Yisha Xiang, associate professor in the Industrial Engineering Department of the Cullen College of Engineering, has received a grant from the University of Houston's Advanced Manufacturing Institute to apply machine learning methods to control the manufacturing process of high temperature superconductors (HTS).
Her proposal, “Statistical Machine Learning Methods for Analysis and Control of the HTS Manufacturing Process,” was funded for $34,971. It is the second project on superconductor research by the Industrial Engineering Department selected by the AMI for funding, in addition to a proposal from professors Ying Lin and Qianmei Feng.
According to the abstract for the project, the goal of the research is to develop efficient and effective statistical machine learning methods to control the HTS manufacturing process.
“We aim to achieve this objective by creating novel and advanced models and algorithms to quantify the effects of the process variables dynamically on the quality and properties of HTS tapes and to predict the shift in the HTS manufacturing process so that process adjustment can be made in advance,” Xiang wrote. “HTS has great potential to enable a number of groundbreaking science and technologies such as the game-changing compact fusion reactors and the highly anticipated aircraft motors.”
While HTS has shown this potential to revolutionize these industries, a barrier to wide adoption has been the high manufacturing costs of HTS tapes.
“Accurate real-time data analysis and process control tools are central to achieving high manufacturing yield for superconductors and reducing the manufacturing cost of HTS tapes,” she wrote. “The successful development of the new and advanced statistical machine learning methods in this project will provide a viable path to the low-cost manufacturing of 2G HTS tapes.”
Xiang joined the Cullen faculty in Fall 2022. She earned an NSF CAREER award in 2020, for her research proposal, “Enhancing Environmental and Economic Sustainability of Additive Manufacturing-Based Remanufacturing.”
Research will continue on the project through the year. For more information on Xiang and her research, visit her lab's website.