Session: DFMLC-08-01-Special Session-Design Tool Showcase

Paper Number: 97942

97942 - Design Tool for Tissue Scaffold Design Evaluations Based on Human Design Decisions 

Tissue engineering scaffolds are artificial mechanical structures to help in regrowing tissues that are often manufactured with 3D printing to enable personalization. Design of such scaffolds is a complicated task since an engineer must tune the mechanical as well as biological properties of the scaffold, for instance, effective elastic modulus, tissue growth, and blood vessel growth. In the past, a common way to design scaffolds was through practical experiments. In recent research computational design of scaffolds is also being explored to determine the scaffold behavior before fabrication and enable structural optimization. However, most simulations focus on one instance of the scaffold behavior and optimize the scaffold design based on that behavior, such as only focusing on tissue growth. However, this approach does not ensure the necessary scaffold behavior for the other cases necessary for high performance. For instance, mechanical simulations can be run to find the scaffold design, but that does not predict how the tissue growth of the scaffold will be affected. Our research ventures into the creation of a design tool that simulates mechanical properties as well as biological properties of the scaffold and allows a designer to select a personalized scaffold design that is ready for manufacturing with 3D printing.

Our methodology for design evaluation of scaffolds begins with unit cell inputs, in this case, unit cell type, like Cubic or Body-Centered-Cubic, and unit cell diameter on a micrometer scale. The lattice design is done through Java code that takes the inputs according to human design decisions and evaluates the scaffold properties. A custom python program evaluates the tissue growth in the scaffold according to curvature-based rules (Figure 1A) and another python script evaluates the mechanical stiffness of the scaffold through finite element analysis via Abaqus (Figure 1B) according to the input given by a human user (Figure 1C: Unit Cell Design). These data are fed to the Java code that simulates blood vessel growth and shows the output for all the evaluated mechanical and biological properties of the scaffold along with the lattice design with a possible blood vessel growth scenario in it (Figure 1C: Lattice Design). The blood vessel growth simulation follows agent-based probabilistic modeling based on the pore size and porosity of the unit cells. If the scaffold properties found are suitable for a particular application, the scaffold can be printed for experimental validations (Figure 1D).

The graphical user interface (GUI) that the users can use requires two inputs for our design tool: unit cell type and beam diameter. With these inputs, the GUI can predict the mechanical and biological properties of a designed scaffold. Since personalized medicine is highly suggested in scaffold implantation, this GUI can help in evaluating such personalized scaffolds and can automate the manufacturing which in turn can save time and resources spent on the design and manufacturing of personalized scaffolds.

Presenting Author: Amit Arefin Texas Tech University

Presenting Author Biography: Amit Arefin is a PhD student in Mechanical Engineering at Texas Tech University. His research interests lie in a broad area of engineering design. His current work focuses on the design of scaffolds for tissue engineering.

Authors:

Amit Arefin Texas Tech University
Paul Egan Texas Tech University