3.8 Proceedings Paper

Microscale representative volume element based numerical analysis on mechanical properties of fused deposition modelling components

Journal

MATERIALS TODAY-PROCEEDINGS
Volume 39, Issue -, Pages 563-571

Publisher

ELSEVIER
DOI: 10.1016/j.matpr.2020.08.469

Keywords

FDM; Elastic characteristics; Micro-scale numerical analysis; Cross-section morphology; RVE homogenisation

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This study numerically investigates the impact of infill density, layer height, and raster orientations on the elastic behavior of FDM components, and develops a predictive model. The study reveals that FDM components exhibit directional behavior under different parameter settings. The predictive model can be used to optimize FDM components for functional applications.
Among additive manufacturing techniques, Fused Deposition Modelling (FDM) has emerged as an economical method capable of fabricating functional components. The thermoplastic filament material used in FDM and the fabrication criterion such as infill density, layer height, raster orientation etc. influence the print attributes. The directional characteristics of the component needs to be determined for designing functional components. The primary aspiration of the study is to numerically investigate the influence of infill density, layer height and raster orientations on the elastic behaviour of FDM components. The study also develops a predictive model to estimate the elastic characteristics of FDM components. Using cross-section morphology of FDM print, micro-scale Representative Volume Element (RVE) are modelled for 0/90 degree raster configuration. The characteristics determined over the RVE could be considered as the characteristics of the FDM component. The components exhibited directional behaviour with higher layer height at lower infill densities. The significance of the build parameters and their relationship on the characteristics of the part is estimated. The Response Surface Methodology (RSM) based predictive model developed can anticipate the directional peculiarities of FDM prints. This predictive model could be effectively used for optimizing the components for functional applications. (C) 2020 The Authors. Published by Elsevier Ltd.

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