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The mechanical performances of 3D-printed parts are influenced by the manufacturing variables. Many studies experimentally evaluate the impact of the process parameters on specimens’ static and dynamic behavior with the aim of tailoring the mechanical response of the prints. However, this experimental approach is hampered by the very large number of parameters, 3D printers and materials, the development of computer simulation models being thus required. In the context, this study aims to fill a gap by experimentally investigating the influence of infill related parameters over the vibrations of 3D-printed specimens, as well as to propose and validate a parametric finite element (FE) model for the prediction of eigenfrequencies.

A generally applicable FE model is not yet available for the 3D printing technology based on the material extrusion process due to the large number of parameters settings that determine a large variability of outcomes. Hence, the idea of developing numerical simulation models that address sets of parameters and assess their impact on a certain mechanical property. For the natural frequency, the influence of the infill density and infill line width is studied in this paper. An FE script that automates the generation of the model geometry by using the considered set of parameters is developed and run. The results of the modal analysis are compared to the experimental values for validating the script.

Based on the experimental results, a linear regression between the weight of the part and the first natural frequency is established. The response surfaces indicate that the infill density is the most significant parameter of influence. The weight-frequency function is then used for the prediction of the natural frequency of specimens manufactured with other infill parameters and values, including different infill patterns.

As the malfunctions or mechanical damages can be caused by the resonant vibration of parts during use, this research develops a FE-parameterized model that evaluates and predicts the eigenfrequencies of 2D printed parts to prevent these undesirable events. The targeted functional applications are those in which 3D-printed polymer parts are used, such as drone arms or drone propellers.

This research studies the influence of process parameters on the natural frequency of 3D-printed polylactic acid specimens, a topic scarcely addressed in literature. It also proposes a new approach for the development of parameterized FE models for sets of parameters, instead of a general model, to reduce the time and resources allocated to the experimental tests. Such a model is provided in this paper for evaluating the influence of infill parameters on 3D prints eigenfrequency. The numerical model is validated for other infill settings.