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About

Onur Denizhan is a researcher specializing in the kinematic synthesis and dynamic modeling of mechanical linkages and robotic systems, with a strong emphasis on the application of artificial neural networks (ANNs) to classical engineering problems. His work bridges traditional mechanism design—such as four-bar linkages and slider-crank mechanisms—with modern computational intelligence, offering novel regression and analysis techniques that enhance accuracy and efficiency. Denizhan’s most-cited paper, “Two-Position Synthesis of the Four-Bar Planar Linkage Mechanisms Using Artificial Neural Networks” (2023, 5 citations), introduces ANN-based methods for synthesizing fundamental mechanisms used in vehicle components, rehabilitation robotics, and engines. He further extends this approach to dynamic modeling in “Application of Various Artificial Neural Network Algorithms for Regression Analysis in the Dynamic Modeling of a Three-Link Planar RPR Robotic Arm” (2025, 2 citations), and to position analysis in “Position Analysis of the Slider-Crank (R-RRT) Mechanism Using Artificial Neural Networks” (2023, 2 citations). By integrating machine learning into mechanism design, Denizhan provides accessible, data-driven alternatives to traditional graphical and analytical solutions, making his work valuable for students and researchers in robotics, mechanical design, and computational kinematics.