A team of Cyclone Engineers are the recipients of a national award in part because of advancements they have made in the field of nondestructive evaluation, specifically in thermal imaging.
Hantang Qin, assistant professor of industrial and manufacturing systems engineering (IMSE) at Iowa State University, is the lead on the project titled “Data fusion approaches to improve real-time capabilities of in-situ NDE via a thermal imaging case study on a hybrid machine.” Qin, who is also affiliated with Iowa State’s Center for Nondestructive Evaluation (CNDE), is supported by Zhan Zhang, associate scientist with CNDE, and Xiao Zhang (no relation to Zhan), Ph.D. candidate in IMSE.
“Our research goal with this project is to utilize in-situ thermal imaging and data analytics to reduce defects during the direct energy deposition, or DED, processes,” said Qin. “In this project, we will utilize thermal imaging to record the thermal history of the melting pool during manufacturing. Through correlation studies, we are trying to establish a relationship between in-situ thermal data and off-line CT data.”
Qin, who also oversees the Flexible Electronics and Additive Printing Lab, said they would eventually like to predict porosity generation based on in-situ thermal information and make real-time corrective actions to ensure manufacturing quality. Zhan Zhang, who has a M.S. in nuclear engineering from Georgia Tech University, brings his expertise in CT data analytics to this project, while Xiao Zhang is exploring the possibilities of developing in-situ NDE capabilities for the DED process. Xiao Zhang said he could utilize concepts and methods from courses like IE 544: Micro/Nano Scale Additive Manufacturing and IE 547: Biomedical Design and Manufacturing for this research project.
“These two courses provided me knowledge about innovative design, cutting edge problems, and various nondestructive evaluation methods for additive manufacturing,” he said.
Defects such as porosity and cracks can generate internally during the printing processes of metal additive manufacturing. The traditional method to detect such defects is CT scanning, similar to how an x-ray scans bones in humans and other animals. However, according to Qin, CT scanning equipment usually cannot scan large parts because the x-ray may not effectively penetrate the object to acquire usable scanning results. CT scanning equipment is also large in size, making it difficult to scan big metal objects.
“We are trying to utilize in-line data analytics to predict the quality of printed parts, making it possible to replace traditional CT scanning partially. What’s more important, the in-situ data can be utilized to create feedback controls for the additive manufacturing systems,” Qin said.
In September 2020, the trio received a 2020 Fellowship Award from the American Society of Nondestructive Testing, a professional organization focused on creating “a safer world by advancing scientific, engineering, and technical knowledge in the field of nondestructive testing.” The engineers will receive $20,000 to support their research.
“I was super excited and grateful when I heard about winning this national award,” said Xiao Zhang. “It is a fantastic recognition of our lab’s hard work and a great honor for my academic career.”
Work on this project began in September 2020 and it will be supported through May 2021, when Xiao Zhang plans to complete his Ph.D. After graduation he hopes to pursue a postdoctoral opportunity or a faculty position in academia.