Resolution-aware Slicing of CAD Data for 3D

Isidore Onyeako and Won-sook Lee

School of Electrical Engineering and Computer Science, University of Ottawa, Ontario, Canada

Keywords: Additive Manufacturing (AM), Layered Manufacturing (LM), 3D Printing, Computer Aided Design, Fused

Filament Fabrication (FFF), Fused Deposition Modelling (FDM), Electron Beam Freeform Fabrication

(EBFF), Electron Beam Melting (EBM), Magnetic Resonance Imaging (MRI), Computer Tomography

(CT), Rapid Prototyping (RP), Stereolithography (SLA).

Abstract: Low resolution printing results in fused joints when the joint clearance is intended to be very small. Various

3D printers are capable of print resolutions of up to 600dpi (dots per inch) as quoted in their datasheets. It is

imperative to include the ability of a 3D slicing application, to validate 3D models, based on the ability of

the printer to properly produce the features with the smallest detail in a model. A way to perform this

validation would be the physical measurement of printed parts and comparison to expected results. Our

method uses ray casting to detect features in the 3D models whose sizes are below the minimum allowed by

the printer resolution. Our model was tested using few simple and complex 3D models. Areas in the slices

with thickness less than the specified resolution were detected. Our model serves two purposes: (a) to assist

CAD model designers in developing models whose printability is assured- by warning or preventing shape

operations that will lead to regions/features with sizes lower than that of the printer resolution; (b) to

validate slicing outputs to identify regions/features with sizes lower than the printer resolution. This makes

our model very powerful in the quality assurance of 3D printing and a huge cost/time saver when planning

for 3D printing.

1 INTRODUCTION

When 3D printing equipment manufacturers quote

their printer resolutions, this information can serve

as an input into a model to validate that the

applicable printer will be able to produce critical

features in the model of interest. The potential cost

and time savings, gained by ensuring that CAD

designers avoid features smaller than the printer

resolutions, is quite significant.

1.1 Basic 3D Printing Process

3D printing (3DP) or rapid prototyping (RP) is an

additive manufacturing process that involves the

production of physical objects by adding thin

successive layers of materials without using moulds

(Munir, 2013). The models being printed can be

obtained via image acquisition from mobile scanners

(Stamos, I., Allen, P., 2000), Magnetic Resonance

Imaging (MRI), Computed Tomography (CT),

positron emission tomography, direct 3D CAD

models. This enhances the rapid prototyping process

as the technology is capable of producing a near-net-

shaped and multi-coloured part. 3D printing is also

referred to as Layered Manufacturing (LM) (Munir,

2013)

The 3D printing process (Figure 1) starts with a

CAD data model generation. The data is “sliced”

into successive layers. A slice is a collection of

contours to be filled during printing.

Create CAD Slice CAD Print model

Figure 1: 3D Basic Printing Workflow (Topcu, O.,

Tascioglu, Y., Unver, H. O. 2011).

1.2 Slicing and 3D Printing Quality

Assurance

The process planning of additive manufacturing, as

shown in Figure 1, begins with the creation of the

CAD model. This can be done with any of the

popular 3D applications. It can also be obtained

from 3D medical imaging data.

124

Onyeako, I. and Lee, W-s.

Resolution-aware Slicing of CAD Data for 3D.

DOI: 10.5220/0005784701220127

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 1: GRAPP, pages 124-129

ISBN: 978-989-758-175-5

Figure 2: Standard Tessellation Language (STL) File and

CAD model.

The next step in the 3D printing workflow is the

creation of the slicing output. O. Topcu, Y.

Tascioglu and H.O Unver (Topcu, O., Tascioglu, Y.,

Unver, H. O. 2011) presented a method for slicing

CAD Models for the purpose of developing G-

Codes. This involves the cutting of the triangulation

surfaces (facets) into shapes with heights equal to

that of the slice thickness. These lines are then

joined to form contours which are used for tool path

data generation (G-Codes). There is presently no

industrial or formal specification for slice data.

Figure 3: (a) Tessellated cube with 12 facets (b) a sliced

cube (Topcu, O., Tascioglu, Y., Unver, H. O. 2011).

An algorithm for slicing 3D models, developed

by Topcu et al, uses STL file as input. In Figure

3(a), the cube is tessellated into 12 triangles in the

STL. The cube is cut into slices in Figure 3(b). The

goal of the slicing algorithm is to produce contours

for generation of G-Code. If the slicing thickness (t)

is larger, the likelihood of facets falling in between

the slices increases. This will make those facets not

to be sliced. To avoid this, the thickness of the slices

is reduced. This gap between adjacent slices defines

the layer thickness. The layer thickness is a variable

in the slicing algorithm.

(Baumann F. Et al, 2015) proposed a framework

for achieving a comparable quality assessment of

both slicing tools for FDM printers and FDM

printers themselves. The framework chose few

popular slicing tools based on their reliability

(ability to handle all test models), G-Code

compatibility and application configurability. The

properties being configured included: print

temperature, print bed temperature, layer thickness,

fill density, print speed and minimum layer print

time.

Baumann’s (Baumann F. Et al, 2015) work

focused on evaluation of 3D slicing applications.

This work did not consider the effects of printer

resolution. Practically all 3D printers, just like

cameras, quote resolutions but in 3D space (x-y-z).

A consideration of this resolution in determining the

ability of a printer to accurately print a model should

be an integral component of a slicing application.

When vendors quote printer resolutions, evaluation

of printers for their suitability for a given model can

make use of a resolution aware slicing process to

select the best printer.

2 RESOLUTION-AWARE

SLICING

In 3D printing terms, resolution is the number of

individual voxels that can be deposited in a given

unit volume. It is usually expressed as layer

thickness (z) and x-y resolution in dots per inch (dpi)

or micrometers (µm). The layer thickness varies by

printer and/or printing technology.

Figure 4: Comparison of Minimum Feature (Baumann F.

Et al, 2015).

Figure 5: Comparison of Minimum Layer Thickness

(Baumann F. Et al, 2015).

Data for five 3D printing technologies were

compared based on their capabilities as it relates to

factors that affect print resolution. The factors

include minimum feature size), minimum layer

thickness and tolerance.

0,002

0,004

0,006

DMLS EBM SLS FDM SLA

Minimum feature size (in)

0,002

0,004

0,006

DMLS EBM SLS FDM SLA

Minimum Layer Thickness (in)

Resolution-aware Slicing of CAD Data for 3D

125

In Figure 4, direct metal laser sintering (DMLS),

electron beam melting (EBM), selective laser

sintering (SLS) and Fused deposition model all

averaged a minimum feature size of approximately

0.005in. If the minimum feature size is less than the

print resolution, it will not be possible to print the

part. While this is not too common with the current

application of 3D printing, it is imperative to take

into consideration the fact that 3D printing is

growing in application as newer uses of 3D

technology is being investigated. Stereolithography

(SLA) offered the best opportunity in terms of

minimum feature size as it is capable of 0.004in

feature size.

The layer thickness has negative effect on the

quality of the print work. As the layer gets thicker it

becomes increasingly difficult for the printer to

accurately target and print intricate shapes. In Figure

5 DMLS and SLA offered the best opportunities as

they are capable of layer thickness of 0.001in. SLS

is next with a minimum layer thickness of 0.004in

while EBM and FDM offered the least performance

with both capable of minimum layer thickness of

0.005in.

The factors that influence printing resolution

include:

• Print accuracy along the x, y and z axes. The

precision of the print head is determines the x

and y axis resolution. The z axis, where the

layers are applied, determines how fine they

will be.

• The viscosity of the binding agent

• The accuracy of color application

• Treatment of the 3D printed object after it

comes out of the machine.

Figure 6: Effect of resolution on printability of features.

In Figure 6, it can be shown that as

feature/region sizes get smaller than the resolution of

the printer the feature/region becomes unprintable

by that printer. Also, the staircase effect common

with 3D printing is also shown. A review of methods

for slicing 3D data to handle staircase effect

(Baumann F. Et al, 2015) identified a few methods

including:

• Cusp height concept (Dolenc, A. and Makela,

I. 1994)

• Stepwise uniform refinement (Sabourin, E.,

Houser, S.A. and Bohn, J.H., 1996)

• Local adaptive slicing (Tyberg, J. and Bohn,

J.H. 1998)

• Accurate exterior and fast interior (Sabourin,

E., Houser, S.A. and Bohn, J.H. 1997)

• Efficient slicing method (Tata, K., Fadel, G.,

Bagchi, A. and Aziz, N. 1998)

• Non Uniform cusp heights (Cormier, D.,

Unnanon, K. and Sanni, E. 2000)

• Consideration of parabolic build (Pandey,

P.M., Reddy, N.V. and Dhande, S.G. 2003)

3 OUR METHODOLOGY

3.1 Objectives

The objective of our research is to improve the

quality of 3D printed objects, by detecting upfront,

potential defects as regions or features that will be

unprintable due to printer resolution. Ray casting

algorithm mentioned in section 3.2 will be applied to

slicing output for the applicable printer. Each slice

will contain a set of contours. Ray casting will be

used to determine the regions of the contours that are

actual solids. The length of the line between one

edge of the solid portion of the contour and the other

will be compared with the printer resolution in that

direction. Regions having lines lower than the

printer resolution in a particular direction will be

flagged as having potential defects on printing.

3.2 Ray Casting of 3D Slice Data

Upon parsing of the 3D slice data for the applicable

printer, ray casting is performed on each slice using

the resolution in one of x or y direction as the

frequency. The algorithm accepts the xyz resolutions

quoted by the printer as inputs. The z resolution is

taken as the slicing thickness. Arbitrarily y or x is

chosen as the direction to begin slicing.

For each slice, a line is drawn from one edge of

the slice to the other at the y axis. The pitch of this

line is equal to the y resolution. All the points where

this line intersects with the edge of the contours in

the slide is identified and labelled 0 or 1 in

alternating order. Lines labelled 1 are for solid

regions. Lines labelled 0 are outside the solid

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

126

regions in the slide; hence they will not be used in

further computations.

Our code checks for intersection of two lines. If

an intersection is found, the code also checks if the

point of intersection actually occurs on the

applicable lines. If the point does not occur on both

lines, the algorithm discards the point.

Figure 7: Schematic of Ray casting to detect solid features

for size comparison.

Our application traverses the entire slicing data

set (Figure 7) to identify defective regions. If the

algorithm in SlicingResValidator is applied to

slicing outputs, it will detect defective regions as

those having sizes/length less than the resolution of

the applicable printers. Other uses of the algorithm

in SlicingResValidator will be when the check is

introduced during model design to enforce the

requirement to ensure that features/regions do not

fall below the printer resolution.

4 RESULTS AND DISCUSSION

Our application, SlicingResValidator accepts a CAD

model, its SVG slicing dataset, x resolution and y

resolution as inputs. A few 3D CAD models were

tested and SlicignResValidator was able to detect the

potential defects on the slicing sets. These defects

are presented as points on both sides of the edges

making up the ray solid region of interest. The

defect can also be viewed as a line joining these

points to show their length as evidence that they are

shorter than the printer resolution in that direction.

The following metrics were generated from our

model:

• Slicing Number: This is the index on the slice

in the total SVG slicing dataset.

• Total Number of Contours: This is the total

number of contours found in the selected slice.

• Total number of Slice Regions with Errors:

When defects are detected, algorithm provides a

count of the defects and presents it to the user.

The first model contained a flat 3D model of regular

geometrical shapes in different sizes. The goal was

to evaluate the ability of SlicingResValidator to

detect regions of sizes lower than that of the

applicable printer resolution. In Figure 8(a), a slice

of the 3D model on the left was validated with a

resolution of x=0.1 and y=0.1 in the applicable unit

of measurement and regions. The number of errors

detected in one slice was 70. This means that this

slice contains 70 regions whose sizes/lengths are

smaller than the x resolution of the printer. When the

resolution was set to x=0.5 and y=0.5 (lower

resolution, see Figure 8 (b)) 120 defective regions

were detected. With a resolution of x=1 and y=0.5,

222 defective regions were detected. This shows that

our model is able to detect more defects as the

resolution is reduced. In effect, a lower resolution

means that the printer will deposit more materials at

a single point than with higher resolutions.

Therefore, a larger number of smaller features will

not be printed.

(a)

(b)

Figure 8: Regular Shapes Created in 3D StudioMax and

Sliced with Slic3r into one SVG slice set and errors

detected with printer resolutions x=0.1, y=0.1 (a); x=0.5,

y=0.5(b) and x=1, y=0.5(c).

In Figure 9, a model of perforated sphere obtained

from Thingivers.com was cut into 242 slices. On

validating the 12

slice with a printer resolution of

Resolution-aware Slicing of CAD Data for 3D

127

x=0.1 and y=0.1, our model detected only 3

defective regions. This demonstrates that as

resolution is decreased, our model is able to detect

more defective regions. However, when this model

was validated with a resolution of x=1, y=1, the

number of contours with errors reduced to 4. This

shows that when the resolution is reduced, our

model may detect fewer errors since some small

features will not be scanned due to frequency in the

particular direction. In effect, features, smaller than

1, are more likely to be missed if the resolution is 1

than when the resolution is 0.5. Our model will not

propose a correction of 3D model. We assume that

correcting the 3D model is outside the scope of

quality inspection. As an inspection tool, its main

focus is to detect and report defects. Also, it is not

always practicable to modify 3D models as they may

be required to be exact replicas of the physical

model. In this case, the utility of our model is in

informing the user that the model will have defects

when printed by a particular printer with a quoted

resolution. Other models used for testing our

application are shown below.

Figure 9: Perforated Sphere (Thingivers.com) and Sliced

with Slic3r into 242 SVG slice sets and errors detected on

different slices at different resolutions.

4.1 Discussion

We have suggested a novel system for validating the

printability of 3D CAD models by a given printer

based on the printer resolutions. It provides

significant benefits to the quality assurance of 3D

printed parts. We showed how our model can utilize

the slicing output of 3D CAD data from any slicer

application outputting SVG <g> sets and polygons.

We demonstrated that it is possible to visualize, in a

slice, regions that will be unprintable in a CAD

model, when they fail resolution validations.

The model proposed by (Baumann F. Et al,

2015) supports our work. It established a framework

for testing the quality of slicing outputs of 3D

models produced in FDM printers. In their model a

few slicing applications were tested and measures

were established to evaluate the slicing applications.

The resolution of the printer was not included in the

framework. We suggested that the validation based

on resolution be added to the framework. We

proposed a system which can locate the positions

where the quality will fail in the actual printing.

An enhancement to our work can be in the area

of applying this concept in the 3D model design

process. The 3D models come from imaging data

that is segmented and consequently stitched up to

form a full model. However, other means of

generating models include direct design. During

modelling, shape operations are very common. They

include scaling, stretching or shrinking a model or

its features. If the resolution of the printer is added

as a constraint, a CAD design application can check

if a shape operation is going to result in defective

features and warn the user.

Our system did not attempt to propose a

correction of CAD models. As an inspection tool, its

main focus is to detect and report defects. Also, it is

not always practicable to modify 3D models as they

may be required to be exact replicas of the physical

model. In this case, the utility of our model is in

informing the user that the model will have defects

when printed by a particular printer with a quoted

resolution.

We also acknowledge that, as a study in 3D

domain, it would be beneficial to visualize our

results in 3D space. We focused on visualizing our

results in 2D space since the actual computations

(ray casting) are done in 2D space based on slices.

Our system also considered performance in terms

of optimal use of computing resources. Depending

on the size of the 3D file as well as the complexity

of the CAD data, the time it takes our model to scan

through a slice and compare/detect regions having

defects depends largely on the resolution applied in

both directions (x and y). The higher the resolution,

the longer it will take to validate the CAD model.

Our model did not consider curved shapes, as is the

case with STL. It means that each curve will be

represented by discrete lines. Therefore, the number

of lines in the polygon, defining each contour in the

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

128

slides, will influence how long it will take to

validate each model or slice. In our model, it took

less than one second to process a slice having 195

lines with a resolution of x=0.1 and y=0.1 in the

dimensions of the CAD model on a Windows(r) 7

PC with Intel(r) Core i5-4310U CPU @ 2.00GHz

2.60GHz processor running on 8.00 GB ram. It also

took less than one second to process this file when

the resolution was set at x=0.01 and y=0.01.

However, it took approximately 5 seconds to process

this same data when the resolution is set to x=0.001

and y=0.001.

Other 3D CAD models evaluated by our system

include:

Figure 10: More 3D Models analyzed by

SplicingResValidator.

REFERENCES

Munir, E., 2013. Slicing 3D CAD Model in STL Format

and Laser Path Generation. International Journal of

Innovation, Management and Technology, Vol. 4, No.

4, Aug 2013,410-413.

Stamos, I., Allen, P., 3-D Model Construction Using

Range and Image Data. In Proceedings of the

Conference on Computer Vision and Pattern

Recognition (CVPR ’00), USA, June 2000.

ISO 10303-21:2002 Industrial automation systems and

integration -- Product data representation and

exchange -- Part 21: Implementation methods: Clear

text encoding of the exchange structure.

Grimm, T., 2004. User's Guide to Rapid

Prototyping, Society of Manufacturing Engineers,

p. 55, ISBN 0-87263-697-6.

Topcu, O., Tascioglu, Y., & Unver, H. O. 2011. A Method

for Slicing CAD Models in Binary STL Format. 6

International Advanced Technologies Symposium

(IATS’11),May 2011, 163, 141-145.

Baumann, F., Buddayci, H., Grunert, J., Keller, F., Roller

D., 2015. Influence of slicing tools on quality of 3D

printed parts, Computer-Aided Design and

Applications, August 2015.

Freedman, David, H. 2013. Layer By Layer. Technology

Review 115.1: 50–53. Academic Search Premier.

Web. 26 July 2013.

Dolenc, A. and Makela, I. 1994. Slicing procedure for

layered manufacturing techniques, Computer Aided

Design, Vol. 1 No. 2, pp. 4-12.

Sabourin, E., Houser, S.A. and Bohn, J.H. 1996. Adaptive

slicing using stepwise uniform refinement, Rapid

Prototyping Journal, Vol. 2 No. 4, pp. 20-6.

Tyberg, J. and Bohn, J.H. 1998. Local adaptive slicing,

Rapid Prototyping Journal, Vol. 4 No. 3, pp. 118-27.

Sabourin, E., Houser, S.A. and Bohn, J.H. 1997. Accurate

exterior, fast interior layered manufacturing, Rapid

Prototyping Journal, Vol. 3 No. 2, pp. 44-52.

Tata, K., Fadel, G., Bagchi, A. and Aziz, N. 1998.

Efficient slicing for layered manufacturing, Rapid

Prototyping Journal, Vol. 4 No. 4, pp. 151-67.

Cormier, D., Unnanon, K. and Sanni, E. 2000. Specifying

non-uniform cusp heights as a potential for adaptive

slicing, Rapid Prototyping Journal, Vol. 6 No. 3, pp.

204-11.

Pandey, P.M., Reddy, N.V. and Dhande, S.G. 2003.

Improvement of surface

finish by staircase

machining in fused deposition modelling, Journal of

Material Processing Technology, Vol. 132 No. 1, pp.

323-31.

Pulak M. P, Venkata N. R, Sanjay G. D. Slicing

Procedures in Layered Manufacturing: a review, Rapid

Protoyping Journal, Vol. 9 No.5, June 2003, pp.274-

288.

Resolution-aware Slicing of CAD Data for 3D

129