"Numerical design, fabrication, and characterization of porous tissue s" by Brandon Coburn

Date of Award

2024

Degree Name

Mechanical Engineering

College

College of Engineering and Computer Sciences

Type of Degree

M.S.M.E.

Document Type

Thesis

First Advisor

Dr. Roozbeh “Ross” Salary

Second Advisor

Dr. James B. Day

Third Advisor

Dr. Nalini Santanam

Abstract

With the recent advancements within biomedical engineering of bone tissue scaffolds, there is still a need to develop mechanically robust and biocompatible with low immunogenicity for bone regeneration. Additionally, the evaluation of the fluid dynamics of the porous Triply Periodic Minimal Surfaces (TPMS) bone scaffold also shows the need for investigation due to the complex fluid interaction of hemodynamics that occurs with the scaffold internal and external domains. To aid in the development of treating bone fractures, defects, and diseases. Furthermore, with the induction of a wide variety of TPMS architecture that yields different topologies, the Convolutional Neural Network (CNN) model will aid in predicting the TPMS scaffold characteristic to help develop critical design parameters. Thus, this research has observed biocompatible and mechanically strong materials with bone regeneration applications by evaluating polyamide, polyolefin, and cellulose fibers (PAPC) and SimuBone biomaterial. The TPMS scaffolds are fabricated by fused deposition modeling (FDM) additive manufacturing. Furthermore, the evaluation of fluid dynamics of internal and external effects using the computational fluid dynamics (CFD) method is used to observe the fluid interaction of the TPMS scaffold. Therefore, ANSYS (Fluent with Fluent Meshing) software captures the pressure, wall shear stress, and velocity streamline characteristics. As for the bone scaffold topology prediction, machine learning CNN is used and developed within Python to observe these properties. Accuracy, loss, validation accuracy, validation loss, and FScore will be recorded to aid in developing the hyperparameters with the CNN platform. Therefore, the findings show that PAPC compression modulus performance observed that Neovius and Schwarz-Diamond designs have higher levels of compression strength than that of Schwarz-Primitive and Schwarz-Gyroid designs. As for SimuBone biomaterial, it was observed to be a suitable bone tissue engineering material due to its robust mechanical performance. Additionally, it is observed that the vertical orientations of P.W. Hybrid showed optimal performance with the compression analysis out of 10 different TPMS designs. It also has suitable mechanical mimicry of human trabecular bone yield strength. The evaluation of the CFD analysis of the internal and external performance of 10 TPMS scaffold designs showed that Schwarz Primitive yielded superior fluid properties. The wall shear stress was the lowest for analysis, with the external cubic evaluation showing Schwarz Primitive has a wall shear stress value of 3.4 mPa. In addition, its fluid pressure performance was suitable for improving cell viability and survival. Furthermore, the CNN evaluations displayed the optima hyperparameter for batch size, convolutional layers, dense layers, layer size, and Epoch training as 16, 6, 3, 32, and 25, respectively. A trend can be discerned within accuracy, loss, validation accuracy, validation loss, and F-Score performance, all yielding improved and consistent performance with the 5- replication analysis. Thus, this research has observed the fluid dynamics, mechanical performance, and topology evaluation of the TPMS bone scaffold. This study will aid in designing and experimenting with bone tissue engineering scaffold development.

Subject(s)

Mechanical engineering.

Biomedical engineering.

Three-dimensional printing.

Additive manufacturing.

Tissue engineering.

Bones.

Computational fluid dynamics.

Machine learning.

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Available for download on Wednesday, September 10, 2025

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