Integration of correction factors for 3D printing errors in FEM simulations for the precise mechanical analysis of single-layer auxetic scaffolds using a wavy pattern for tissue engineering

Abstract

Biofabrication through additive manufacturing plays a key role in tissue engineering, particularly in the creation of scaffolds with porous structures that mimic the properties of native human tissues. These scaffolds are typically designed assuming ideal geometries without defects. However, during the production process, various defects can arise that affect the mechanical properties of the structure. Such defects include filament deposition irregularities, interactions with previously printed layers, and factors like temperature, layer height, and printing speed.

This study focuses on the fused deposition modeling (FDM) technique, where material is added layer by layer to create six auxetic structure models, referred to as the “wavy” model. Each model consists of single-filament geometries with varying curve amplitudes, which were 3D-printed and subjected to both experimental tensile testing and computational simulations. The goal of the experimental tests was to determine Young’s modulus (E) and Poisson’s ratio, while the computational simulations were performed using the Finite Element Method (FEM) to simulate ideal geometries for validation and to correct experimental errors.

The in silico stiffness was found to be consistently lower than the experimental results. Upon inspection of the printed structures using confocal microscopy, two main errors were identified: the intersection area of the filaments was larger than expected in the printed plane, and the overlap in the transversal section was incomplete. Based on these observations, two correction factors were derived to adjust the FEM simulations, improving the alignment between computational and experimental results.

By incorporating these correction factors, the discrepancy between experimental and simulated results was reduced from 14% to 3%. This approach provides a novel framework for enhancing the accuracy of mechanical characterizations of auxetic scaffolds, with a particular focus on their application in tissue engineering.