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. David Simonton
Third Advisor
Dr. Yousef Sardahi
Abstract
The advancement of additive manufacturing technologies has opened new avenues for fabricating biocompatible and structurally functional bone tissue scaffolds, essential in treating osseous fractures, defects, and diseases. This research aims to develop mechanically strong, dimensionally precise, and patient-specific porous bone tissue scaffolds that provide both structural integrity and biological functionality. Through three interconnected studies, several key challenges in the fabrication of these structures using the PME additive manufacturing process are addressed. First, the influence of polysaccharide and hydroxyapatite concentrations on the compressive modulus of PME-printed porous scaffolds is investigated. By creating structures with varying hydroxyapatite and polysaccharide compositions, the study seeks to identify the optimal material formulations for enhanced strength. It was found that increasing hydroxyapatite concentrations decreased scaffold stiffness, while increasing the polysaccharide concentration increased the scaffold stiffness. It was observed that there was a correlation between the density and the compressive modulus of the structures. Second, a robust computational framework is developed to simulate the bioink deposition process using a CFD model in Ansys Fluent. This study focuses on evaluating the effects of print speed on the deposition characteristics and behaviors, including material deposition, velocity, pressure, and wall shear stress. The primary objective is to understand and optimize deposition parameters through a comprehensive analysis of volume fraction, velocity, pressure, and wall shear stress contours. It was found that the deposited bead width was much more greatly affected than the bead height. Additionally, the high wall shear stress within the nozzle may cause some of the cells within the bioink to die. Third, a convolutional neural network (CNN) is developed for real-time quality monitoring during the PME biofabrication process. This part of the research aims to detect extrusion quality issues such as normal extrusion, over-extrusion, and under-extrusion, ensuring high-quality production. The CNN model is trained using a dataset of images from the printing process. Hyperparameters such as batch size, convolutional layers, dense layers, and training epochs are varied to determine their impact on model performance. Performance metrics, including accuracy, loss, validation accuracy, validation loss, and F-score, are used to evaluate and experimentally optimize the model, enabling its application for real-time quality control. It was found that the number of training epochs had the most significant influence on performance, even leading to possible overfitting. By achieving these objectives, this research enhances the reliability and precision of scaffold fabrication, ultimately contributing to the production of high-quality, patient-specific tissue-engineered products that meet clinical standards. Through the integration of material composition studies, computational flow simulations, and advanced quality monitoring techniques, this work aims to advance the field of bone tissue engineering and provide a foundation for future clinical applications.
Subject(s)
Mechanical engineering.
Biomedical engineering.
Three-dimensional printing.
Additive manufacturing.
Tissue engineering.
Bones.
Computational fluid dynamics.
Machine learning.
Recommended Citation
O’Malley, Ethan, "Optimal additive fabrication of patient-specific bone tissue scaffolds through material formulation, computational flow simulation, and material deposition monitoring" (2024). Theses, Dissertations and Capstones. 1909.
https://mds.marshall.edu/etd/1909
