Analyzing the effect of infill density on the mechanical compression of ASA in additive manufacturing: a FEM perspective

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Abstract

Additive manufacturing (AM) represents a novel method for parts manufacturing, revolutionizing the design principles and processes. Among the different AM methods, fused filament fabrication (FFF) is one of the most widely employed and affordable, with numerous applications across a broad range of fields. Inherently, due to the fundamental physical mechanisms occurring during part building, the material acquires different properties compared to those of bulk material. Simultaneously, parameters such as the infill pattern and infill density significantly affect the overall behavior of the part. An efficient and effective tool to minimize the necessity for experimental investigations and to define the mechanical properties with respect to these parameters (i.e., infill density and pattern) is the finite element method (FEM). In the current study, accurate FEM models were developed and presented, considering the precise geometry of compression specimens for simulating the compression behavior of FFF-printed ASA polymer. More specifically, honeycomb infill patterns with different infill densities were simulated, and the results were validated by direct comparison to respective experimental results. It was deduced that utilizing an appropriate mesh size leads to higher precision and also increases the stability of the numerical simulation, while the FEM models can predict the loads as well as the deformed geometric shapes for different infill densities. As an overall conclusion, it is proved and reasoned that employing FEM and a proper modeling approach is indeed a feasible and efficient way to predict and define the compressive behavior of FFF parts.

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Karkalos, N. E., Rydzoń, K., Papazoglou, E. L., & Karmiris-Obratański, P. (2024). Analyzing the effect of infill density on the mechanical compression of ASA in additive manufacturing: a FEM perspective. International Journal of Advanced Manufacturing Technology, 134(5–6), 2815–2832. https://doi.org/10.1007/s00170-024-14297-x

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