PSI - Issue 77

Boyu Li et al. / Procedia Structural Integrity 77 (2026) 316–322

317

2

Boyu Li et al. / Structural Integrity Procedia 00 (2026) 000 – 000

Nomenclature diffusivity of polylactic acid (PLA) 11 diffusivity in the direction of -axis 22 diffusivity in the direction of -axis 33 diffusivity in the direction of -axis 1. Introduction

In 3D printing, the communication between CAD models and 3D printers is facilitated through the use of a standard tessellation language (STL) file and slicing software. CAD models are exported as STL files in CAD software, where the surface geometry is represented by triangular facets. These STL files are then processed with slicing software, which segments the model into layers and generates the corresponding G-code for 3D printing. The STL file is written by traversing all facetted faces in the CAD model and outputs the facet normal and the positions of the vertices around the triangle facet (Stroud and Xirouchakis, 2000). Therefore, the resolution of the segmented triangular facet limits the accuracy of STL files. Smaller triangles result in higher accuracy but lead to larger file sizes, while larger triangles reduce the file size at the cost of precision. Consequently, there is information loss during the conversion from CAD models to sliced facets in STL files. When generating G-code with slicing software, the accuracy of the printed model depends on the slicing layer thickness and nozzle path during material deposition. Hanon et al. (2021) indicated that the layer thickness and raster angle had a significant impact on the printing quality. Additionally, Alsoufi et al. (2018) showed that the heat shrinkage of extruded polymer also reduced the printing quality. Montalti et al. (2024) compared the STL model from the original CAD model with the one reconstructed from the G-code. They analysed fine-, dense-, and coarse-mesh STL files, finding that the fine-mesh models produced better-quality slices than coarse and dense meshes. This indicates that there are additional losses during the slicing process. Zouaoui et al. (2021) improved the finite element analysis (FEA) of 3D-printed polymers by assigning material properties to the mesh elements based on the printing orientations extracted from the G-code. Dogbone-shaped tensile specimens with a printing orientation of 0°, 45° and 90° were printed, and their mechanical performance was evaluated through tensile testing. The finite-element (FE) model was generated from a CAD file and subsequently meshed, with the element height set equal to the printing layer height to enhance the model’s accuracy. Material properties, including the Young’s modulus in both longitudinal and transverse directions, the Poisson’s ratio, and the shear modulus, were assigned to each element based on its corresponding printing orientation. However, in this model, manufacturing defects inherent to the 3D printing process were neglected, and the material was assumed to be homogeneous and isotropic. Although the simulation results showed a good agreement with experimental data, this approach primarily focuses on specimens with simple, uniform printing orientations. For printed specimens with more complex infill patterns, such as concentric or gyroid structures, additional modelling strategies may be required to accurately capture the effects of various infill patterns in FEA. Kim and Seo (2023) mentioned that in the water diffusion of 3D-printed composite materials, printing patterns significantly influenced the global diffusion rate. Li et al. (2023), Jiang et al. (2020), and Gagani and Echtermeyer (2018) proved that FEA is an effective method for investigating water diffusion in polymers by simulating transport mechanisms and predicting the moisture uptake. Therefore, developing FE models that accurately represent the geometrical characteristics of 3D-printed polymers is crucial for understanding the water diffusion behaviour at a microscale. Such a model allows detailed analysis of diffusion mass flow and provides insights into the effect of printing parameters on moisture transport. Further, Bacciaglia et al. (2023) reconstructed real 3D geometry models from G-code. The G-code was read line by line in MATLAB, where specific keywords related to nozzle movements, both with and without material extrusion and layer changes, were detected. A Python script was then developed to convert the coordinate data output by MATLAB into a sequential list of commands that define a sweep spine trajectory for the printed paths. The resulting CAD models were exported as STEP files, which are compatible with the most popular FEA software, enabling further