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About

Xiaojian Zhang is a dynamic researcher at the intersection of robotics, advanced manufacturing, and intelligent measurement systems. His work spans robotic machining, path planning, and precision measurement — areas where he has made meaningful contributions to both theoretical foundations and practical applications. Zhang's most impactful recent contributions center on robotic grinding and milling processes. His dual flexible contact material removal model addresses the complex dynamics of robotic disk grinding, while his investigation of mode-coupling chatter in robotic milling — leveraging dynamic mode decomposition — advances understanding of stability in flexible robotic manufacturing systems. Together, these papers have already accumulated over 30 citations since their 2024 publication, signaling rapid uptake in the field. His work on spiral complete coverage path planning using conformal slit mapping offers an elegant mathematical solution for robotic cavity machining in multi-connected domains, generating smoother, more efficient tool paths. Complementing this, Zhang has developed robust calibration and pose optimization methods for mobile robotic measurement systems, enhancing 3D full-field measurement accuracy. Earlier work on biped robot gait planning reveals the breadth of his robotics expertise. Across his career, Zhang demonstrates a consistent commitment to bridging computational intelligence with real-world robotic manufacturing challenges — making his research essential reading for engineers advancing smart manufacturing systems.