But that may change in the future through the efforts of a UCF researcher. Dazhong Wu, an associate professor of mechanical and aerospace engineering, has received a Young Faculty Award from the Defense Advanced Research Projects Agency (DARPA) for his project titled �鶹��Ʒ S�Artificial Intelligence-Enabled Affordable and Scalable Additive Manufacturing Part Qualification. �鶹��Ʒ S� The award will include nearly $500,000 of funding for the two-year project with an optional $500,000 for a third year of work, depending on how the research progresses.

The goal of the project is to develop an efficient and cost-effective machine learning model that can predict the defects and mechanical performance of 3D printed materials. Current metal additive manufacturing processes use expensive materials, such as titanium alloys, to build complex, high-performance parts layer-by-layer from digital models. Those parts undergo lengthy trial-and-error testing cycles that result in the destruction of parts and an overall loss of money.

With Wu �鶹��Ʒ S�s novel method that mixes AI with additive manufacturing, the industry can move away from destructive testing and reduce inspection costs.

�鶹��Ʒ S�Using AI we can predict the mechanical performance of 3D printed parts with small amounts of destructive and non-destructive testing data. With this, we can ensure every part is consistent, reliable and less costly. �鶹��Ʒ S�

Once Wu �鶹��Ʒ S�s AI model is built, he hopes it can be implemented in various industries to transform how they manufacture critical components.

�鶹��Ʒ S�I �鶹��Ʒ S�m hopeful this AI-enabled additive manufacturing qualification framework will be used across many industries, including aerospace and, many more, �鶹��Ʒ S� Wu says. �鶹��Ʒ S�Bringing costs down is crucial to the additive manufacturing industry. To do that, we need to make sure every part consistently meets performance requirements. �鶹��Ʒ S�

About the Researcher

Wu joined UCFin 2017 after serving as a senior research associate at Penn State University �鶹��Ʒ S�s Department of Industrial and Manufacturing Engineering. In 2021, the Society of Manufacturing Engineers ranked him among the 20 most influential academics in additive manufacturing. In the College of Engineering and Computer Science, he manages the Additive Manufacturing and Intelligent Systems Lab, where he and his team develop smart manufacturing techniques.

The project depicted is sponsored by the Defense Advanced Research Projects Agency. The content of this article does not necessarily reflect the position or policy of the government and no official endorsement should be inferred.

SME �鶹��Ʒ S�s 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.

�鶹��Ʒ S�Manufacturing is an essential component of economic growth, �鶹��Ʒ S� Wu says. �鶹��Ʒ S�I 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. �鶹��Ʒ S�

Wu joined UCF in 2017 after serving as a senior research associate at Penn State University �鶹��Ʒ S�s Department of Industrial and Manufacturing Engineering. He earned his Ph.D. in mechanical engineering from Georgia Tech and his master �鶹��Ʒ S�s degree from Shanghai Jiao Tong University in China. He manages the at UCF, 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 �鶹��Ʒ S�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 �鶹��Ʒ S�s created predictive modeling tools that are key enablers of manufacturing automation, known as Industry 4.0 or the Fourth Industrial Revolution.

�鶹��Ʒ S�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, �鶹��Ʒ S� Wu says. �鶹��Ʒ S�These tools also allow engineers to detect manufacturing defects through real-time sensor data and machine learning. �鶹��Ʒ S�

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 �鶹��Ʒ S�d like to create cost-effective tools to enable machines to work smarter, not harder.

�鶹��Ʒ S�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, �鶹��Ʒ S� he says. �鶹��Ʒ S�I hope that my team will contribute to the next industrial revolution. �鶹��Ʒ S�

The digital edition of the June issue of Smart Manufacturing is now available online.

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