Prediction of geometric characteristics in polycaprolactone (PCL) scaffolds produced by extrusion-based additive manufacturing technique for tissue engineering. Issue 2 (11th September 2019)

• Record Type: Journal Article
• Authors: Lee, Jiwoon; Walker, Jesse; Natarajan, Sanjay; Yi, Sung
• Is Part Of: Rapid prototyping journal. Volume 26:Issue 2(2020)
• Page Start: 238
• Page End: 248
• Publication Date: 2019-09-11
• Subjects: Additive manufacturing -- Scaffold -- 3D bioprinting; Engineering design -- Periodicals; 620.004205
• DOI: 10.1108/RPJ-08-2018-0219
• Languages: English
• ISSNs: 1355-2546

Abstract

Abstract : Purpose: Extrusion-based additive manufacturing (AM) has been considered as a promising technique to fabricate scaffolds for tissue engineering due to affordability, versatility and ability to print porous structures. The reliability and controllability of the printing process are necessary to produce 3D-printed scaffolds with desired properties and depend on the geometric characteristics such as porosity and pore diameter. The purpose of this study is to develop an analytical model and explore its effectiveness in the prediction of geometric characteristics of 3D-printed scaffolds. Design/methodology/approach: An analytical model was developed to simulate the geometric characteristics of scaffolds produced by extrusion-based AM using fluid mechanics. Polycaprolactone (PCL) was chosen as a scaffold material and was assumed to be a non-Newtonian fluid for the model. The effectiveness of the model was verified through comparison with the experimental results. Findings: A comparison study between simulation and experimental results shows that strut diameter, pore size and porosity of scaffolds can be predicted by using extrusion pressure, temperature, nozzle diameter, nozzle length and printing speed. Simulation results demonstrate that geometric characteristics have a strong relationship with processing parameters, and the model developed in this study can be used for predicting the scaffold properties for the extrusion-based 3D bioprinting process. Originality/value: The present study provides a prediction model that can simulate the printing process by a simple input of processing parameters. The geometric characteristics can be predicted prior to the experimental verification, and such prediction will reduce the process time and effort when a new material or method is applied.