An interdisciplinary team of Texas A&M University researchers has been awarded a $1 million National Science Foundation grant to research data mining to optimize decision making in the software brain of the Autonomous Experimentation Platform for Accelerating Manufacturing of Advanced Materials.
Dr. Yu Ding, professor in the industrial and systems engineering department, is the principal investigator of the team while the co-principal investigators are Drs. Satish Bukkapatnam, professor in the industrial and systems engineering department and director of the Texas A&M Engineering Experiment Station’s Institute for Manufacturing Systems; Raymundo Arroyave, a materials science and engineering professor; and Xia (Ben) Hu, assistant professor in the computer science and engineering department.
The project introduces artificial intelligence and autonomy modules into an autonomous experimentation platform to mimic a human scientist's ability to handle surprising observations, synthesize diverse bodies of knowledge and explore a large, complex design space. The key research components in this platform are organized around three capability themes: exploitation to efficiently determine the most promising regions of a design space, exploration to recognize and reason about surprises arising from unusual designs and the expansion of newly discovered design spaces based on mining new knowledge from literature and databases, while preferentially gaining knowledge in regions likely to contain superior material design solutions.
The proposed system is cognizant, adaptive, knowledge-rich and taskable, interacting with human scientists by way of simple commands and executing an autonomous discovery process with a minimal and appropriate degree of human intervention.
“Once the machine is able to data mine past literature of the design space, its knowledge base will surpass that of a group of industry experts,” said Hu. “This will enhance the artificial intelligence decision making function of the software brain.”
His work in data mining began four years ago when he began researching automated machine learning to replicate human efforts in a task.
The autonomous experimental testbed could have significant impacts on engineering practice and revolutionize the material discovery and advanced manufacturing landscape.