Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Messimer, Sherri L.; Pereira, Tais Rocha; Patterson, Albert E.; Lubna, Maliha; Drozda, Fabiano Oscar

doi:10.3390/jmmp3010006

Cited by 30 publications

(18 citation statements)

References 43 publications

(63 reference statements)

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“…In the present work, dimensional error values of up to 2.6% are obtained. Messimer et al [42] reported slightly higher values of up to 3% for high-temperature polylactic acid (HTPLA).…”

Section: Regression Model For Dimensional Errormentioning

confidence: 99%

Effect of Printing Parameters on Dimensional Error, Surface Roughness and Porosity of FFF Printed Parts with Grid Structure

2021

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Extrusion printing processes allow for manufacturing complex shapes in a relatively cheap way with low-cost machines. The present study analyzes the effect of printing parameters on dimensional error, roughness, and porosity of printed PLA parts obtained with grid structure. Parts are obtained by means of the fused filament fabrication (FFF) process. Four variables are chosen: Layer height, temperature, speed, and flow rate. A two-level full factorial design with a central point is used to define the experimental tests. Dimensional error and porosity are measured with a profile projector, while roughness is measured with a contact roughness meter. Mathematical regression models are found for each response, and multi-objective optimization is carried out by means of the desirability function. Dimensional error and roughness depend mainly on layer height and flow rate, while porosity depends on layer height and printing speed. Multi-objective optimization shows that recommended values for the variables are layer height 0.05 mm, temperature 195 ºC, speed 50 mm/min, and flow rate 0.93, when dimensional error and roughness are to be minimized, and porosity requires a target value of 60%. The present study will help to select appropriate printing parameters for printing porous structures such as those found in prostheses, by means of extrusion processes.

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“…In the present work, dimensional error values of up to 2.6% are obtained. Messimer et al [42] reported slightly higher values of up to 3% for high-temperature polylactic acid (HTPLA).…”

Section: Regression Model For Dimensional Errormentioning

confidence: 99%

Effect of Printing Parameters on Dimensional Error, Surface Roughness and Porosity of FFF Printed Parts with Grid Structure

2021

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“…The authors attributed this decrement due to variability in the raster. Another important aspect to consider when dealing with geometrical issues is the dimensional error to evaluate the manufacturing accuracy [53].…”

Section: Experimental Characterization Of Mechanical Propertiesmentioning

confidence: 99%

Characterization of the Mechanical Properties of FFF Structures and Materials: A Review on the Experimental, Computational and Theoretical Approaches

et al. 2019

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The increase in accessibility of fused filament fabrication (FFF) machines has inspired the scientific community to work towards the understanding of the structural performance of components fabricated with this technology. Numerous attempts to characterize and to estimate the mechanical properties of structures fabricated with FFF have been reported in the literature. Experimental characterization of printed components has been reported extensively. However, few attempts have been made to predict properties of printed structures with computational models, and a lot less work with analytical approximations. As a result, a thorough review of reported experimental characterization and predictive models is presented with the aim of summarizing applicability and limitations of those approaches. Finally, recommendations on practices for characterizing printed materials are given and areas that deserve further research are proposed.

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“…Additive manufacturing or 3D printing techniques allow small batches of parts to be produced directly, economically, and flexibly [1]. There are different additive manufacturing techniques; however, fused deposition modeling (FDM) printers are the most extended due to their low cost and the wide variety of materials that can be used [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Methods for Predicting Surface Roughness in Fused Deposition Modeling Parts

Barrios

Romero

2019

Materials

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3D printing using fused deposition modeling (FDM) includes a multitude of control parameters. It is difficult to predict a priori what surface finish will be achieved when certain values are set for these parameters. The objective of this work is to compare the models generated by decision tree algorithms (C4.5, random forest, and random tree) and to analyze which makes the best prediction of the surface roughness in polyethylene terephthalate glycol (PETG) parts printed in 3D using the FDM technique. The models have been created using a dataset of 27 instances with the following attributes: layer height, extrusion temperature, print speed, print acceleration, and flow rate. In addition, a dataset has been created to evaluate the models, consisting of 15 additional instances. The models generated by the random tree algorithm achieve the best results for predicting the surface roughness in FDM parts.

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Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Cited by 30 publications

References 43 publications

Effect of Printing Parameters on Dimensional Error, Surface Roughness and Porosity of FFF Printed Parts with Grid Structure

Effect of Printing Parameters on Dimensional Error, Surface Roughness and Porosity of FFF Printed Parts with Grid Structure

Characterization of the Mechanical Properties of FFF Structures and Materials: A Review on the Experimental, Computational and Theoretical Approaches

Decision Tree Methods for Predicting Surface Roughness in Fused Deposition Modeling Parts

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