Total Citations

H-Index

About

Supachoke Manigpan is a researcher whose work lies at the intersection of robotics, artificial intelligence, and industrial automation. His most cited paper, "A simulation of 6R industrial articulated robot arm using backpropagation neural network" (2010, 11 citations), addresses a fundamental challenge in robotics: inverse kinematics for six-degree-of-freedom articulated arms. By integrating the Denavit-Hartenberg modeling approach with backpropagation neural networks, Manigpan demonstrated how machine learning can efficiently solve complex kinematic equations that are computationally intensive for traditional methods. This work has practical implications for improving the precision and adaptability of industrial robots used in manufacturing and assembly lines. Beyond this key contribution, his research portfolio reflects a sustained interest in applying computational intelligence to mechanical systems, bridging theoretical modeling with real-world engineering applications. While his citation count is modest, the focused nature of his work—particularly in neural network-based robotic control—offers a valuable foundation for students and researchers exploring how AI can enhance traditional robotics. Manigpan’s contributions highlight the ongoing synergy between soft computing techniques and industrial robotics, a field with growing relevance in smart manufacturing.