The Society of Manufacturing Engineers has named Assistant Professor of Mechanical Engineering Dazhong Wu one of the 20 Most Influential Academics. He is the only professor from 麻豆原创 and the only academic from the state of Florida to be included on the list, which was published in the latest issue of SME鈥檚 magazine Smart Manufacturing.
SME鈥檚 experts and industry peers selected the honorees for their role in shaping the next generation of manufacturing engineers and technologists across a variety of disciplines. Wu says he feels honored and humbled by this distinction. As an influential academic, he hopes to impress upon his students the important role that smart manufacturing plays in society.
鈥淢anufacturing is an essential component of economic growth,鈥� Wu says. 鈥淚 hope that mechanical engineering students will not only learn the fundamental knowledge of advanced manufacturing, but also become manufacturing engineers who can solve real-world problems.鈥�
Wu joined 麻豆原创 in 2017 after serving as a senior research associate at Penn State University鈥檚 Department of Industrial and Manufacturing Engineering. He earned his Ph.D. in mechanical engineering from Georgia Tech and his master鈥檚 degree from Shanghai Jiao Tong University in China. He manages the at 麻豆原创, where he and his team develop novel smart manufacturing systems as well as improve the reliability and safety of complex systems. His published work has been cited more than 3,600 times, according to Google Scholar.
Smart Manufacturing highlights Wu鈥檚 work in predictive modeling, which uses machine learning and industrial sensors to detect and prevent the manufacturing defects of high-end products such as turbine blades. He鈥檚 created predictive modeling tools that are key enablers of manufacturing automation, known as Industry 4.0 or the Fourth Industrial Revolution.
鈥淭he predictive modeling tools we developed enable engineers to predict the surface roughness and mechanical properties of 3D printed parts as well as cutting tool wear in machining,鈥� Wu says. 鈥淭hese tools also allow engineers to detect manufacturing defects through real-time sensor data and machine learning.鈥�
He and his team are developing tools and processes to fabricate lightweight and high-performance carbon fiber reinforced composite materials that can significantly improve the fuel economy of automobiles and aircrafts. Eventually, he鈥檇 like to create cost-effective tools to enable machines to work smarter, not harder.
鈥淢y vision for the manufacturing industry is that manufacturing machines equipped with low-cost sensors are able to make intelligent decisions automatically based on the knowledge extracted by artificial intelligence techniques,鈥� he says. 鈥淚 hope that my team will contribute to the next industrial revolution.鈥�
The digital edition of the June issue of Smart Manufacturing is now available online.
U.S. News and World Report ranks 麻豆原创 No. 40 in Industrial/Manufacturing/Systems Engineering and No. 71 in Mechanical Engineering.聽
