Prediction and examination of the impact of the raster angle on the orthotropic elastic response of 3D-printed objects using a novel homogenization strategy based on the real clustering of RVEs

Abstract

The distinct process of layer-by-layer 3D printing generates an anisotropic distribution of mechanical properties. The use of computational models to predict the mechanical properties of 3D-printed objects made by fused filament fabrication (FFF) has received little attention despite a large literature on experimental approaches. The primary objective of this study is to investigate the application of multi-scale computational models in order to achieve precise predictions of the mechanical properties of observed components. A numerical homogenization method is used in combination with clustering algorithms, notably the “K-means” algorithm, to adjust the predicted behavior of the parts to the actual properties resulting from this process as a function of the variation of the raster angle. This method analyzes samples’ internal structure at the micro- and mesoscales. K-means algorithms classify observations to create representative volume elements (RVEs) that match the reel morphologies of the interlayer cavities. The data is utilized to create micromechanical models that calculate effective orthotropic constants based on filament orientation; the derived constants are then used to develop macroscale numerical models that simulate the mechanical response of 3D-printed samples subjected to tensile stress. In summary, the findings suggest that employing the homogenization technique is a reliable approach for forecasting the elastic behavior of 3D-printed elements. Moreover, it is imperative to utilize existing models, such as homogenization utilizing Green’s functions or homogenization based on ideal geometry-material models, in order to obtain an initial approximation of the elastic response of 3D-printed components. Furthermore, the methodology employed in this study, which combines the homogenization process with intelligent clustering algorithms, effectively minimizes the error between numerical simulations and experimental findings. This, in turn, improves the development of precise predictive models that accurately represent the elastic properties of structures fabricated using FFF. This methodology has the potential to be implemented across all materials utilized in FFF manufacturing. This study presents trustworthy prediction laws that enable the designer to conduct a quicker iterative analysis and select the ideal printing process parameters based on FE analysis in order to create high-quality 3D FFF-printed components.