Effect of Lattice Structure and Composite Precursor on Mechanical Properties of 3D-Printed Bone Scaffolds

Abstract

This article presents an investigation on designing and fabricating scaffolds with different structures, desired porosity, composition, and surface area to volume ratio (SA:V) for orthopedic applications by using the computer-aided design (CAD) and the stereolithography (SLA) 3D printing technique. Different triply periodic minimal surfaces (TPMS) and functionally graded lattice structures (FGLS) were designed based on various cell geometries. Finite element analysis (FEA), tensile and compression tests were carried out, and the results are presented. Two different resin compositions were used to print the models and compare the effect of resin precursors on the mechanical properties of scaffolds. The first was a biodegradable resin made from soybean oil commercially available on the market (made by Anycubic Co.). The second was a mixture of biodegradable UV-cured resin with 5% W/W of hydroxyapatite (HA) and 5% W/W calcium pyrophosphate (CPP). Bio-Hydroxyapatite and Bio-Calcium Pyrophosphate were obtained from eggshells waste and characterized using XRD and FESEM. The obtained data show that adding resin precursors (HA/CPP) slightly decreases the mechanical strength of printed scaffolds; however, considering their extraordinary effect on bone regeneration, this small effect can be ignored, and HA/CPP can be used as an ideal agent in bioscaffolds.

Review on vat photopolymerization additive manufacturing of bioactive ceramic bone scaffolds

“…Hydroxyapatite (HA) is a biomimetic calcium phosphate ceramic material with a chemical composition similar to that of natural apatite found in natural bones and teeth. Natural apatite is the main inorganic component of natural bones and teeth, 124,125 which accounts for about 65% and 95%, respectively. As a result, HA has been extensively studying in the field of bone scaffold development.…”

Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network

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