Zengxi Pan

Professor Zengxi (Stephen) Pan is an internationally recognised leader in industrial robotics, wire arc additive manufacturing (WAAM), and intelligent manufacturing systems, with a research career defined by strong industrial translation and global impact. He has authored over 220 publications with more than 20,000 citations and is ranked among the world’s leading researchers in WAAM. His work is recognised through inclusion in the Stanford Global Top 2% Scientists list and multiple Highly Cited Papers, reflecting sustained influence across academia and industry. His research is distinguished by the seamless integration of scientific innovation with real-world deployment. His early work at ABB Robotics led to the development of Force Control Machining (FC Machining), a robotic machining technology that progressed from fundamental research to a patented and commercially deployed product. He has since led more than AU$6 million in industry-funded programs through DMTC and CRC initiatives, delivering impactful outcomes for Australia’s defence and advanced manufacturing sectors, recognised by awards including the Eureka Prize (2013) and the Pacific Maritime Innovation Award (2015). Professor Pan has demonstrated a strong track record in translating research into industry impact. His work in Automated Offline Programming contributed to the establishment of Verbotics, while his WAAM research underpinned AML3D, the first ASX-listed company focused on large-scale metal additive manufacturing. These achievements highlight his ability to bridge fundamental research, engineering systems, and commercialisation pathways. He has also established a strong research and training environment, having supervised more than 30 HDR students to completion and currently mentoring a large cohort of research students. His group is recognised for developing engineers who combine rigorous theoretical understanding with practical capability, with graduates progressing into leadership roles across academia and industry. His current research focuses on AI-driven autonomous manufacturing systems, integrating robotics, additive manufacturing, digital twins, and machine learning into intelligent, self-optimising production platforms. This work aims to advance manufacturing beyond conventional automation towards fully autonomous systems capable of adaptive control and assured performance, positioning his research at the forefront of next-generation manufacturing. Manufacturing robotics, Flexible manufacturing systems, Simulation, modelling, and programming of mechatronics systems, Additive manufacturing, Metals and alloy materials