Abstract —The fabrication of patient-specific tissue engineering scaffold is highly appreciated that requires prior estimation of porous and mechanical characteristics. Architectural controllability and reproducibility are also essential aspects in the development of 3D functional scaffolds. This work presents a computational approach to determine porous and mechanical characteristics of 3D scaffolds. The computational modeling could be a powerful tool to assist designing 3D scaffold with optimum characteristics as required for a particular patient in need. The 3D scaffolds were successfully modeled investigating the influences of design parameters on the porous and mechanical properties via finite element analysis (FEA) and ANSYS application software. It was revealed by ANSYS that the increase in porosity decreased the mechanical properties and increased the damping factor. The Scaffold porosities were obtained in the range of 47% to 95% with varying pore shape and size by modulating lay-down pattern, filament diameter and filament distance. Index Terms—Tissue engineering, scaffold, rapid prototyping, finite element analysis. I. INTRODUCTION Tissue engineering (TE) and guided tissue repair are very rapidly developing new areas of science. TE is evolving discipline that seeks to repair, replace, or regenerate specific tissues or organs by translating fundamental knowledge in physics, chemistry, and biology into practical and effective materials, devices, systems, and clinical strategies [1], [2]. The principles of TE is that tissues can be isolated from a patient, expanded in tissue culture and seeded into a scaffold prepared from a specific building material to form a scaffold guided three-dimensional (3D) tissue. The construct can then be grafted into the same patient to function as a replacement tissue [3]. Many scaffolds used as medical implants and for TE purposes are fabricated by conventional methods (i.e., expanded grafts, textile weaves and braids, porous films, and sponges). These methods are limited in that they typically generate scaffolds with simple macro-architectures and homogeneous microstructures [4]. Critical variables in scaffold design and function include the bulk material or materials from which it is made, the 3D architecture, the surface chemistry, the mechanical properties, the initial environment in the area of the scaffold, and the late scaffold environment, which is often determined by degradation Manuscript received April 30, 2013; revised July 22, 2013. Yong L. Chuan is with the INTI International University (e-mail: yonglengchuan@gmail.com). MD. E. Hoque and Ian Pashby are with the University of Nottingham Malaysia Campus. characteristics. 3D porous scaffolds promote new tissue formation by providing a surface and void volume that promotes the attachment, migration, proliferation, and desired differentiation of connective tissue progenitors throughout the region where new tissue is needed [5], [6]. Jaecques et al. (2004) [7] have performed a stress–strain aalysis of complete scaffolds via FEA to investigate the state of stress and strain within the scaffolds and its interaction with the surrounding tissues [8], [9]. Such analysis can be used to vary several geometrical or material parameters at the same time and to choose the most suitable ones for the replacement of natural tissues [7]. Simulations of perfusion bioreactors have been investigated for 3D scaffold performances [10], [11], [12]. Lacroix, D., and Prendergast, P.J. (2002) [13], [14] studied tissue differentiation and bone regeneration as functions of the porosity, Young’s modulus, dissolution rate, load condition, and recommended these as the possible optimal scaffold parameters that can be controlled by RP fabrication. II. METHOD AND PROCEDURE Fig. 1. TE scaffolds with different architectures Fig. 2. Schematic diagram for porosity calculation Cubic porous scaffolds (25mm × 25mm × 25mm) were designed with various architectures by varying design Prediction of Patient-Specific Tissue Engineering Scaffolds for Optimal Design Yong L. Chuan, Md. E. Hoque, and Ian Pashby International Journal of Modeling and Optimization, Vol. 3, No. 5, October 2013 468 DOI: 10.7763/IJMO.2013.V3.322