Anas Alazzam

Khalifa University of Science and Technology

Papers

Total Citations

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About

Anas Alazzam is a researcher focused on the intersection of advanced manufacturing, robotics, and computational mechanics. His work primarily addresses the precision and predictive modeling challenges in hybrid kinematic systems and novel material structures. A key contribution is his development of external kinematic calibration methods for hybrid kinematics machines, specifically those utilizing lower-degree-of-freedom planar parallel mechanisms, as detailed in his 2019 paper (11 citations). This work enhances the accuracy of complex robotic systems through sophisticated algorithms like nonlinear least squares. More recently, Alazzam has pioneered the use of deep artificial neural networks to predict the mechanical behavior of triply periodic minimal surfaces (TPMS) under damage loading (2024, 7 citations). This research is critical for advancing TPMS applications in robotics, biomedical engineering, and impact energy absorption, offering a data-driven approach to understanding material failure. His work demonstrates a strong commitment to integrating machine learning with mechanical design, providing tools for more resilient and efficient engineering systems.