Modeling of the Influence of Input AM Parameters on Dimensional Error and Form Errors in PLA Parts Printed with FFF Technology

Abstract

As is widely known, additive manufacturing (AM) allows very complex parts to be manufactured with porous structures at a relatively low cost and in relatively low manufacturing times. However, it is necessary to determine in a precise way the input values that allow better results to be obtained in terms of microgeometry, form errors, and dimensional error. In an earlier work, the influence of the process parameters on surface roughness obtained in fused filament fabrication (FFF) processes was analyzed. This present study focuses on form errors as well as on dimensional error of hemispherical cups, with a similar shape to that of the acetabular cup of hip prostheses. The specimens were 3D printed in polylactic acid (PLA). Process variables are nozzle diameter, temperature, layer height, print speed, and extrusion multiplier. Their influence on roundness, concentricity, and dimensional error is considered. To do this, adaptive neuro-fuzzy inference systems (ANFIS) models were used. It was observed that dimensional error, roundness, and concentricity depend mainly on the nozzle diameter and on layer height. Moreover, high nozzle diameter of 0.6 mm and high layer height of 0.3 mm are not recommended. A desirability function was employed along with the ANFIS models in order to determine the optimal manufacturing conditions. The main aim of the multi-objective optimization study was to minimize average surface roughness (Ra) and roundness, while dimensional error was kept within the interval Dimensional Error≤0.01. When the simultaneous optimization of both the internal and the external surface of the parts is performed, it is recommended that a nozzle diameter of 0.4 mm be used, to have a temperature of 197 °C, a layer height of 0.1 mm, a print speed of 42 mm/s, and extrusion multiplier of 94.8%. This study will help to determine the influence of the process parameters on the quality of the manufactured parts.

How generalisable are material extrusion additive manufacturing parameter optimisation studies? A systematic review

“…PHV can also influence the defects listed: Circularity, Geometrical accuracy, Surface Roughness, Shrinking, and Warping. ↑ [ 25 , 31 , 37 , 53 , 54 , 61 , 65 , 72 , 73 , 88 , 99 , 100 , 109 , 114 , 116 , 117 ] ↓ [ 32 , 34 , 36 , 68 , 71 , 76 , 92 , 106 , 108 , 112 , 118 ] Air Gap (AG) The measured lateral distance between the centre points of deposited adjacent filament strands. AG can also influence the defects listed: Geometrical accuracy, Porosity, and Surface Roughness.…”

“…ET can also influence the defects listed: Circularity, Geometrical accuracy, Porosity, Surface Roughness, Stringing, and Warping. ↑ [ 28 , 37 , 67 , 73 , 77 , 88 , 105 , 114 , 118 , 123 , 125 , 126 , 127 ] ↓ [ 34 , 36 , 38 , 43 , 54 , 68 , 83 , 90 , 92 , 93 , 95 , 102 , 108 , 111 , 112 , 115 , 116 , 117 , 128 ] Nozzle diameter (ND) The fixed diameter of the nozzle orifice, where the filament is extruded. This directly affects the road width.…”

“…ND can also influence the defects listed: Geometrical accuracy and Surface Roughness. ↑ [ 6 , 63 ] ↓ [ 64 , 112 ] Bed temperature (BT) The set temperature of the build envelope that enables adhesion between the material and substrate. BT can also influence the defects listed: Circularity, Geometrical accuracy, Shrinking, Surface Roughness, and Warping.…”

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

“…Multiple studies [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] have explored how factors such as layer thickness, layer height, infill density, and other parameters affect the mechanical properties of these parts. Other studies [ 6 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] are investigating the influence of printing parameters on the dimensional accuracy of 3D-printed parts. Additionally, post-processing heat treatments can be used to further enhance the quality and performance of the printed parts [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ].…”

Accuracy of FDM PLA Polymer 3D Printing Technology Based on Tolerance Fields

“…Their study concludes that linear programming is a functional approach for identifying optimal parameters in 3D printing. Luis-Perez et al [13] investigated multiobjective parametric optimization and suggest maintaining the lowest values for the design of experiments (DOE) for the nozzle diameter, layer height, and temperature (0.4 mm, 0.1 mm, and 195 • C, respectively). Meanwhile, the print speed and extrusion multiplier should be set approximately at their central values (40 mm/s and 95%).…”