Accepted Manuscript

A study of parametric calibration for low cost 3D printing:Seeking improvement in dimensional quality

Leonardo Santana, Jorge Lino Alves, Aurélio da Costa SabinoNetto

PII: S0264-1275(17)30852-3DOI: doi: 10.1016/j.matdes.2017.09.020Reference: JMADE 3353

To appear in: Materials & Design

Received date: 1 June 2017Revised date: 2 September 2017Accepted date: 9 September 2017

Please cite this article as: Leonardo Santana, Jorge Lino Alves, Aurélio da Costa SabinoNetto , A study of parametric calibration for low cost 3D printing: Seeking improvementin dimensional quality, Materials & Design (2017), doi: 10.1016/j.matdes.2017.09.020

This is a PDF file of an unedited manuscript that has been accepted for publication. Asa service to our customers we are providing this early version of the manuscript. Themanuscript will undergo copyediting, typesetting, and review of the resulting proof beforeit is published in its final form. Please note that during the production process errors maybe discovered which could affect the content, and all legal disclaimers that apply to thejournal pertain.

ACC

EPTE

D M

ANU

SCR

IPT

A study of parametric calibration for low cost 3D printing:

seeking improvement in dimensional quality

*Leonardo Santana a, Jorge Lino Alves

a, Aurélio da Costa Sabino Netto

b

a Faculdade de Engenharia da Universidade do Porto – Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal;

bInstituto Federal de Santa Catarina, Av. Mauro Ramos, 950, 88020-300, Florianópolis - SC, Brazil.

*Corresponding author: leonardosantana29@gmail.com

Abstract: open source projects have helped extrusion-based Additive Manufacturing processes gain

popularity in recent years. While they allow the design and development of low cost machines, one of

the main difficulties users have found is the parametric calibration. A study was proposed to

understand the best practices for the setup of "input parameters", since in the open software chain

there are many available for setup. Through experimental design methods, the dimensional accuracy

of a cubic structure was analysed by varying factors such as: slicing software, layer thickness, infill

density, first layer, infill and perimeter speeds, as well as extrusion temperature and multiplier. A

Prusa I3 Hephestos printer and a polylactic acid (PLA) filament were used, and the parts were

evaluated with contact measurement, 3D scanning and mass measurement procedures. Statistical

analysis showed that the dimensional accuracy of the components was mostly affected by the infill

density and the extrusion multiplier. Both parameters highlight the influence of the slicing software on

the planning and quality of the models. Instabilities in the amount and flow of material, characterized

by excess deposition, were responsible for the distortions along the three fundamental directions of the

cubes.

Keywords: Additive Manufacturing; Parametric Calibration; Dimensional Quality; PLA.

1. Introduction

Fused Deposition Modelling (FDM) can be highlighted as one of the most popular

Additive Manufacturing (AM) technologies, mostly because of its liability, low initial cost

and low-cost materials used (CARNEIRO et al., 2015; WU et al., 2016). In general, FDM is

classified as an extrusion-based AM process (GIBSON et al., 2015). FDM equipments usually

work with termoplastic materials such as Polylactic Acid (PLA) and Acrylonitrile Butadiene

Styrene (ABS). PLA has better thermo-mechanical properties and low thermal expansion

coefficient when compared to ABS. These features make PLA better for printing, once they

reduce material warpage during printing (CASAVOLA et al., 2016).

After the expiration of the patents for the FDM process, owned by the Stratasys®

company (USA), there has been a major development of open source communities such as

RepRap and Fab@Home, which have democratized the technology, making it accessible to a

wider public, and enabling the emergence of both commercial and do-it-yourself 3D printers.

As a result of this movement, the price of the technology has been halved in a few years

(SANTOSO et al., 2013, CHAN et al., 2016). In other words, the combined effect of process

simplicity and lower cost equipments and materials has made FDM accessible to to individual

needs — ideal for hobbyists — and on a small scale, to both the domestic and the commercial

environments (XINHUA et al., 2015, BIKAS et al., 2016).

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Despite its benefits, the FDM process has as one of its main limitations the low

dimensional and geometric precision (BOSCHETTO and BOTTINI, 2014; SONG et al.,

2014). The dimensions and resolution of the parts produced by FDM are dependent on factors

as process parameters, product design, and properties of the building material. Basically, the

process resolution is a function of factors such as the precision of the motors that control the

extruder head movement, the quality of the control algorithm, and the nozzle diameter

(TURNER and GOLD, 2015).

In extrusion-based AM, the process parameters related to the construction trajectories

are very influential in the dimensional accuracy, especially in the layer thickness (SOOD et

al., 2009; TURNER and GOLD, 2015). According to Nancharaiah et al. (2010) the lower the

layer thickness, the better the dimensional accuracy. The authors point out that the use of

moderate sweep widths increases dimensional quality, while negative air gaps can negatively

affect tolerance. In addition to the parameters mentioned above, others are investigated in

technical literature as factors of influence on dimensional stability: infill, number of contours,

printing speed, extrusion speed, building orientation, raster angle, flow rate, extrusion

temperature, bed temperature, among others (GÓSRSI et al., 2013, SAHU et al., 2013,

SANCHEZ et al., 2014 LANZOTTI et al., 2015, PANDA et al., 2016, and RAHMAN et al.,

2016).

Galantucci et al. (2015) also warn that dimensional accuracy in FDM-built parts can

be affected by factors such as: variations in the geometry of deposited filaments in relation to

theoretical cylindrical shape; high values of layer thickness, which influence the integrity of

the extruded material — this characteristic is probably related to the increase in the volume of

deposited material —; shrinkage of the material; and adhesion problems with the first layer

extruded. It is also worth mentioning that the effects described by the authors acted together

with the process parameters: nozzle type, layer thickness and width, and printing speed, thus

agreeing with the studies previously described.

Studies conducted by authors such as Alcock et al. (2016) verified the interaction

between users of 3D printers and factors such as the configuration of process variables. From

conversations with the users, the authors identified that after obtaining 3D models from online

platforms (Thingiverse) one of the main difficulties was the understanding of how to build the

pieces. The main questions revolved around: the presence of support structures, infill

configuration (the percentage of the model to be filled with material), thicknesses, scales,

cooling system speeds, among others.

Considering the above mentioned easy access to the technology, the difficulty of the

users to setup building parameters, and the strong influence of these variables on the

dimensional quality of the parts, the relevance of developing a calibration study of "input

parameters" for 3D printing is clear. These parameters — choice of slicing software, amount

of material needed, speed patterns, layer thickness, etc. — must be configured by users of any

level, whether common or technical. Therefore, the objective of this article was to develop a

database that provides a starting point to help users adjust input parameters, and understand

the commitment relationships between building variables and the dimensional compliance of

the parts manufactured with the original design.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

2. Materials and Methods

The study was divided into two stages. First, the dimensional quality of the printed

parts was evaluated according to the variation of six factors, characterized by manufacturing

process planning tools (CAM software) and building parameters: slicing software (Ss), layer

thickness (Lt), infill density (Id), first layer speed (Sfl), infill speed (Si) and perimeters speed

(Sp) — Figure 1 (a). For each factor, two levels were assigned (Table 1) and the conditions of

analysis, that is, the combinations between the factors and their respective values, were

determined from the application of a Taguchi L8 orthogonal array (Table 2). In each

condition, three replications were performed, generating a total of 24 samples.

Stage Factors Name in Slic3r Name in Cura Levels

1 2 3

1

Lt (mm) Layer height, first layer

height

Layer height, initial

layer thickness

0.25 0.30 -

Id (%) Fill density Fill density 20 100 -

Sfl (mm/s) First layer speed Bottom layer speed 15 30 -

Si (mm/s) Infill, solid infill, Top solid

infill

Infill speed,

Top/bottom speed

40 60 -

Sp (mm/s)

Perimeters, small

perimeters, external

perimeters

Outer shell speed,

Inner shell speed

20 60 -

Ss Slic3r (1.2.9) Cura (15.04.6) Slic3r Cura -

2 Et (ºC) Temperature (ºC) - Extuder * 190 210 220

Em Extrusion multiplier * 0.5 0.9 1

Fixed parameters Name in Slic3r Name in Cura Value

Pn Perimeters Shell thickness 3

Ran (º) Fill angle Not adjustable 45/-45

Et (ºC) (stage 1 only) Temperature (ºC) - Extuder Printing

temperature

210

Bt (ºC) Temperature (ºC) – Bed Bed temperature 70

Fc (stage 1 only) Extrusion multiplier Flow 1 (100%)

Cooling Enable auto cooling Enable cooling fan Yes

Nd (mm) 0.4 Notes:

Pn (number of perimeters), Ran (raster angle), Bt (bed temperature), Fc (flow control), Nd (nozzle diameter);

In the Cura version used, there is not a setting box for the raster angle, and the software uses 45º/-45º as

default;

Cooling: the controlled use of the cooling fan after the first layers;

In Cura there is not a setting box for the number of perimeters, but by configuring Shell thickness it was

possible to insert 3 perimeters when multiplying the nozzle diameter (0.4 mm) by 3 (1.2 mm). The result of

the multiplication is the configuration of the number of perimeters.

Table 1 – The factors and the levels for the experimental stages 1 and 2.

Conditions Taguchi L8

Conditions Factorial (2

3)

Lt Id Sfl Si Sp Ss Et Em

1 0.25 20 15 40 20 Slic3r 1 190 0.5

2 0.25 20 15 60 60 Cura 2 190 0.9

3 0.25 100 30 40 20 Cura 3 220 0.5

4 0.25 100 30 60 60 Slic3r 4 220 1

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

5 0.30 20 30 40 60 Slic3r 5 210 0.9

6 0.30 20 30 60 20 Cura 6 210 0.5

7 0.30 100 15 40 60 Cura 7 190 1

8 0.30 100 15 60 20 Slic3r 8 210 1

9 220 0.9

Table 2 – The Taguchi L8 orthogonal array and the full factorial design experiment.

Based on the information obtained in the first stage of the work, the goal of the second

stage of the analysis was to improve the critical situations observed, in this case the samples

constructed with 100% infill density. For this purpose, parameters related to material

processing — extrusion temperature (Et) —, and to material flow — extrusion multiplier (Em)

— were adjusted. It is important to note that the other parameters evaluated in the first stage

were kept fixed at the levels that generated the best results.

Due to the lower number of parameters analysed, a full factorial design of experiments

(Table 2) was used for two factors, three levels (Table 1), and three repetitions for each

condition. A total of 27 samples was manufactured. For both Stages 1 and 2, each sample of

each experimental condition was individually built in order to ensure the same heating and

leveling conditions of the building platform.

A cubic geometry of (15x15x15) mm, Figure 1 (b), was adopted as the standard for

dimensional analysis in both stages. The pieces were built with a blue PLA filament, 1.75 mm

in diameter, from 3D INK® company, on a Prusa I3 Hephestos 3D printer. In each of the

main directions of the samples, X, Y and Z, three measurements were performed, as shown in

Figure 1 (c), with the aid of 0.0025 mm resolution micrometer (diâmetro da face de

medição=6.50 mm). A 3D Atos Triple Scan (Gom) scanner (16-megapixel camera resolution)

was used to survey shapes in parts with greater and lesser dispersion around projected

dimensional values. In addition to the above-mentioned analysis, the mass of all samples was

measured (weighing scale, d = 0.005g).

Figure 1 – The process parameters (a), the cubic sample (b), and the measuring points for the three axis (c)1.

1 The regions in red correspond to the intersection between the measuring points on the cube faces (X, Y and Z)

as a function of the diameter of the measuring face of the micrometer (6.50 mm).

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

3. Results and discussion

3.1 Stage 1

Table 3 presents the results of the dimensional analysis for each direction (X, Y and Z)

and for each parametric combination (Table 2).

Conditions Dimension for X (mm) Dimension for Y (mm) Dimension for Z (mm) Average Ead Average Ead Average Ead

1 15.01 ± 0.03 0.01 15.05 ± 0.03 0.05 14.98 ± 0.03 -0.02

2 15.29 ± 0.04 0.29 15.30 ± 0.02 0.30 15.04 ± 0.06 0.04

3 15.48 ± 0.05 0.48 15.57 ± 0.05 0.57 15.59 ± 0.05 0.59

4 15.39 ± 0.02 0.39 15.54 ± 0.03 0.54 15.85 ± 0.03 0.85

5 15.09 ± 0.01 0.09 15.13 ± 0.02 0.13 15.09 ± 0.01 0.09

6 15.17 ± 0.02 0.17 15.25 ± 0.02 0.25 15.03 ± 0.01 0.03

7 15.45 ± 0.03 0.45 15.56 ± 0.05 0.56 15.50 ± 0.04 0.50

8 15.34 ± 0.03 0.34 15.44 ± 0.01 0.44 15.72 ± 0.02 0.72

Note: average dimensional error (Ead) corresponds to the difference between the "average of the means"

of the dimensions for each direction of the three samples manufactured and the true value (CAD, 15

mm)

Table 3 – The results of the dimensional analysis by experimental conditions (Taguchi L8).

In order to identify the effects of the evaluated factors on the dimensional variation

Table 3, we used the concepts of analysis of variance (ANOVA) (Table 4). It allowed the

verification of what factors of infill density (Id), infill and perimeters speed (Si and Sp), and

slicing software (Ss) were significant for the variations in the dimensions of the X axis of the

parts. In the cases of Y and Z directions, the parameters previously mentioned for X also

influenced the responses statistically, however, unlike the previous situation, the first layer

speed (Sfl) also had a contribution to the observed effects. Furthermore, on the results of the

analysis of variance, the percentage of filling was the factor with the largest contribution to

the dimensional variation when compared to the other variables investigated, being 71.61%

for the X direction, 81.56% for Y, and 90.44% for Z, as shown in Table 4.

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT

Factors “X direction” ANOVA “Y direction” ANOVA

Df SS V F P P% Df SS V F P P%

Lt (1) (0.006) Pooled (1) (0.002) Pooled

Id 1 0.463 0.463 260.71 0.0001 71.61 1 0.710 0.710 677.35 0.0001 81.56

Sfl (1) (0.001) Pooled 1 0.007 0.007 6.66 0.0188 0.68

Si 1 0.011 0.011 6.33 0.0210 1.47 1 0.016 0.016 15.21 0.0010 1.71

Sp 1 0.017 0.017 9.45 0.0062 2.33 1 0.018 0.018 16.95 0.0006 1.92

Ss 1 0.119 0.119 67.19 0.0001 18.25 1 0.100 0.100 95.10 0.0001 11.35

Error 18 0.034 0.002 1.00 6.34 18 0.019 0.001 1.00 2.77

Total 23 100.00 23 100.00

Factors “Z direction” ANOVA Correlation analysis for dimensions

Df SS V F P P% Factors

X direction Y direction Z direction

Lt (1) (0.005) Pooled Cc P Cc P Cc P

Id 1 2.371 2.371 1559.48 0.0001 90.44 Lt -0.09 0.67 -0.05 0.80 -0.04 0.84

Sfl 1 0.041 0.041 26.85 0.0001 1.50 Pp 0.85 0.00 0.90 0.00 0.95 0.00

Si 1 0.086 0.086 56.55 0.0001 3.22 Sfl 0.04 0.86 0.09 0.68 0.13 0.56

Sp 1 0.009 0.009 6.04 0.0244 0.29 Si 0.13 0.54 0.14 0.53 0.18 0.40

Ss 1 0.086 0.086 56.29 0.0001 3.21 Sp 0.16 0.45 0.14 0.51 0.06 0.78

Error 18 0.027 0.002 1.00 1.33

Total 23 100.00

Notes:

(1) All statistical analysis used a reliability level (α) of 95%

(2) Correlation coefficient (Cc)

Table 4 – Statistical analysis for the results of the dimensional analysis of the first experiment (Taguchi L8).

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

About the variation of the dimensions in the three directions of the pieces, it was

verified the tendency — more pronounced in some cases — that the dispersions in the

dimensions around the target value increased from the smaller to the higher configured level,

which resulted in larger-than-projected parts. On the slicing software, the best results, that is,

those closest to the real value, were the ones with the Slic3r software for the X and Y

directions, and with the Cura software for Z. The behaviors described — i.e. the average of

the dimensions as a function of significant parameters and levels — are presented in the

graphs of Figure 2, for the X, Y, and Z directions.

Figure 2 – Dimensional variation depending on the significant factors and levels for the X, Y and Z directions

Figure 2 shows there are higher values than the projected for the dimensions as we use

higher printing speeds. This effect is related to the increase of the volume of deposited

material by building toolpath (mm3/s), which is proportional to the speed variation, associated

to the accommodation time and space — that reduces or increases from 100% to 20% Id —

between two or more neighboring filaments (Figure 3). Such accommodation should be seen

as the capacity of the deposited filament to smoothly stabilize its main dimensions and its

interaction with neighboring elements intra and inter layers. Instabilities in this process alter

the printing resolution of parts.

Figure 3 – Accommodation of the deposited filaments, intra and inter layers.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

The speed values we found confirm the expected behavior of greater influence of the

perimeters speed on the X and Y axis — that is, in the sense of lateral building — and of infill

on Z — the bottom-top direction. The first and the last layers, even for the samples with 20%

infill, were built with 100% Id. Therefore, higher infill speeds reduced the already mentioned

accommodating capacity of the deposited filaments, which increased the Z dimensions. There

were larger dimensional variations in Z for Slic3r than for Cura, which resulted mainly from

the fact that the most critical combinations between infill speed (60 mm/s) and infill density

(100%), conditions 4 and 8 (Table 3), were associated to Slic3r, thus contributing to the

growth of the average value associated with the Slic3r level.

Cura generates building codes with printing speeds equal or very close to the values

set by the user in the configuration interface while Slic3r has automatic settings that in most

cases reduce speeds when compared to the original numbers set (Figure 4). One of the factors

related to that is the use of the cooling system after the first layers. Since Cura maintains the

original values set, the greater dimensional dispersions may be related to this feature because

the greater volumetric flows associated to the highest speeds are actually being executed. This

effect is more evident when the highest speeds are combined with 100% Id. The presented

concepts explain the dimensional differences between conditions 3 and 4 (Table 3). The

authors understand that the differences between the set speeds and the speeds Slic3r

automatically adjusted in most cases favored the statistical results applied to Slic3r when

compared to Cura.

Figure 4 – Speeds behavior on infill (a) and perimeters (b)2.

One of the causes identified for the greater dimensional deviations observed for parts

with 100% Id (Figure 2) is the excess of deposited material, due to issues such as instability in

the extrusion flow and the calculation of the amount of filament required for the construction

of the samples with such configurations by the slicing software. To confirm the above

statement, we performed a thorough analysis of the building code (Gcode), taking into

account the filament length to be fed to each layer of the model. We calculated the the total

volume of material to be extruded from the sum of the layer to layer length and the filament

diameter (1.75 mm). With the known volume and density of the PLA, we estimated the mass

each sample should have. Finally, we compared the estimated value to the measured average

mass of the samples, as shown in Figure 5 (a).

2 Speeds corresponding to layers between the first three and the last three layers.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Figure 5 – The mass value calculated from the Gcode vs. the average measured mass (a); mass-volume ratio (b).

The graph of Figure 5 (a) highlights that the average mass measured under all

conditions is very close to the mass calculated from the information obtained from the Gcode.

These results are positive because they show the equipment's ability to reproduce components

as directed; on the other hand, they confirm that the slicing software calculates an amount of

material to be fed higher than the material needed to occupy a theoretical volume based on the

nominal dimensions (CAD design) of the samples. The effect is most pronounced for 100%

infill cases.

The variation in the amount of material used to build the parts caused dimensional

distortions which led to bigger parts than designed — again more pronounced to 100% Id

cases. Thus, the volume of the parts rose and their mass was in some cases larger than the

theoretical value (4.22 g) for a 15x15x15 mm PLA cube. In conditions 3 and 4 for instance,

the mass of the samples (considering natural voids) exceeded the theoretical value (4.22 g) in

+4.7% (4.42 g for condition 3) and +5.5% (4.45 g for condition 4) (Table 5). The graphs in

Figure 5 (b) relate the average volumes in ascending order to their corresponding average

masses, for parts with 100% infill density only. Table 5 shows the other volume values for

each condition.

Conditions Mass (g) Volume (mm

3)

Average Df Average Eav

1 2.49 ± 0.01 -1.73 3383.5 ± 14.32 8.52

2 2.03 ± 0.00 -2.19 3517.2 ± 24.07 142.16

3 4.42 ± 0.00 0.20 3758.4 ± 33.55 383.43

4 4.45 ± 0.01 0.23 3790.4 ± 14.31 415.44

5 2.39 ± 0.01 -1.83 3444.9 ± 7.26 69.95

6 2.05 ± 0.02 -2.17 3477.7 ± 4.19 102.67

7 4.39 ± 0.02 0.17 3725.3 ± 29.39 350.26

8 4.33 ± 0.02 0.11 3722.8 ± 12.52 347.83

Note:

(1) The average volumetric error (Eav) is the difference between the

calculated volume (3375 mm3) and the average volume of the parts

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

per condition;

(2) Difference (Df) is the subtraction between the average mass of parts

per condition and a calculated theoretical value (4.22 g)3.

Table 5 – Measurement of the masses and the real volume of the parts (Taguchi L8).

In order to illustrate the effects of the excess of material in the parts configured with

100% Id, one sample of condition 3 was selected to be analyzed through 3D scanning4.

Figures 6 and 7 (a) and (b) present the results for the shape analysis performed in Top and

Base references. In both figures (a) represents the comparisons between the digitized model

and the CAD model through a point measurement of the relative dimensional deviations,

represented in a chromatic scale and (b) shows an overlap between CAD (dark gray) and

digitized (light gray) models. This allows comparisons between the regions in which the

manufactured part exceeded the geometric limits of the original design.

Figure 6 – The dimensional deviations (a) and the overlap of the digitized and CAD models (b) for the 100%

infill density sample; "Top" reference (Condition 3).

Figure 7 – The dimensional deviations (a) and the overlap of digitized and CAD models (b) for the 100% infill

density sample; "Base" reference (Condition 3).

3 Determined by the relation between the theoretical volume of the parts, and the density of the PLA —

~1.25 g/cm3 (HENTON et al., 2005; SANTANA et al., 2016).

4 Condition 3 was selected for the 3D scanning analysis since it was more critical in two directions, X and Y,

when compared to condition 4 (condition with the second largest deviations).

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

As shown in the images above, the manufactured part practically encompassed the

CAD model in the overlap analysis, Figures 6 e 7 (b). There is a concordance between the 3D

graphical models obtained by the 3D scanning and the results of the dimensional analysis of

Table 35. Such correlation can be observed, since the critical directions Y and Z found in the

first stage of the study — with average dimensional errors of approximately +0.57 mm and

+0.59 mm, respectively — are the faces with greater predominance of gray areas in the

juxtaposition of the 3D models, with emphasis on the representation of Figure 7 (b).

In the above-mentioned case ("Base" reference), we notice in Figure 7 (a) dimensional

deviations between (+0.26 to +0.31) mm in Y and of (+0.23 to +0.27) mm in Z. In the "Top"

reference — Figure 8 (a) —, the deviations in Y and Z were respectively in intervals of

(+0.04 to +0.29) mm and (-0.04 to +0.26) mm.

The same type of analysis of the digitized model of the part fabricated with 100%

infill density was adopted for samples with 20%. In this case, a sample from condition 1 was

selected. When comparing the two images in Figure 8 (a) e (b), it was possible to identify

some geometric and dimensional proximity between the two CAD models and the digitized

one. The results further confirm the responses presented in Table 3 for condition 1, the one in

which the part had the largest increase in its dimension for the Y direction in relation to the

actual value. These values are represented in Figure 8 (a) by the positive dimensional

deviations, and by the larger extent of the gray area — Figure 8 (b) — when compared with

the X and Z axes.

The dimensional distortions observed in Figure 8 (a) e (b) might be caused by effects

such as polymer swelling, polymer fluidity, or instabilities in the feeding control of the

filament during the building process, since the 20% Id parts were not influenced by the

material excess caused by the slicing software planning. The digitized model overcomes the

limits of the CAD model in the perimeters and in the infill areas of the Z axis — Figure 8 (b).

In these interface areas occurs the transition between two infill toolpaths, and Slic3r applies

volumetric flows6 from 2.722 mm

3/s to 2.727 mm

3/s (values really close to those applied in

longer infill toolpaths) — Figure 8 (c). In summary, a short transition toolpath with a great

material volume does not allow the proper accommodation of the extruded material. It is

important to remember that the top layers of the model are 100% filled.

5 It should be noted, however, that considerations must be made to link both analysis. The contact measurement

(micrometer) has a larger area of coverage and, therefore, greater approximation of the measurement results. It

should also be noted that the results of Tables 3 and 7 correspond to the "average of the means", that is, the

average value of the dimensions in each condition integrates the average of the three measurements per direction

in each of the three manufactured parts. The measurement with the digitized model is more judicious, since it

can observe punctual deviations. 6 Values calculated from the Gcode.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Figure 8 – The dimensional deviations (a), the overlap of the digitized and CAD models (b) for the sample with

20% infill density (Condition 1), and the (c) toolpath transition.

To end the discussions about infill density, it is crucial to highlight the strong positive

statistical correlation in X, Y and Z (Table 4) confirming that as the infill density increases —

from 20% to 100% in this study —, the dimensions of the part tend to grow in all directions.

In the scatter plot of Figure 9 this behavior is represented for the Z direction, in which this

characteristic was more relevant.

Figure 9 – Linear regression analysis: scatter plot.

The last step in Stage 1 was to analyze the effects of the factors and levels on the mass

and volume variations of the samples. The mass variations (Table 5) were mainly influenced

by the infill density — which is evident by the values of 20% and 100% included in this work

—, but also by the slicing software, and these effects complete each other due to the

calculation of the amount of material to be fed, as previously discussed. Other parameters that

influenced the mass of the parts were layer thickness and printing speed, both responsible for

controlling the material flow rate, as described by Agarwala et al. (1996) (Table 6). The

average volume of the parts (Table 5) was influenced by both the significant parameters for

the dimensional analysis and the average mass (Table 6). The average behavior of the mass

and of the volume, in relation to the significant parameters and their levels are presented in

Figure 10 (a) e (b).

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Figure 10 – The mass and the average volume related to the significant parameters and levels.

“Mass” ANOVA

Factors Df SS V F P P%

Lt 1 0.018 0.018 15.9 0.0008 0.06

Id 1 27.875 27.875 24125.2 0.0001 98.16

Sfl (1) (0.002) Pooled

Si 1 0.249 0.249 215.6 0.0001 0.87

Sp (1) (0.000) Pooled

Ss 1 0.231 0.231 200.0 0.0001 0.81

Error 19 0.022 0.0012 1.0 0.09

Total 23 100

“Volume” ANOVA

Factors Df SS V F P P%

Lt 1 2331.04 2331.04 4.91 0.0408 0.33

Id 1 516548.87 516548.87 1088.16 0.0001 92.01

Sfl 1 5647.04 5647.04 11.90 0.0031 0.92

Si 1 14398.13 14398.13 30.33 0.0001 2.48

Sp 1 6870.28 6870.28 14.47 0.0014 1.14

Ss 1 7016.74 7016.74 14.78 0.0013 1.17

Error 17 8069.92 8069.92 1.00 1.95

Total 23 100

Table 6 – Analysis of variance ( =95%) for mass (g) and volume (mm3).

3.2 Stage 2

The responses presented in Table 7 correspond to the measurements of the main

directions of the samples built in Stage 2 of this study. As in Stage 1, the data was evaluated

by the method of analysis of variance (Table 8), which demonstrated that the extrusion

multiplier was the only significant parameter for the dimensional variations in X, Y and Z.

Conditions Dimensions for X (mm) Dimensions for Y (mm) Dimensions for Z (mm) Average Ead Average Ead Average Ead

1 14.51 ± 0.04 -0.49 14.55 ± 0.06 -0.45 15.05 ± 0.03 0.05

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

2 15.01 ± 0.03 0.01 15.08 ± 0.03 0.08 15.33 ± 0.09 0.33

3 14.49 ± 0.01 -0.51 14.57 ± 0.04 -0.43 15.10 ± 0.07 0.10

4 15.53 ± 0.05 0.53 15.60 ± 0.03 0.60 16.13 ± 0.07 1.13

5 15.02 ± 0.02 0.02 15.10 ± 0.01 0.10 15.33 ± 0.20 0.33

6 14.51 ± 0.08 -0.49 14.64 ± 0.03 -0.36 15.06 ± 0.06 0.06

7 15.46 ± 0.16 0.46 15.58 ± 0.17 0.58 15.91 ± 0.11 0.91

8 15.50 ± 0.08 0.50 15.59 ± 0.06 0.59 16.10 ± 0.11 1.10

9 15.03 ± 0.07 0.03 15.10 ± 0.05 0.10 15.43 ± 0.03 0.43

Table 7 – The results of the dimensional analysis of the samples produced in the second experimental stage.

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT

Factors “X direction” ANOVA “Y direction” ANOVA

Df SS V F P P% Df SS V F P P%

Em 2 4.41 2.205 497.66 0.0001 97.45 2 4.52 2.259 509.86 0.0001 97.58

Et (2) (0.003) Pooled (2) (0.007) Pooled

Et*Em (4) (0.006) Pooled (4) (0.007) Pooled

Error 24 0.11 2.55 24 0.10 0.004 0.97 2.42

Total 26 100 26 100

Factors “Z direction” ANOVA “Volume” ANOVA

Df SS V F P P% Df SS V F P P%

Em 2 4.53 2.266 233.84 0.0001 93.69 2 2145829 1072914.3 456.28 0.0001 97.22

Et 2 0.067 0.033 3.44 0.054 0.98 (2) (7862) Pooled

Et*Em 4 0.043 0.011 1.11 0.382 0.09 (4) (4555) Pooled

Error 18 0.17 0.010 1.00 5.23 24 56435 2351.445 1.00 2.78

Total 26 100 26 100

Factors “Mass” ANOVA

Factors

Correlation analysis for dimensions Df SS V F P P% X direction Y direction Z direction

Em 2 23.88 11.942 11913.23 0.0001 99.84 Cc P Cc P Cc P

Et 2 0.013 0.007 6.54 0.006 0.05 Me 0.940 0.000 0.936 0.000 0.821 0.000

Et*Em (4) (0.005) Pooled Te 0.027 0.893 0.028 0.889 0.117 0.561

Error 22 0.001 1.00 0.11

Total 26 100

Table 8 – The statistical analysis of the responses scored in the second experimental stage of the study.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

In summary, the X, Y and Z dimensions of the cubes presented a tendency to grow in

relation to the projected value of 15 mm between the configurations from the lowest (0.5) to

the highest (1) extrusion multiplier levels evaluated. The described behavior can be observed

in the chart that shows the averages for the three directions — Figure 11 (a) — and in the

results of Table 7. The directly proportional relationship between the extrusion multiplier and

the dimensions of the samples is also confirmed by the strong positive correlation analysis in

X, Y and Z (Table 8), and it is illustrated in the scatter plot (for the X direction) in Figure 11

(b).

Figure 11 – The dimensional variation for each direction as a function of Em and levels (a) and (b): scatter plot

for X direction.

Based on the data presented in Figure 11 (a), it was found that the adjustment of the

extrusion multiplier at 0.9 generated the best results, ie, closer to the value of 15 mm for the X

and Y directions. Experimental condition 2 (Table 7) is the one that best exemplifies this

behavior, since it recorded the lowest absolute difference between all conditions, and

especially among those that shared the same value of Em. The mean errors in X and Y for this

condition were of approximately +0.01 mm (+ 0.04%) and +0.08 mm (+ 0.51%).

Also in Figure 11 (a), the values for the Z direction that showed to better adequate to

the projected ones were obtained with the Em adjustment equal to 0.5. As in the previous case

(for Em = 0.9), it was possible to identify among the conditions with a 0.5 multiplier the one

that had the best use for the absolute difference for Z: condition 1 (Table 7), with a + 0.05 mm

error (+ 0.36%). However, for the same condition — as for the others with the same level

associated to the parameter — the X and Y directions presented much lower average values

than the CAD model, around -0.49 mm (-3.25%) and -0.45 mm (-2.97%) respectively. To

illustrate this behavior, a sample of condition 1 was selected for the shape survey analysis, as

shown in Figure 12 (a) and (b).

The 3D scanning information emphasizes the reduction of the part dimensions in

relation to the CAD design for X and Y, by the predominance of negative dimensional

deviations — Figure 12 (a). These deviations, considering the faces highlighted in the image,

were respectively in intervals of (-0.46 to -0.19) mm and (-0.25 to -0.12) mm for X and Y.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Figure 12 (b) further complements the analysis so that the CAD model stands out completely

on the faces for X and Y, with only some regions in Z where the manufactured element

exceeds the boundaries of the 3D design.

Figure 12 – The 3D scanning for the sample with Em equal to 0.5 (condition 1): (a) the dimensional deviations

and the (b) overlapping of models.

Another important effect observed by the use of the 0.5 extrusion multiplier was the

weak or absence of adhesion between the deposited filaments, resulting in the detachment of

these structures along the layers (Figure 13). The characteristic was associated with the low

material flow — 50% lower than the extrusion volume in a 100% stable system — which

generated very thin filaments, and with little wetting in the directions between and within the

layers.

Figure 13 – Thin filaments and the absence of adhesion between them – condition 1.

Finally, the critical results, ie with a greater dispersion around the target value of 15 mm

for the cubic geometry, were assigned to the extrusion multiplier set at 1, according to the

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

scenario presented in the first stage of the study for parts with 100% infill density — since Em

equal to 1 corresponds to the default value of the slicing software. As in the previous

discussions, one condition stands out in relation to the others with the same value associated

with the parameter: condition 4. The average dimensions of the parts manufactured in this

condition were approximately 3.5% greater than those of condition 2 in X and Y, and 7.2%

higher than condition 1 in Z.

When comparing a sample made under condition 2 (Em = 0.9) with one built under 4

(Em =1) by the 3D scanning analysis, there was a significant increase in regions with positive

dimensional deviations from the CAD design, from 2 to 4. The characteristic described can be

observed in Figure 14 (a) and (b), respectively conditions 2 and 4.

Figure 14 – The 3D scanning and the dimensional deviations: (a) sample with Em = 0,9 (condition 2) and (b) Em

= 1 (condition 4).

Visually, the surfaces of parts made with 100% infill density and extrusion multiplier

set in 1 clearly depict the instability in the material flow. Basically, the faces of the cubes with

such configuration presented a "wrinkled" appearance, alternating in the same layer regions

with excess material, in the form of drops, and thin filaments, as if the material was stretched.

This effect contributed to the dimensional variations identified in the study. To portray this

scenario, a sample was constructed with the critical parametric settings mentioned above and

with twice the dimension of the cubes evaluated in this article, as shown in Figure 15.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Figure 15 – The aspect of surfaces, (a) the sample on a larger scale and (b) the experimental sample – condition

4.

The analysis of the masses contributed to emphasize the influence of the multiplier on

the final properties of the manufactured parts. As in Stage 1, it was noticeable from the Gcode

review the amount of material needed to build the samples in each experimental condition,

calculated by the slicing software. From this procedure, it was clear that the volume of

material to be fed, and consequently the mass, increased with the augmentation in Em levels.

This caused the largest or the smallest variations in the dimensions of the real parts. Once

again, we identified the proximity between the calculated values for mass from the Gcode

review and the average mass measured from the samples built — Figure 16 (a). An important

parallel can be established between the mass-volume ratio, which highlights the proportional

relation between both answers — Figure 16 (b). (Table 9) shows the values for average

volume of all experimental conditions.

Figure 16 – The mass value calculated from the Gcode vs. the average measured mass (a); the mass-volume ratio

(b) - Stage 2.

Conditions Mass (g) Volume (mm

3)

Average Df Average Eav

1 2.26 ± 0.03 -1.96 3179.5 ± 27.81 -195.47

2 4.02 ± 0.04 -0.20 3467.5 ± 7.59 92.51

3 2.27 ± 0.02 -1.95 3187.8 ± 18.42 -187.21

4 4.45 ± 0.03 0.23 3910.2 ± 35.73 535.20

5 4.06 ± 0.02 -0.16 3478.6 ± 49.33 103.64

6 2.27 ± 0.02 -1.95 3200.4 ± 19.48 -174.62

7 4.37 ± 0.05 0.15 3831.6 ± 109.50 456.60

8 4.44 ± 0.03 0.22 3890.0 ± 59.86 515.04

9 4.08 ± 0.03 -0.14 3501.1 ± 34.28 126.12

Table 9 – The measurement of the mass and the actual volume of samples, second experimental stage.

In other words, when using 1 as Em (largest value), the software calculated an amount

of material to be fed greater than the necessary to built a sample with the designed dimensions

(15x15x15 mm) in PLA, as seen in Stage 1. When “imposing” a greater volume of material to

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

be extruded than the theoretical one there were augmentation in the dimension of the samples

and instability in the material flow, causing an “over-extrusion” effect in the surface of parts

(Figura 15). On the other hand, as we tried to build 100% Id samples with a smaller amount of

material (Em = 0.5), dimensions were reduced to “compress” the filaments, specially in X and

Y. However, this caused a low extrusion flow; the 0.9 value for Em was a point of balance

between the extremes presented.

In the conditions with greater dimensional compliance for the X, Y and Z directions

around the projected value (15 mm), ie for Em equal to 0.9, the volume of the pieces varied in

a smaller scale (Table 9) in relation to the theoretical value (3375 mm3), generating an

average mass of 4.05 g, lower than the theoretical mass, but more compatible with the

characteristics of the manufacturing process. Such behavior, following the assumptions

presented earlier, was attributed to a more stable flow of material during the deposition of the

layers.

Finally, we evaluated the influence of the factors on the variations of mass and volume

of the samples. Despite the significant influence of the extrusion temperature on the mass

variation, according to the analysis of variance — Tabela 8 — the contribution of this factor

(~ 0.05%) is very small when compared to the extrusion multiplier one (~ 99.84 %). Figure 17

(a) shows the variation of the mass in relation to the parameters and levels.

The average value of the mass (considering the extreme values of Em) when compared

to the theoretical mass (4.22 g) ranged from -46.3% (~ 2.27 g) to Em equal to 0.5 and +4.8%

(~ 4.42 g) for Em set in 1. It was possible to relate the mass variation with the amount of

deposited material. There was excessive material deposition when the extrusion multiplier

was adjusted to 1 — since the mass of the samples exceeded the theoretical value, considering

the presence of natural voids in the process — and there was shortage when at an extrusion

multiplier configuration of 0.5. The volume of the samples (Table 8) was influenced

exclusively by the extrusion multiplier, and the average behavior in function of the Em levels

is presented in Figure 17 (b).

Figure 17 – The variation of the responses in relation to the parameters and levels: (a) mass and (b) volume.

4. Final considerations

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

In this last topic we synthesize the content of this work in a methodological procedure

for the parametric calibration process. It is a generalist method and therefore can be used in

any low-cost 3D extrusion printing scenario. It should be noted that the calibration study is a

preliminary study. Its goals are to stabilize a basic configuration of the input parameters, to

help the choice of the slicing software, and to identify the causes of error in the manufacturing

process. It precedes work that seeks parametric optimization, that would focus for example on

the construction of complex geometries or of parts for end use. The authors suggest for this

procedure the use of a simple geometry, such as the cube used, in order to facilitate the

measurement steps and the visualization and interpretation of the effects of the different

configurations tested on the qualitative aspects of the parts. The following steps are described

in Figure 18:

Figure 18 – Methodological procedure for the parametric calibration process.

(1) Define the responses: the user must establish a quality criterion in which the

influence of the factors involved in the calibration process will be evaluated, and

define the goals to be reached with the procedure. In this study, the dimensional

aspect was the quality criterion selected, and the goals were: (i) to define the

causes of dimensional errors, (ii) to find the best slicing software and the

parameter configuration that generated the smallest absolute difference between

the dimensions designed for a part and those obtained on the printed samples;

(2) Select the factors and levels: this step must be based on the factors influencing

the response established in (1) - determined from the literature review -, on the

input parameters of the slicing software used, on the general characteristics of the

printing (e.g. nozzle diameter), and on the characteristics of the construction

material (processing temperature, warping tendency, adhesion to the building

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

platform, among others). Remember: input parameters are those that inevitably

need to be configured;

(3) Select the DOE method: it depends on the number of variables and levels

established in (2). This was the criterion used for the experimental design method

we applied in this paper, and can be used by other users. For a greater number of

factors and levels, the Taguchi method is suggested. For a smaller number of

factors and levels, one can use the full factorial design. These are two examples of

widely used experimental design methods, while others may be found in the

literature. To illustrate the DOE method selection, we selected the first

experimental stage of this paper, which was based on six factors with two levels

each. By the method of Taguchi, eight experimental conditions were determined,

and the authors built three samples for each one, generating a total of 24 pieces.

For this same situation, if the full factorial design was used, 192 samples would

have to be built, implying longer time and higher costs for the study;

(4) Print the samples: first, it is essential to calibrate the machine (leveling the base

and axes) and to check the correct operation of the extrusion system. When

building the samples, it is important that each part is individually constructed, in

order to maintain the same conditions of leveling and heating of the building

platform;

(5) Analyze the samples: the user must select the measurement procedure (according

to the quality criterion to be studied) and maintain a systematic repetition of the

measurements. In this study, for example, we acquired dimensions by contact

measurement (micrometer) and by 3D scanning analysis. Then, the user must

apply statistical methods to analyze the data obtained (dispersion measures,

methods of comparison of the means, correlation analysis, regression analysis,

among others);

(6) Are the responses stabilized? at this moment the user must verify if the goals

have been reached: has a minimum variation of the response been achieved in

relation to the quality criterion established? Was it possible to map the causes of

error? If there are positive answers to the previous questions, the process can be

finalized. Otherwise, information obtained should be used for a new process of

literature review to determine new factors of influence to be investigated.

5. Conclusion

The main contribution of this calibration study was to establish that the dimensional

variations in the parts manufactured by extrusion-based 3D Printing are a function of the

amount of material projected and deposited. Low infill densities (such as the 20% used)

improve dimensional quality by a simple relation between low volume of material to be

extruded and greater freedom for filament deposition and accommodation. The combination

of high infill densities (100%) and high material flow (Em = 1) reduces dimensional

compliance since it causes over-extrusion that leads to bigger geometric and physical values

than the designed when allied to a low level of accommodation of deposited filaments. The

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

accommodation of the filaments within each layer, or between them, interfered in the building

resolution of the parts.

The root causes of the dimensional problems found in this study were the slicing

software and the strategies they use to calculate the amount of material to be fed to build

parts. Such effect is not clearly highlighted in the statistical results because it competes with

the other variables in the design of experiment as a process parameter. The analysis of the

Gcodes made it possible to verify that the infill density (Stage 1) and the extrusion multiplier

(Stage 2) emphasize indirectly the influence of the software in the dimensional variations.

The extrusion multiplier emerged in this study as a tool that acted in the mathematics

envolved in the slicing software, allowing the control of the amount of material to build the

samples and improving the dimensional properties of parts with 100% Id. The increase in the

volume of the extruded material and its influence on the integrity of the deposited filaments,

and consequently on the dimensions of the parts, as evidenced by Galantucci et al. (2015),

was also identified in this study. However, unlike the authors, who related the phenomenon to

the increase of layer thickness, in this study the phenomenom was attributed to the higher

levels of the extrusion multiplier, associated with high infill densities.

In addition to contributing to the studies of the authors mentioned above, the content

developed in this research also collaborates with the research of Lanzotti et al. (2015). The

authors found that by increasing the material flow adjustment by 5%, that is, to 105%, the

dimensional accuracy was improved, as opposed to the scenario observed in this article, in

which the greater conformity of the dimensions with the original design was obtained by

reducing the multiplier by 10% (Em = 0.9). However, it is important to note that the authors

used in their study a PLA filament of 2.85 mm in diameter, unlike the one used in this article,

1.75 mm.

The known proportionality between mass and volume was an important relation for

the analyses carried out, since it allowed to identify the effects of over deposition in the

dimensions of the samples. Thus, this is suggested by the authors to be used as a quality

evaluation tool since mass and volume are properties that are easy to measure. In summary, it

was found that:

a) printing speed is a directional parameter, that is, its best setting should be made

considering the lateral (X and Y) and vertical directions (Z). Furthermore, printing

speed interferes in the volume of the extruded material (mm3/s), filament by filament,

and in the capacity of accommodation of the filaments layer by layer. For these

reasons, the use of lower speeds in all directions of the sample improves its

dimensional quality;

b) the slicing software influences the patterns of speed set by the user;

c) in this study, the layer thickness was not a significant factor for the dimensional

variation, opposing the literature (SOOD et al., 2009; NANCHARAIAH et al., 2010).

However, very close levels, 0.25 mm and 0.30 mm, were used, and the effects of

excess material may have interfered with the performance of the parameter. The Z

axis, for instance, in which layer thickness is commonly a crucial factor that affects

dimensional precision, was highly influenced by the over extruded material and by

parameters as infill speed at its highest levels. Now that we know how the slicing

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

software calculates the amount of material, we suggest new studies focusing on the

role of the layer thickness in the process.

Finally, the authors believe that the main contribution of this work is to alert users,

about the importance of adjusting and checking the building codes and the material flow

before building samples and prototypes. This can be made manually or with the help of tools

such as CAM simulation software or online platforms. To do so, a simple model, such as the

cubic structure used, can be applied as the standard for parametric calibration, and the best

levels associated with the input variables found in the present research can be used as a

starting point for the optimization process.

Acknowledgments

To the Brazilian National Council of Scientific and Technological Research and

Development (CNPq) for financing the doctoral research of which this article is part, the

Faculty of Engineering of the University of Porto, INEGI Institute, and the Federal Institute of

Santa Catarina (IFSC - Florianópolis). We also thank Project NORTE-01-0145-FEDER-

000022 - SciTech-Science and Technology for Competitive and Sustainable Industries,

cofinanced by the Regional Operational Program of the North (NORTE2020), through the

European Regional Development Fund (ERDF).

References

AGARWALA, Mukesh K. et al. Structural quality of parts processed by fused

deposition. Rapid Prototyping Journal, v. 2, n. 4, p. 4-19, 1996.

ALCOCK, Celena; HUDSON, Nathaniel; CHILANA, Parmit K. Barriers to Using,

Customizing, and Printing 3D Designs on Thingiverse. In: Proceedings of the 19th

International Conference on Supporting Group Work. ACM, 2016. p. 195-199.

BIKAS, H.; STAVROPOULOS, P.; CHRYSSOLOURIS, G. Additive manufacturing

methods and modelling approaches: a critical review. The International Journal of

Advanced Manufacturing Technology, v. 83, n. 1-4, p. 389-405, 2016.

BOSCHETTO, Alberto; BOTTINI, L. Accuracy prediction in fused deposition modeling. The

international journal of advanced manufacturing technology, v. 73, n. 5-8, p. 913-928,

2014.

CARNEIRO, O. S.; SILVA, A. F.; GOMES, Rui. Fused deposition modeling with

polypropylene. Materials & Design, v. 83, p. 768-776, 2015.

CASAVOLA, Caterina et al. Orthotropic mechanical properties of fused deposition modelling

parts described by classical laminate theory. Materials & Design, v. 90, p. 453-458, 2016.

CHAN, Kamfai et al. Low-Cost 3D Printers Enable High-Quality and Automated Sample

Preparation and Molecular Detection. PloS one, v. 11, n. 6, p. 1-19, 2016.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

GALANTUCCI, L. M. et al. Analysis of dimensional performance for a 3D open-source

printer based on fused deposition modeling technique. Procedia CIRP, v. 28, p. 82-87, 2015.

GIBSON, Ian; STUCKER, Brent; ROSEN, David. Additive Manufacturing

Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. 2. ed. New

York: Springer, 2015. 510 p

GÓRSKI, Filip; KUCZKO, Wiesław; WICHNIAREK, Radosław. Influence of process

parameters on dimensional accuracy of parts manufactured using Fused Deposition Modelling

technology. Advances in Science and Technology Research Journal, v. 7, n. 19, p. 27--35,

2013.

HENTON, D.E.; GRUBER, P., LUNT, J.; and RANDALL, J., 2005. Polylactic Acid

Technology. In: Mohanty A.K., Misra, M., and Drzal, L.T.. Natural Fibers, Biopolymers, and

Biocomposites

KITSAKIS, K. et al. A Study of the dimensional accuracy obtained by low cost 3D printing

for possible application in medicine. In: IOP Conference Series: Materials Science and

Engineering. IOP Publishing, 2016. p. 012025.

LANZOTTI, Antonio; MARTORELLI, Massimo; STAIANO, Gabriele. Understanding

process parameter effects of reprap open-source three-dimensional printers through a design

of experiments approach. Journal of Manufacturing Science and Engineering, v. 137, n. 1,

p. 011017, 2015.

MOHAMED, Omar Ahmed; MASOOD, Syed Hasan; BHOWMIK, Jahar Lal. Optimization

of fused deposition modeling process parameters for dimensional accuracy using I-optimality

criterion. Measurement, v. 81, p. 174-196, 2016.

NANCHARAIAH, T.; RAJU, D. Ranga; RAJU, V. Ramachandra. An experimental

investigation on surface quality and dimensional accuracy of FDM

components. International Journal on Emerging Technologies, v. 1, n. 2, p. 106-111, 2010.

PANDA, Biranchi N. et al. Performance evaluation of warping characteristic of fused

deposition modelling process. The International Journal of Advanced Manufacturing

Technology, v.88, n.5, p. 1799-1811, 2016.

RAHMAN, H. et al. Investigation on the Scale Factor applicable to ABS based FDM

Additive Manufacturing. In: International Conference on Processing of Materials, Minerals

and Energy, 2016, Ongole, Andhra Pradesh, India.

SAHU, Ranjeet Kumar; MAHAPATRA, S. S.; SOOD, Anoop Kumar. A study on

dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy

logic. Journal for Manufacturing Science & Production, v. 13, n. 3, p. 183-197, 2013.

SANCHEZ, Fabio Alberto Cruz et al. Towards a standard experimental protocol for open

source additive manufacturing: This paper proposes a benchmarking model for evaluating

accuracy performance of 3D printers. Virtual and Physical Prototyping, v. 9, n. 3, p. 151-

167, 2014.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

SANTOSO, Stephanie M.; HORNE, Benjamin D.; WICKER, Stephen B.. Destroying by

Creating: Exploring the Creative Destruction of 3D Printing Through Intellectual

Property. 2013. Disponível em:

<https://www.truststc.org/education/reu/13/Papers/HorneB_Paper.pdf>. Acesso em: 26 mar.

2017

SANTANA, Leonardo et al. Avaliação da Composição Química e das Características

Térmicas de Filamentos de PLA para Impressoras 3d de Código Aberto. In: CONGRESSO

NACIONAL DE ENGENHARIA MECÂNICA (CONEM), 9., 2016, Fortaleza. Anais do

CONEM 2016. Fortaleza, 2016. p. 1 - 10.

SONG, Suoyuan et al. Shape deviation modeling for fused deposition modeling processes.

In: Automation Science and Engineering (CASE), 2014 IEEE International Conference

on. IEEE, 2014. p. 758-763.

SOOD, Anoop Kumar; OHDAR, R. K.; MAHAPATRA, S. S. Improving dimensional

accuracy of fused deposition modelling processed part using grey Taguchi method. Materials

& Design, v. 30, n. 10, p. 4243-4252, 2009.

TURNER, Brian N.; GOLD, Scott A. A review of melt extrusion additive manufacturing

processes: II. Materials, dimensional accuracy, and surface roughness. Rapid Prototyping

Journal, v. 21, n. 3, p. 250-261, 2015.

XINHUA, Liu et al. An investigation on distortion of PLA thin-plate part in the FDM

process. The International Journal of Advanced Manufacturing Technology, v. 79, n. 5-

8, p. 1117-1126, 2015.

WU, Haixi; WANG, Yan; YU, Zhonghua. In situ monitoring of FDM machine condition via

acoustic emission. The International Journal of Advanced Manufacturing Technology, v.

84, n. 5-8, p. 1483-1495, 2016.

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Graphical abstract

ACCEPTED MANUSCRIPT

ACC

EPTE

D M

ANU

SCR

IPT

Highlights

Cubic structures in PLA were built and dimensionally evaluated by contact measurement and

by 3D scanning analysis. The goal was to calibrate the input parameters of the 3D printing

process, and to select the best slicing software option. Two experimental design methods were

used: Taguchi and full factorial method;

It was found that dimensional quality is a function of the amount of deposited material;

The mathematics developed by the slicing software to calculate the amount of material to be

fed is a significant factor for dimensional quality of the parts;

Lower infill density values tend to reduce dimensional errors. However, for parts with 100%

infill density the results can be improved with the adjustment of the extrusion multiplier. Both

parameters indirectly emphasize the influence of the slicing software on dimensional quality;

Printing speed is a directional parameter, which means its setting must be made by axis

(X,Y,Z). It is influenced by the compensation strategies the slicing software makes.

ACCEPTED MANUSCRIPT