Numerical and experimental analysis of bead cross-sectional geometry in fused filament fabrication

Abstract

Purpose This paper aims to develop experimentally validated numerical models to accurately characterize the cross-sectional geometry of the deposited beads in a fused filament fabrication (FFF) process under various process conditions. Design/methodology/approach The presented numerical model is investigated under various fidelity with varying computational complexity. To this end, comparisons between the Newtonian, non-newtonian, isothermal and non-isothermal computational models are presented for the extrusion of polylactic acid material in an FFF process. The computational model is validated through an experimental study on an off-the-shelf FFF printer. Microscope images of experimentally printed FFF bead cross-sections corresponding to various printing conditions are digitally processed for the validation. In the experimental study, common practical printing conditions for an FFF process are tested, and the results are compared to the numerical model. Findings Microscope image analyses of the cross-sectional geometries of deposited beads show that the numerical model provides a precise characterization of the cross-sectional geometry under varying process parameters in terms of the cross-section outline, bead height and width. The results show that the nozzle-to-table distance has a great effect on the bead shape when compared to the extrusion rate at a given nozzle-to-table distance. Comparison of the various computational models show that the non-Newtonian isothermal model provides the best tradeoff between computational complexity and model accuracy. Originality/value The authors provide detailed computational models, including the extruder nozzle geometry for cases ranging from Newtonian isothermal models to non-Newtonian non-isothermal models with experimental validation. The validation study is conducted for practical process parameters that are commonly used in FFF in practice and show that the computational models provide an accurate depiction of the true process outputs. As the developed models can accurately predict process outputs, they can be used in further applications for process planning and parameter tuning.

Stress Flow Guided Non-Planar Print Trajectory Optimization for Additive Manufacturing of Anisotropic Polymers

“…Similarly, print trajectory planning for non-planar robotic deposition is studied in [20], ignoring, however, stress flow alignment and further manufacturability constraints for the printing; the same holds for [13]. Full end-to-end implementation for non-planar FFF optimization requires knowledge of the extrusion dynamics [21,22], machine kinematics [15,23], and efficient trajectory optimization formulations [24,23] that consider process constraints [15], material, printed geometry [25,26,27], and stress flow field [14]. Consequently, improving the non-planar printing process performance is not trivial and requires developing advanced methods to optimize print path trajectories for arbitrary geometries under a given stress flow field and material extrusion constraints.…”

Stress Flow Guided Non-Planar Print Trajectory Optimization for Additive Manufacturing of Anisotropic Polymers

“…On most current applications, the challenge is partially caused by a lack of in-situ sensors, control-oriented models, and closed-loop control strategies [3]. A possible approach to improve performance is through modeling printed bead outputs as a function of input parameters [4], [5]. Similarly, extrusion dynamics models can be used for feedforward input optimization to improve dimensional accuracy [6].…”

“…Layer-to-layer measurements of FFF prints have been used for accurate process modeling [3] and parameter optimization [11]. However, layer-to-layer updates are ineffective for rejecting fast-acting run-time disturbances to the extrusion flow in the layer, which has a great influence on the resulting printed material shape [4], [12], [13]. Recent developments consider in-layer measurements of the material flow within the extruder [14].…”

“…Similarly, print trajectory planning for non-planar robotic deposition is studied in [13] and in [20]; however both works ignore stress flow alignment and manufacturability constraints. End-to-end implementation of non-planar FFF optimization requires knowledge of the extrusion dynamics [21,22], machine kinematics [23], and efficient trajectory optimization formulations [24] that consider process constraints [15], material, printed geometry [25][26][27], and stress flow field [14].…”