Hairol Nizam Mohd Shah
Papers
21
Total Citations
271
H-Index
9
About
Hairol Nizam Mohd Shah is a robotics and automation researcher whose work centers on computer vision, robotic welding systems, and autonomous robot navigation. Best known for his contributions to weld seam detection and path recognition, he has developed innovative image processing techniques that enable welding robots to autonomously identify, locate, and track weld joint positions — a critical advancement for low-to-medium volume manufacturing environments where manual programming is costly and time-consuming. His most influential work, "Butt Welding Joints Recognition and Location Identification by Using Local Thresholding" (2018), has accumulated 84 citations and demonstrates his expertise in applying thresholding algorithms to solve real-world industrial challenges. Complementing this, his research on autonomous weld seam path detection and feature point extraction using laser and vision sensors has collectively shaped modern approaches to intelligent welding automation. Beyond welding, Hairol Nizam has broadened his research scope to include pipeline inspection robots, leader-follower robotic systems, and object-tracking service robots, reflecting a versatile engineering vision aligned with Industry 4.0 demands. With over 225 cumulative citations across his published works, his contributions represent a meaningful body of knowledge advancing intelligent robotics in both industrial and service contexts.
Research Focus
Key Achievements
Top Papers
- 1
- 2Autonomous detection and identification of weld seam path shape position37 citations · 2017
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- 5Design and Development of Remotely Operated Pipeline Inspection Robot16 citations · 2020
- 6Weld seam feature point extraction using laser and vision sensor13 citations · 2023
- 7Comprehensive review on controller for leader-follower robotic system12 citations · 2019
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