Session: CIE-25-01 - Graduate Student Poster Symposium

Paper Number: 97834

97834 - Exploring Mechanical Property Space by Developing Novel Multi-Material Filled Cellular Structures 

Designing cellular structures can be categorized into deterministic or stochastic approaches. Their representative volume element generally is a porous unit cell, and they are well known for their interesting mechanical behaviors. We are using a broad range of cellular structures with both stochastic and deterministic designs characteristics in this work. Triply periodic minimal surfaces (TPMS), and Fourie serries function (FSF) based cellular structures are considered the deterministic family. Spinodoid and Voronoi-based cellular structures are members of the stochastic family. The idea is to fill the cellular structure’s RVE porous with different materials and study the change in the mechanical behavior of the cellular structure. These materials can have a low Poisson’s ratio, low densities, high energy absorption, and directional-dependent stiffness. They can have more than one of these characteristics simultaneously. The novelty of this work is the selection of the cellular structure types. There is no previous research on multi-material filled TPMS, FSF, Spinodoid, or Voronoi cellular structure. After developing cellular structures’ mechanical behaviors by numerical and experimental analysis, we will introduce them to topology optimization to take full advantage of their unique properties for different applications. Considering our experience and background with cellular materials, we believe that exploring multi-material-filled cellular structures makes it possible to find materials with unique mechanical properties that could have never been seen in conventional materials or single-material cellular structures. 

Presenting Author: Sina Rastegarzadeh University of Illinois at Chicago

Presenting Author Biography: Sina got his bachelor’s degree in mechanical engineering from Yasouj University in 2015 and his Master of Science in 2018 from Shiraz University. He has an extensive background in optimization, starting with his master thesis, optimizing a novel modular flywheel storage system to reduce energy waste in the metro train network. He is a second-year Ph.D. student in the Mechanical and Industrial engineering department at the University of Illinois at Chicago. In his Ph.D. program, Sina focuses on multi-scale topology optimization and developing novel cellular structures with interesting mechanical behaviors. He is also working on integrating topology optimization with neural networks. His research interests are topology optimization, cellular materials, computer-aided design, machine learning, and neural network.

Authors:

Sina Rastegarzadeh University of Illinois at Chicago