Paper Dec 2016

School of Science, Engineering and Technology
Department of Engineering
A Methodology to Apply 3D Scanning and Software Tools to Reverse Engineer
Various Geometric Shapes
by
Olga Zavala Handal
A Graduate Project Presented to the Department of Engineering
in Partial Fulfillment of the Requirements
for the Degree of Masters in Science in Industrial Engineering
San Antonio, Texas
December 12, 2016
Supervising Advisers:
Dr. Angel E. Esparza
Dr. Rafael Moras
Dr. Winston Erevelles

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Acknowledgments
It is a pleasure to thank those who made this project possible. Firstly, I thank God for
the strength and courage to keep me moving forward. I wish to express my sincere gratitude to
Rafael Moras, Ph.D., and Angel E. Esparza, Ph.D., for their encouragement, guidance and
support on this endeavor from start to end as well as their assistance to help me achieved this
honor, and to Dean Winston Erevelles, Ph.D., for giving us the opportunity to develop and
understand this project. Lastly, I offer my regards and blessings to all my family and friends
who supported me in any respect during the completion of the project.

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Abstract
We present a framework to efficiently and accurately reverse engineer mechanical
components by converting given artifacts to Computer-Aided Design (CAD) models. This
framework was developed by applying CAD tools, rapid prototyping (RP) machines, simple
hand tools, fixed and movable laser scanners, as well as reverse engineering (RE) software to
several primitive objects and test parts with compound features. Software tools and analytical
methods were used to determine the difference between key dimensions as designed and as
reverse engineered.

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Table of Contents
Abstract........................................................................................................................................... 3
Introduction..................................................................................................................................... 5
Background Information................................................................................................................. 6
Project Plan and Methodology........................................................................................................ 8
CAD Design................................................................................................................................. 9
STL File & Part Prototyping..................................................................................................... 10
Part Scanning & Sketch Extraction .......................................................................................... 10
Experimental Setup....................................................................................................................... 11
Hardware Tools......................................................................................................................... 11
Software Tools........................................................................................................................... 15
Implementation ............................................................................................................................. 16
Establishing a Reference Plane................................................................................................. 19
Results........................................................................................................................................... 20
Conclusions................................................................................................................................... 23
Recommendations for Future Work.............................................................................................. 25
References..................................................................................................................................... 25
Appendix A: Images FaroArm...................................................................................................... 29
Appendix B: Images from the E-Scanner ..................................................................................... 33

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Introduction
Manufacturing, the production of goods or wares by manual labor or by machinery, is a
key economic driver for entities that depend on this activity. Therefore, optimization of design
and manufacturing techniques is essential, and currently used in instances such as to rebuild a
damaged machine or to fix a broken basic part on a vehicle. Many engineers restructure
systems using advanced manufacturing techniques, with the fundamental idea of solving
problems. Reverse engineering (RE) is one way of solving such problems, and it consists of
deconstructing a system, identifying broken or nonfunctional parts, and redesigning prototypes
to improve the system in general. In this research, we examined the field and process of RE, by
applying rapid prototyping (RP) and scanning techniques as a tool for the development of
discrete parts and components, particularly primitive geometric shapes, which functioned as
test artifacts. By scanning several test parts, we identified the variability in reproduction of
these shapes, which permitted the assessment of discrepancies. Through these calculations, the
errors in measurement were calculated. The measurement error when printing and designing
was analyzed by scanning the part, then creating an STL (STereoLithography) file, and
subsequently, printing the parts in three dimensions (3D).
Using different machines and components, the proposed method for experimental
accuracy was applied to the geometric shapes, in order to determine the best technique to
reverse engineer them. The methodology involved several known primitive shapes, and
comparisons of their scans to investigate both, absolute accuracy and acceptable tolerance.

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Background Information
The role of engineers, particularly those engaged in design and manufacturing of any
product, has changed over the years due to the emergence of powerful and sophisticated RP
tools. Such shifts have required extensive research, some of which is reviewed in this section.
Three-dimensional (3D) laser scanning technology can be used to acquire a large
amount of target surface point cloud data that can identify the exact depth and real life
measurements of a part, when scanned. The measurements can be analyzed with the auto
surface command, which generates the surface model of the product. The auto surface
command also registers the point errors and accuracy on the measurements faster than other
commands involving manual manipulation of the point cloud data (Meng, 2015). In recent
years, much research have been conducted to streamline the point cloud data to reduce errors
and tighten tolerances. There are many specific ways to streamline the point cloud data, such
as: uniform grid method, plane fitting, and algorithm point cloud analysis (Wang, Luo, Wu,
2015).
Point clouds are generated using contact and noncontact methods. Contact methods use
physical probes that navigate the interior and exterior surfaces of components with point-to-
point measurements. Noncontact methods use devices such as lasers and LIDAR (Light
Detection And Ranging), to collect high-density spatial image data. The former is more
accurate but slower and labor-intensive; the latter, is less accurate but faster, and generates
denser point clouds. These methods are generally used in conjunction with software
applications that can translate point cloud data into geometry. Software applications are crucial
in extending the functionality of a scanner. Research presented by Kaner (1998) focused on

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interoperability of software applications and identified areas in RE techniques where these
applications are essential.
RE is applied is to reduce product development costs, lead time, and idle time due to
system non-availability when a component has failed. CAD data can be obtained to
manufacture a replacement, when it is not available. According to Thilmany (2012), engineers
are increasingly using RE since hardware and software have become more affordable, thereby,
helping engineering companies (especially smaller ones) speed-up development and cut
production costs (Thilmany, 2012). Reducing the timeline for product development saves
money in the overall time-to-market scenario. The integration of RP and scanning techniques
provides a fresh way to achieve the goal of RE.
RP techniques allow for automatic construction of physical models, and are used to
significantly reduce the time for the product development cycle, improving the final quality of
the designed product. Before the advent of RP, computer numerical controlled (CNC)
equipment, or manual machines and tools, were used to create prototypes (either directly or
indirectly) using CAD data (Nasr, 2006). CNC and other machining processes are subtractive
in nature, and consist of the removal of material in order to achieve the final shape of the part.
In contrast, the RP operations models are usually built by adding material layers, until the
whole part has been constructed.
In order to have a good generic representation of the designed object for Computer-
Aided Manufacturing (CAM) applications, and especially for process planning, the overall
designed object description and its features need to be represented in a suitable, structured
database. An object consists of manufacturing features that can be classified into form features,
which decompose into either simple or compound/intersection features (Nasr, 2006). Features

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are further classified into concave or convex0as attributes in the generic feature class. The
hierarchy of different features help determine the attributes that are needed and the ones that are
not needed.
Project Plan and Methodology
The objective of this project was to develop a reliable and repeatable methodology. As
shown in Figure 1, the project consisted in applying 3D scanning techniques and equipment, as
well as related software tools to reverse engineer various geometric shapes. The project was
conducted in three phases. These phases demonstrated how the cycle of RP and RE come
together. The cycle shown in Figure 1 was followed to identify the measurement difference
• CAD Design
Phase 1
• STL File
• Part PrototypingPhase 2
• Part Scanning
• Sketch
Extraction
Phase 3
Figure 1-Reverse Engineering Cycle.

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between the designed CAD model, the 3D printed part (using hand tools such a Vernier caliper
to measure), and the dimensions detected by 3D scanners.
CAD Design
Three test parts were either manufactured using RP or selected from a fixturing kit to
implement the RE cycle. These parts included a sphere, with an axial hole (SphereHole)
(50.800 mm, 50.800 mm, 101.600 mm); a cube, with an axial hole (CubeHole) (50.800 mm,
50.800 mm, 38.100 mm); and a clamp component (TPart1) (50.800 mm, 50.800 mm, 20.300
mm). While some of the test parts were used in early testing and for validation, most of the
testing focused on the SphereHole. A steel 1 x 2 x 3 block was also used to validate scanning
accuracy on a metrology artefact of known dimensions.
The dimensions of the three test parts as designed are shown in Figure 2. It should be
noted that x, y, and z dimensions were used by the scanning hardware and software to establish
Figure 2-Dimensions of Sample Parts

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locating planes for a given part. The z-axis dimensions are commonly shown as z/2, reflecting
user-inserted planes of symmetry.
STL File & Part Prototyping
In order to print part prototypes using RP machines (also referred to as 3D printers),
CAD models were translated into a neutral file format used by all 3D printers. This format,
called the STL file, converts a CAD image into a tessellated model using polygons and points.
Highly curved surfaces employ many polygons, resulting in very large files. These files are
sliced along the z-axis by the software, which is resident on 3D printers based on their native
resolution. As a result, any 3D object may be printed in additive fashion, by stacking layers of
x- and y- point data. In this phase, the CAD files of the test specimens were converted into the
STL format, and printed on 3D printers using various materials. Printed artefacts were
removed from the build platform, stripped of any supports, and cleaned using solvents and a
waterjet (when needed).
Part Scanning & Sketch Extraction
The objective of this phase was to scan the printed, physical objects, in order to develop
3D CAD models. Laser digitizers (also referred to as 3D scanners), were used to capture data
points from these objects and develop dense point clouds for further processing. Software tools
were used to condition these point clouds, and reconstruct the part design in CAD by extracting
key datum points, reference planes, and reference geometry. This information can be used to
recreate 3D CAD models. Additionally, for RE purposes, key measurements were evaluated by
comparing the CAD design measurements with the 3D printed outcome measurements.

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Experimental Setup
The experimental setup implemented in this project was comprised of the following list
of machines and materials, which permitted the implementation of the aforementioned
methodology.
Hardware Tools
1. FaroArm Edge: the FaroArm®
is a machine with an articulated arm that terminates in a
hard, spherical probe or a hand-held laser line probe that provides both contact and non-contact
measurement.
Figure 2- Specifications FaroArm Edge (Ltd, 2016)

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Unlike other scanning systems, the hard probe and the laser line probe can digitize
interchangeably without having to remove either component. Users can accurately measure
prismatic features with the hard probe, then laser scan sections requiring larger volumes of data
— all in one tool (Ltd, 2016). The user identifies what needs to be scanned, and manually moves
the arm to position the probes. The operator can scan millions of cloud points with the laser
scanner, and export those points to native Original Equipment Manufacturer (OEM) software or
other commercially-available packages for further processing. The arm connects to a host
computer via USB, and uses a device driver that permits the live-transfer of images and
measurements. The detailed specifications of this machine are shown in Figure 2. Images
produced by this device are featured in Appendix A.
2. E-Scan Optix 500: The E-Scan Optix 500 is a fixed device that is similar in objective to
the FaroArm. The difference is that this device captures point data while fixed in position in
relation to the object. As a result, this device is capable of scanning one view at a time.
Figure 3- E-Scanner (TECH-LABS, 2016)

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Parts may be indexed manually or using a turntable. Capture images are manually
clarified, and may be transferred to RE software via a proprietary device driver. The scanner and
its specifications are shown in Figures 3 and 4, respectively. Images produced by this device are
depicted in Appendix B.
3. Dimension Elite Printer: The Dimension Elite is a 3D printer that uses fused deposition
modeling, otherwise known as FDM Technology. It is capable of printing in various colors
using real ABS (Acrylonitrile Butadiene Styrene) plus thermoplastic. The build volume of this
printer is 203 × 203 × 305 mm (8 x 8 x 12 inches). Catalyst, the software application running
this machine, allows the user to print using fine (0.178 mm or 0.007 in.) and coarse (0.254 mm
or 0.010 in.) resolutions for layer thickness (Ltd., 2016). A picture of the Dimension Elite printer
is furnished in Figure 5.
Figure 4- E-Scanner Specifications 3D Digital Corps-- 3ddigitalcorp.com (TECH-LABS, 2016)

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4. Connex2: The Connex printer was the first 3D printer in the world to simultaneously 3D
prints multiple colors and materials (Ltd., 2016). The build volume of this printer is 255 × 252 ×
200 mm (10.0 x 9.9 x 7.9 in.). The layer thickness is as fine as 16 microns (µm or 0.0006 in.),
and the build resolution is x-axis: 600 dpi; y-axis: 600 dpi; z-axis: 1600 dpi. Numerous
composite materials can be manufactured by mixing the raw materials concurrently while
printing, including digital ABS, rubber-like materials, blended colors in rigid opaque, translucent
colored tints, and polypropylene-like materials with improved thermal resistance (Ltd., 2016).
The materials used for the 3D prints were FL X980 (Tango Black Plus) and RGD835 (Vero
White Plus). A picture of the Connex printer is provided in Figure 6.
Figure 6-Connex2 3Dprinter (Ltd., 2016)
Figure 5 - Dimension Elite 3D Printer (Ltd., 2016)

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Software Tools
5. Geomagic: The software used for CAD design was Geomagic Design X®
(refer herein as
Geomagic), a program intended to highlight and design a part or shape of any kind. This
software is similar to SolidWorks®
, a well-known commercial software package for 3D
modeling. However, Geomagic has the additional capability of manipulating point cloud data
and convert that data into a parametric CAD model. Geomagic supports RE by combining CAD
tools with 3D scan data processing to create feature base, editable solid models, which are
compatible with a wide range of commercial CAD software packages. The software contains
three modules: DesignX, Control, and Wrap. These process scanned point data, generating
polygons, surfaces, and parametric CAD. The architecture of the software is depicted in figure 7.
Figure 7- Software’s capability Geomagic (Geomagic, 2014).
Design X
NURBS-
Reverse
Engineering
Solid Sheet
RAPID
Forms
CONTROL
Polygons
Inspection
WRAP
NURBS Polygons
Points
Points
Points

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Implementation
Initial tests were conducted using a combination of test parts and machines for prototyping
and scanning. In Figures 8 and 9 we provide an overview of the application of the Geomagic
software and the FaroArm to the scanning process and the specific steps followed to create a
parametric CAD model from the point cloud. These figures are representative of the procedures
Figure 8- Flow Process to Scan and Extract Measurements.

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also followed using the E-Scan device. A data flow diagram used to connect the FaroArm to the
Geomagic software in order to scan and collect point cloud data is shown in Figure 8. It was
assumed that the arm was calibrated when initially installed. Recalibration is required only when
the system is moved or the backup batteries are replaced. When the system is initialized, the
three software modules within Geomagic interact with the arm and laser scanning head in two
different ways, as shown in Figure 8. In both cases, manual movement of the arm initiates the
operation of the encoders. A trigger on the FaroArm causes a point location to be recorded in the
Cartesian coordinate system based on the encoded values at each joint in the arm. This is similar
to the kinematics of an industrial robot arm. When configured with the laser scanning head, the
trigger stays on, and continuously records point data until the trigger is manually turned off.
Both of the modules used allowed the user to set various switches to control the operation of the
software. Early testing focused on Geomagic Wrap and Control. With the upgrade to the
software, much of the functionality needed for RE was embedded within Design X. As a result,
the majority of the scanning employed this software module.
The steps followed to convert a point cloud to either a non-parametric surface (mesh) or a
parametric CAD model (sketch and design intent) are shown in Figure 9. This enables the user
to connect point cloud processing, mesh processing, auto surfacing, and the identification of
design intent. During the processing of the point cloud(s), multiple point clouds (one point cloud
was generated for each scanning operation. For example, a cube might require as many as six
scanning operations to fully capture all six faces to fully represent the scanned object. These
point clouds were inserted into the work space and aligned to resolve translational and rotational
differences between multiple scans of the same artifact from different perspectives. Following
this step, the point cloud was manipulated to eliminate noise such as background, over scans, or

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Figure 9 - Step- by- Step after scan and create the final STL File
Software: Geomagic Design X (Gliffy, 2015)

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false positives from unrelated geometry. The point clouds were further processed to smooth out
and reduce the data set, generating the mesh representing the scanned surface. A mesh may be
linked to a fine net, being snugly draped over an object. This net represents the geometry of the
part using polygons, vertices, and edges, which are techniques commonly used in surface
modeling. Each of these steps had associated software settings that could be manually controlled
by the user, in order to approach specific problems in processing point clouds. Nonetheless, the
entire process could be automated using default settings in the software through the mesh
buildup wizard. Test parts were scanned using both operator-controlled and wizard-controlled
setups, and no appreciable differences in scans were observed. Consequently, the majority of the
scans were completed using the software wizard. The point cloud process terminated in the
generation of the mesh, giving the user the option of processing these data to generate a surface
or discern design intent. Both of these options are shown in Figure 9, and were used to generate
additional prototypes (auto surfacing) or extract features, curves, surfaces, and solid models
(design intent). The auto surface data was exported to a STL file format, and the test parts were
reprinted on the Connex2 as well as Dimension Elite printers for validation. Although not used,
design intent data supports STEP/ IGES exchange of information between different CAD
systems. The process shown in Figure 9 was applied to all test parts, with multiple replications.
Establishing a Reference Plane
An important step in generating a valid scan involved establishing a reference or
scanning plane. Omission of this step could yield inaccurate point cloud data. The first parts
were scanned over a white background to provide high contrast and aid in detecting the part.
However, it became apparent that separating the point cloud of the part from the point cloud of
the background was complicated and prone to human error, because it was hard to identify the

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essential points of the image. The results in which the variability in scanned data resulted in
inaccurate results is depicted in Table 1. The data shown are from eleven scans, with the
software set to minimum point spacing, two filter angles, and the maximum error allowed by the
user.
The solution to resolving the variablity in scans consisted of eliminating the background
plane by elevating the object to be scanned. This method offers several advantages, including
faster and more complete, accurate, and reliable scans.
Results
Table 2 is a summary of test part dimensions, as compared to original CAD dimensions.
The first set of five columns shows the Cartesian coordinates of the bounding box of the part, the
Table 1- Inaccuracy in results resulting from variability
Table 3 – SphereHole Analysis
Table 2 – SphereHole Analysis

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sphere diameter, and the diameter of the axial hole. As designed in CAD, the part was a sphere
with a diameter of 101.6 mm, with an axial hole with a diameter of 90.0 mm. The next set of
five columns represents dimensions obtained from the 3D printed part using a Vernier caliper.
These dimensions represent the average of four measurement trials. The final set of five
columns represents dimensions obtained from the 3D printed part using the FaroArm and the
Geomagic software. These dimensions represent the average of four scans and resulting
measurements from extracted sketches. Study 1 represents the cycle using the Dimension Elite
printer and RGD835(Vero White Plus) material. Study 2 represents the same cycle using the
Connex printer and FL X980 (Tango Black Plus) material.
The difference in measurements between the part as designed and as measured using two
different methods (calipers and scanner) appears at the bottom of the table, for each key
dimension on the part. For example, in study 1, the difference between the diameter of the
sphere as designed and as measured using the caliper was 101.600 mm - 99.441 mm = 2.159
mm (2.120% error). This was characterized as the measurement error for the caliper. Positive
values for the error indicate that the measured part was smaller than as the one designed in CAD,
and vice versa. Similarly, the measurement error for the same dimension, measured using the
FaroArm, was -0.200 mm (-0.200% error). Another key dimension on this test part was the
diameter of the axial hole (90.0 mm). The measurement error for the caliper and the FaroArm
was -0.600 mm (-0.670% error) and 0.200 mm (0.221% error), respectively.
The measurment errors for study 2 also appear in Table 2. The difference between the
diameter of the sphere, as measured using the caliper, and the FaroArm was 101.600 mm -
99.822 mm = 1.778 mm (1.750% error). This was characterized as the measurement error for the
caliper. Similarly, the measurement error for the same dimension, measured using the FaroArm

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was 0.180 mm (0.180% error). Another key dimension on this test part was the diameter of the
axial hole (90.000 mm). The measurement error for the caliper and the FaroArm was -0.100 mm
(0.111% error) and 0.020 mm (0.021% error), respectively. The measurement errors for the
CubeHole part are shown in Table 3. It was constructed in identical fashion to Table 2.
Scan of Metal Part 1 x 2 x 3 Gage Block
In order to verify whether coating a specular surface with powder might introduce small
errors in accuracy, an additional scan was conducted on a reference prismatic part of known
dimensions (Figure 10). This was a prismatic part with the following dimensions: length=3 in,
Scanned Image:
Exact
Measurements
E
Figure 10- FaroArm Scan (Using SprayON WL 745).
Table 4- CubeHole Analysis.

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breadth=2 in, and height=1 in. The part had several holes that were not considered for the test.
This part was sprayed with ON WL 745, a powder spray, to make it less specular and enable
scanning. The part was processed in identical fashion as the other two test parts, and resulted
in null measurement errors for both calipers and the FaroArm.
Conclusions
The purpose of this project was to develop a framework to efficiently and accurately
reverse engineer mechanical components. The following conclusions were drawn:
 A framework utilizing CAD tools, precision manual measurement instruments, laser
scanners, and RE software was developed and successfully implemented on several
primitive objects and test parts with compound features.
 The RE software was successfully applied to point clouds to extract features that were
used to generate parametric CAD models, thereby, meeting the fundamental objective
of RE.
 In general, laser scanning using a movable scanner produced higher quality results
with low measurement errors. For the devices used, the fixed scanner did not
produced results that were comparable to those generated by the movable arm.
 The laser scanner allowed for the generation of high quality scans with virtually no
part fixturing. This is typically a time-consuming and complicated process involving
several components such as a base plate, clamps, and other devices.
 Convex and concave cylindrical and spherical features were more accurately
measured using the scanner in comparison to the Vernier Caliper. Further, multiple
features can be captured and extracted in a single setting, as opposed to manually

24
measuring individual features of a given part. This also enables the user to quickly
establish the spatial relationship of various features in relation to a datum or to each
other.
 Linear and planar features were comparable when measured using the caliper or the
scanner. However, the caliper provided a quick means of verifying simple
measurements and may, therefore, be used to accelerate the RE process when such
features are scanned.
 The success of 3D scanning depended on several critical factors:
o Prior to initiating a scan, it is important to analyze the geometry of the part,
and position/orient the object to produce a quality point cloud.
o In order to achieve a successful scan, it may be necessary to divide the process
into multiple scans. The fragmented point clouds are then stitched together to
generate a complete image of the part. Alignment of fragmented point clouds
is critical to successful RE. It is possible to automate this step by using an
indexing device, such as a turn table.
o Fragmented point clouds or overlapping scans produce a large number of
duplicate points. Software utilities within the RE package should be used to
filter the point cloud to delete duplicate data, reduce noise, and order the data
before processing the point cloud into a mesh. Failure to do so, will generate
large STL files, where the polygons are not closed, resulting in invalid part
geometry.

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Recommendations for Future Work
Recommendations for further work are:
1. The use of analysis of variance principles would add a solid scientific foundation and
statistical rigor to studies in which an extension to the work presented here is attempted.
2. Analysis similar to the one presented here may be conducted using a Coordinate
Measuring Machine (CMM), in order to further analyze measurements errors.
Measurement tools such as micrometers, height gages, and bore gages could be used to
validate manual measurements.
3. Similar analysis can be replicated and should be conducted for new parts with varying
geometries. It would be of interest, as well, to determine additional “tipping points” in
geometry that would result in a particular methodology or measurement instrument
being preferred over another.
The results reported here should always be considered with caution, as the rapid
advancement of technology will irremediably render the equipment and methods used in this
project obsolete. Intelligent systems may, in a not so distant future, reduce, streamline, or even
eliminate many of the steps described here.
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Appendix A: Images FaroArm
The following images were taken from the FaroArm:
First Scan of Metal Block
History Tree Shown in Geomagic Design X

30
Accuracy Analyzer (TM) - Curvature Dimensions
Accuracy Analyzer (TM) - Deviation Color Map

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Accuracy Analyzer (TM) Top View
Accuracy Analyzer (TM) - Allowable Values

33
Appendix B: Images from the E-Scanner
Front Plane Scan
Top View Scan

Paper Dec 2016

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