Rizuan Norhaniza

Papers

Total Citations

H-Index

About

Rizuan Norhaniza is a researcher advancing the frontiers of smart materials and soft robotics, with a primary focus on magnetorheological (MR) foams and magnetic polymer composites. Their most cited work, "Prediction for magnetostriction magnetorheological foam using machine learning method" (2022, 8 citations), introduces a novel approach that combines extreme learning machine algorithms with material science to model the complex mechanical and magnetostrictive behaviors of MR foams. This contribution is pivotal for developing soft sensors and actuators, enabling more precise control in soft robotic systems. By integrating machine learning into material characterization, Norhaniza addresses a critical gap in predicting the performance of these adaptive composites under varying magnetic fields. Their research not only enhances the understanding of MR foam dynamics but also paves the way for more intelligent, responsive devices in biomedical and industrial applications. With a growing citation impact, Norhaniza’s work exemplifies the synergy between computational modeling and experimental material design, offering a compelling pathway for students and researchers interested in the intersection of soft robotics, smart materials, and data-driven engineering.