Smart Manufacturing highlights Wu’s 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’s created predictive modeling tools that are key enablers of manufacturing automation, known as Industry 4.0 or the Fourth Industrial Revolution.

“The 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,” Wu says. “These tools also allow engineers to detect manufacturing defects through real-time sensor data and machine learning.”

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’d like to create cost-effective tools to enable machines to work smarter, not harder.

“My 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,” he says. “I hope that my team will contribute to the next industrial revolution.”

The digital edition of the June issue of Smart Manufacturing is now available online. Visit the SME website to view the full list of influential academics.

U.S. News and World Report ranks UCF No. 40 in Industrial/Manufacturing/Systems Engineering and No.71 in Mechanical Engineering.