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About

Keivan Ahmadi is a mechanical engineering researcher whose work sits at the intersection of robotics, structural dynamics, and advanced manufacturing. He has established himself as a leading authority on the dynamics of robotic machining, with a particular focus on understanding and mitigating vibration and chatter phenomena that arise when industrial robots perform high-force operations such as milling and drilling. Ahmadi's most influential contribution, "Modelling the dynamics of industrial robots for milling operations" (2019), has accumulated 178 citations, reflecting the field's pressing need for accurate dynamic models as manufacturers increasingly look to replace rigid CNC machine tools with more flexible robotic systems. His research addresses a fundamental challenge: industrial robots, unlike conventional machine tools, exhibit significant structural flexibility and nonlinearity, making vibration prediction and control exceptionally difficult. His work on pose-dependent frequency response functions, regenerative chatter, axial vibrations, and nonlinear structural behavior has collectively built a rigorous analytical framework for robotic machining dynamics. More recently, his application of Gaussian Process Regression to estimate in-process dynamic properties signals a productive integration of machine learning into manufacturing research. Ahmadi's body of work provides both industry practitioners and academic researchers with essential tools for deploying robots reliably in precision machining environments.