Session: CIE–31: Graduate Student Poster Symposium

Paper Number: 147053

147053 - Inverse Design of Material Symmetry Inspired Cellular Materials. 

Imagine a world where cellular materials featuring exceptional properties are not only possible but readily available through real-time 3D printing, tailored to meet specific on-demand tasks. Architected cellular materials are materials with precisely engineered microstructures designed to exhibit unique properties not achievable in traditional materials. The recent surge in data-driven research has greatly enhanced the utilization of machine learning in the design process, particularly in the realm of structure-property mapping for periodic cellular materials. However, existing data driven design methods of periodic cellular materials have faced criticism due to their limited design space, ambiguity in structure-property mapping, and challenges in manufacturing the predicted structures. To alleviate these bottlenecks, we propose a new method for generating cellular material datasets that utilizes planes of symmetry. This innovative approach facilitates the creation of a comprehensive cellular dataset, represented in a continuous format suitable for machine learning. Leveraging this dataset, we aim to efficiently invert desired performance into periodic shape design variables using geometric design methods, ensuring frustration-free yet manufacturable geometry, with high speed, accuracy, learning capacity of the prediction. This work promises to pave the way for the development of robust inverse design tools for cellular materials, making a significant contribution to the metamaterial community.

Presenting Author: Mohammad Abu-Mualla University of Illinois Chicago

Presenting Author Biography: I am currently a Ph.D. student in mechanical engineering at the University of Illinois at Chicago. I earned my bachelor's degree in mechanical engineering from the University of Jordan in 2019, receiving the Victorian Award for academic excellence. Following this, I gained experience as a land systems design engineer at JODDB. Motivated by my passion for design, I pursued a Fulbright scholarship for a master's degree in mechanical engineering, focusing on design, and was placed at UIC's Dream Lab in 2021. Now, in my first year of a Ph.D. program, I am actively engaged in research on mechanical metamaterial design and the automation of the design process with the help of machine learning and topology optimization

Authors:

Mohammad Abu-Mualla University of Illinois Chicago