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Selective data acquisition for direct integration ofreverse engineering and rapid prototypingSuchada Rianmora a , Pisut Koomsap a & Dang Phi Van Hai ba Industrial and Manufacturing Engineering, School of Engineering and Technology , AsianInstitute of Technology , Pathumthani, Thailandb Automation Department, Faculty of Mechanic and Technical , Nong Lam University , HoChi Minh, VietnamPublished online: 03 Dec 2009.

To cite this article: Suchada Rianmora , Pisut Koomsap & Dang Phi Van Hai (2009) Selective data acquisition fordirect integration of reverse engineering and rapid prototyping, Virtual and Physical Prototyping, 4:4, 227-239, DOI:10.1080/17452750903381397

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Selective data acquisition for direct integration of reverseengineering and rapid prototyping

Suchada Rianmoraa, Pisut Koomsapa* and Dang Phi Van Haib

aIndustrial and Manufacturing Engineering, School of Engineering and Technology, Asian Institute of Technology,

Pathumthani, ThailandbAutomation Department, Faculty of Mechanic and Technical, Nong Lam University, Ho Chi Minh, Vietnam

(Received 15 June 2009; final version recevied 6 August 2009)

Reverse engineering (RE) has been used closely with rapid prototyping (RP) for fabricating

one object from another. Existing RE�RP integrations all begin with the data acquisition of

the entire surface of an object. This large point cloud data contains redundancy that must be

reduced to avoid unnecessary difficulty in a subsequent surface reconstruction step.

Presented in this paper is an alternative approach for direct RE�RP integration. Rather than

performing data reduction after capturing the data of an entire object, data are acquired

selectively and locally layer by layer. An image-processing algorithm has been developed for

recommending the scanning positions based on the part complexity to minimise the number

of scans. The result of each scan is contour data points, which can be directly used to

generate commands for fabricating a prototype. The implementation of this selective data

acquisition approach with a non-contact measuring device is also presented and discussed.

Keywords: reverse engineering (RE); point clouds; selective data acquisition; rapid

prototyping (RP); image processing

1. Introduction

The concept of rapid prototyping (RP) was introduced in

the early 1980s, and has become an important technology

that reduces manufacturing time by 30�50% for product

development (Chua et al. 2009). This process is beneficial

for accelerating the design process, continuously improving

products and increasing competitiveness. Typically, the RP

process starts with obtaining a 3D CAD model, which can

be created directly on any commercial CAD software. Since

the data formats are different among the software, the

created model will be converted to a common format, called

a stereolithographic (STL) file, before being sliced. The

STL model is then sliced by the slicing process,

and based on these sliced layers machine commands are

generated to build a physical prototype layer by layer. Post

processes may be required depending upon the selected RP

technique.

Since the initial stage of the RP process is related to

obtaining a 3D CAD model, reverse engineering (RE)

can be used as an assisting technology that allows a CAD

model to be constructed quickly from a physical object and

to be ready for the RP process. Creating a model from a

physical object is very helpful when design/manufacturing

documentation is not available. It also enables us to capture

the surface of a complex object that may be difficult to

measure. In the RE process, the surface data of an object

are captured by using scanning or measuring devices and,

after enhancing their conditions, these point cloud data are

then used to generate the surface of the 3D model that can

be used in a CAE/CAM system (Sokovic and Kopac 2005).

*Corresponding author. Email: pisut@ait.ac.th.

Virtual and Physical Prototyping, Vol. 4, No. 4, December 2009, 227�239

Virtual and Physical PrototypingISSN 1745-2759 print/ISSN 1745-2767 online # 2009 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/17452750903381397

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Several techniques are available for acquiring the surface

data, and they can be categorised into two groups: contact

and non-contact methods. The coordinate measuring ma-

chine (CMM), a multi-jointed mechanical arm with a touch

probe, is an example of the contact method. This arm can

move along the x-y-z axis to get the coordinates of points on

the surface. Using CMM is very time-consuming, but this is

mandatory when high accuracy is required, whereas non-

contact methods such as computed tomography (CT) scans,

magnetic resonance imaging (MRI), optical digitiser and

ultrasound are more efficient in terms of speed and reduce

the human labour required. The detected distances are

converted into a point cloud map representing the 3D

surface (Lee et al. 2000). Besides the application of each

individual technique, their combination has been proposed

also to make the acquisition process more effective. By

incorporating a vision system with CMM, knowledge gained

from the rough model, created from four range images, is

used to minimise the number of CMM scans (Carbone et al.

2001). While the aforementioned techniques are non-

destructive approaches, a couple destructive approaches

have also been reported: abrasive computer tomography

(ACT) (Chang and Chiang 2003), and milling computer

tomography (MCT) (Liu et al. 2006). The two techniques

are similar in that the image of each cross-section is captured

after a physical part is subtracted layer by layer.

Point cloud data, acquired from these techniques, require

preprocessing operations, such as filtering outliers, smooth-

ing and blending of existing points before being registered

into one coordinate system (Schoene and Hoffmann 1997).

Obtaining the entire surface data of an object, especially

with a CCD camera or laser scanner, is quick, but produces

a very large batch with data redundancy that creates

unnecessary difficulty in surface reconstruction. Conse-

quently, a data reduction process is necessary and this has

led to many data reduction techniques.

Rather than conducting the data reduction process after

acquiring the data for the entire part, this paper presents an

alternative approach for direct interfacing of RE and RP by

considering part complexity before selectively acquiring

data. The obtained data can be directly applied in the RP

process to fabricate a prototype. The implementation of

local selective data acquisition with a non-contact acquisi-

tion device is also presented and discussed.

2. RE�RP interface

Build time and accuracy are two contradicting issues that

have been a major concern in rapid prototyping and have

led to the development of many slicing approaches (Pandey

et al. 2003), including those applying adaptive slicing (Suh

and Wozny 1994, Sabourin et al. 1996, Tyberg and Bohn

1998), direct slicing (Jamieson and Hacker 1995, Chen et al.

2001, Starly et al. 2005) and adaptive direct slicing concepts

(Ma and He 1999, Zhao and Laperriere 2000, Sun et al.

2007). The adaptive slicing concept has been introduced to

minimise the staircase effect resulting from layer-by-layer

construction. The thicknesses of layers are varied based on

the geometry change of a model along a build direction.

The direct slicing concept has been researched to skip STL

conversion to avoid chordal error, the maximum deviation

between the original surface of a CAD model and the

triangle of its STL model. Adaptive direct slicing has been

developed from the previous two concepts in an attempt to

minimise the construction time while maintaining prototype

accuracy at a high level by determining the appropriate

thickness for each directly sliced layer.

Since RE has been used closely with RP to make the

execution more effective, researchers have tried to realign

and remove redundant steps to interface RE and RP. Three

existing RE�RP integrations are illustrated in Figure 1 (Lee

and Woo 2000). They all begin with acquiring a point cloud

from the entire object. For the first path, data redundancy

existing in this huge point cloud is removed before the

remaining data are organised and then used to reconstruct a

3D surface model which is a typical input in the RP process.

Next, the model is converted into an STL model before being

sliced. This path is very common for practitioners since

most, if not all, RP applications use this format.

The concept of the voxel bin has been used to reduce the

point cloud data from the unorganised entire point cloud

before model reconstruction (Sun et al. 2001, Shin et al.

2004, Shi et al. 2006). For this concept, the smallest cube

that contains the entire point cloud data is created. This

cube is further divided into smaller bins in which the number

of bins is predetermined or calculated from the cube’s size.

The point that is closest to the centre point of each bin will

represent the point cloud data in that bin. There was an

attempt to use chordal deviation analysis to screen scanned

points before feeding into the voxel bin (Wire et al. 1996).

However, the taskof creating surfaces from the point cloud

data is difficult and time-consuming even with the help of

surface modelling programmes. The surface reconstruction

Entire point cloud data of surface

CAD file

STL file

RP slice file

2

3

1

1

1 2

Figure 1. Interfacing modes between RE and RP (Lee and

Woo 2000).

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accounts for 90�95% of the RE processing time compared to

5�10% of that for the digitising task (Schoene and Hoffmann

1997). If the process is intended for prototype fabrication,

time and accuracy can be improved enormously when

reconstruction is bypassed, and this leads to the second

path, whereby an STL model is being created from point

cloud data. Organised data can be further reduced even when

they are used for creating an STL model. Chen et al. (1999)

proposed two criteria for data reduction: percentage data

reduction, and maximum bounded error. For the first

criterion, an index is calculated for each point from the

normal vectors of its surrounding triangles, and points whose

indexes rank below the user-defined percentage are deleted.

For the second criterion, the normal vector of the patch of

triangles is calculated to determine its smallest bounding

box, and the improved patch, after data reduction, is

accepted if the bounding box error is within the tolerance.

Moreover, Liu et al. (2006) introduced an alternative

approach that applies Delaunay triangulation of planar

polygons and the closest local polar angle to generate

triangular facets from the tracked points of contour data

obtained from applying the contour tracing method on cross-

sectional images. However, the STL conversion step intro-

duces errors into the model such as chordal error, degener-

ated facets, undesirable gaps or flipped normal that lead to

incomplete cross-sections for manufacturing as layers

(Fischer 2000), and a decision needs to be made on the

trade-off between accuracy and time.

In order to eliminate the surface modelling task and the

STL file generation task, Lee and Woo (2000) proposed

direct integration of RE and RP as shown in the third path

in Figure 1. In their approach, to minimise the staircase effect,

the angular deviation method is used to detect sudden

changes of points onvertical cross-section planes oforganised

point cloud data, and points extracted at those positions are

used to create subregions. Boundary contours of each

subregion can be determined from the horizontal cross-

sections, and represented by B-spline curves. For each

subregion, datapoints of a cross-section, identified by halving

the boundary contours, can be deleted if they can be rep-

resented by a contour, calculated by the linear homotopy

method of boundary contours. If the deviation of any point

from the contour violates the tolerance, the cross-sectional

data will become a boundary contour, and cannot be deleted.

To simplify subsequent RP operations, a stack arrange-

ment of horizontal cross-sectional data is widely chosen to

apply for creating a prototype. Two main methods for slicing

cross-sectional data are a projection approach (Liu et al.

2002, 2003, Shin et al. 2004, Kumbhar et al. 2007) and a

virtual edge approach (Sun et al. 2006, Park et al. 2007). The

difference between the two approaches is in the way of

computing contour points. For the projection approach,

point cloud data are sliced into several layers, and points in

each layer are projected onto a plane. These cloud data are

converted into a set of regions of points by using an adaptive

subdivision algorithm. Based on the orientation of their

tangent vectors, points are sorted and compressed; conse-

quently, only feature points are kept while their neighbours

are deleted. For the virtual edge approach, the minimum

distance-based correlated point-pairs method is used to

extract sectional contours from point cloud. Then, on each

slice, contour points are linked to form cross-sectional

contours by using the edge-neighbourhood chain-coding

method. This linked contour is used for the RP machine.

Since the RE�RP interface is an error-prone process,

bypassing steps, especially reconstruction, can improve

accuracy and build time. As aforementioned, the three

existing RE�RP interfaces have in common that they all

acquire the entire point cloud data, and require a data

reduction process. It may be better if the reduction process

is performed before data acquisition. In order to increase

process efficiency, this paper proposes a new approach to

acquire RE data for directly generating contour data for the

RP process that is described in next section.

3. Selective data acquisition

Presented in this section is the development of selective data

acquisition as an alternative to the three existing paths as

illustrated in Figure 2. The concept of this selective data

acquisition is that data are acquired selectively from an object

layer by layer, rather than acquiring the entire point cloud,

such that they can be directly used to generate commands for

creating a prototype. The key component of this concept is

scanned positions which will be determined relatively from

the bottom up based on the complexity of a part to minimise

the number of scans while maintaining accuracy, as shown in

Figure 3. Typically, layers with a maximum thickness are

desirable, except when a technical constraint exists in the RP

technique. This implies that the distance between two

scanned contours, in this case, is at a maximum. However,

the maximum thickness should be recommended only for a

layer that is simple. The simplicity of a layer is decided based

Entire point cloud data of surface

CAD file

STL file

RP slice file

Local point cloud data

2

3

1

1

1 2

New approach4

Figure 2. Interfacing modes between RE and RP with new

approach.

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on the identicalness of its top and bottom contours. The layer

is considered to be simple (i.e., error does not exist) if its

two contours are identical or at least their maximum

deviation is within tolerance. Otherwise, its thickness should

be reduced until it achieves a simple or minimum thickness

layer to minimise the staircase effect. The maximum allow-

able contour deviation, related to the surface quality of a

prototype, is determined by the user, while the maximum and

minimum allowable layer thicknesses are determined by the

capability of the selected RP system.

Image processing has been used to implement this

proposed concept in which four orthogonal views of an

object are captured and each image, to reduce computation

time in the subsequent steps, is trimmed down to the

minimum rectangular size that still contains the entire

object. The Canny edge detection algorithm is then applied

to extract edge images. On each edge image, the row of

pixels at the bottommost contour is acquired first, followed

by the row of pixels at the maximum allowable distance

from the bottom. The relationship between pixel and metric

dimensions is obtained from the known height of an object

and the number of rows representing the object’s height

in the image. Next, the two sets of data are compared.

Two criteria are applied for identifying the identicalness of

the two contours: contour deviation, and features on the

layer’s surface. On an image, the contour deviation criterion

is determined from the number and location of pixels

representing edges. Extra features are determined from the

number of edge pixels of additional rows acquired between

the contours. Unless the criteria are satisfied for all images,

new acquisition at a closer position is required. This activity

is repeated until the new set of pixels is similar to the one of

the bottom contour or is at the lowest allowable position.

Consequently, the appropriate thickness of this particular

layer is determined, the scanning position is recommended

and the row of pixels of this top contour automatically

becomes the bottom contour for the following layer. The

process is repeated until the topmost contour is obtained.

The obtained recommended positions are then passed to

the scanning device for acquiring activities. The inputs of

this process can be both symmetric and non-symmetric

parts. This approach allows additional views to be inserted

to review the features. The flowchart (Figure 4) and details

of each stage in the selective data acquisition approach are

described in the following sections.

3.1 Preparing input image

The inputs for determining recommended scanning posi-

tions on an object are the images of the four orthogonal

views, front, right-side, back and left-side views, of the

physical object itself. The object must be oriented similarly

to when it is scanned, and the four images must be taken

under the same setup. Ideally, the heights of the object in

the four images should be equal, but it is unavoidable that

they may be slightly different. However, the negative

influence of the slightly different heights on the determina-

tion of the scanning positions is insignificant when the

calculation on each image is referred to the known height of

the object, instead of the numbers of rows representing the

height of the object from other images.

In the next step, edge images will be extracted from these

four images. High contrast between object and background

will improve the quality of edge images. To prevent shadow

and reflection from appearing on the images, a lighting

setup is required. Shiny objects or objects with printing on

the surface should be spray-coated also to subdue the

appearance of unnecessary extra edges on the edge images

and flat colour is recommended for obtaining good results

(Rianmora and Koomsap 2008). However, the coating

process may be skipped for objects with low contrast

between the colours of the surface and printing.

3.2 Extracting edge images

An edge-detector algorithm was introduced into this study

to extract information on an object from the images. Based

on sudden changes in intensity value among consecutive

pixels, boundaries and features on the object can be

represented by edges. Among the edge-detector algorithms,

the Canny edge detector has been selected for this study

because its results outperformed other well-known edge

detectors (Heath et al. 1998). Focusing on the three criteria

of good detection, good localisation and simple response to

an edge, the implementation of the Canny edge-detection

algorithm was executed in steps (Sonka et al. 1999, Ding

and Goshtasby 2001) starting with applying a symmetric

2D Gaussian filter to smooth out the entire image and to

remove noise. Then, similar to the Sobel filter, two

convolution masks were applied to all pixels in the image

to estimate the gradients in both X and Z directions. The

results of the two convolutions were used to determine

the orientations and magnitudes of edges, subsequently

(a) (b)

Simple shape

Complex shape

Simple shape

Complex shape

Figure 3. Identifying a layer’s complexity based on the

selective data acquisition approach on the object images. (a)

Front view. (b) Side view.

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Figure 4. Flowchart of the selective data acquisition algorithm.

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providing ridges on the image. The process, called non-

maximal suppression, was then used to trace along the top

of the ridges, and to suppress any pixels that are not

considered to be on the ridge tops. Thresholding with

hysteresis was then applied to remove streaking, spurious

responses to the ridge tops caused by noise. To execute the

Canny edge-detection algorithm, a user was asked to assign

the values for two parameters: sensitivity threshold (0.01�

0.99) and sigma (a positive value representing standard

deviation of the Gaussian filter).

3.3 Identifying a layer’s complexity

After applying the Canny edge-detection algorithm onto

the four orthogonal views of an object, the information for

creating a layer was represented by edges on the four edge

Figure 5. The user interface of the selective data acquisition program on LabVIEW software.

Figure 6. The data acquisition unit.

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images. A layer was generated from two consecutive

contours, and was considered to be simple when projected

on a common plane: the two contours were identical in

size, shape and location (i.e. no contour deviation); and

when no additional feature existed on the surface between

the two contours.

The identicalness can be determined from the slopes

of edges and extra edges between the contours. A layer

is considered to be simple when all edges run parallel

vertically from top to bottom contours (i.e. no slopes exist

on all edges), and no additional edge is in between or

connects to either contours.

For any contour k, acquired from the bottom up (/zkB

zk�1) of the four obtained edge images, let Ak, Bk, Ck, and

Dk be row matrixes of pixel coordinate ais, bjs, cts, and dus

on the first, second, third, and fourth edge images,

respectively, and ai � Ak; /bj � Bk; ct � Ck; and du � Dk; when

their IE(x; z)s are equal to /IEmax:

/IE(x; z) is the function of the edge image at coordinate

(x,z), and IEmax is for the white pixels in binary image. mk; /

nk, /ok, and pk represent the number of edges of contour k on

the first, second, third, and fourth edge images.

For any pair of contours k and k�1, the two contours

are identical when the numbers of edges are the same on

both contours, and the contour deviations are smaller than

the maximum allowable contour deviation (o).

mk�1�mk; nk�1�nk; ok�1�ok; pk�1�pk (1)

kDAkkF �kAk�1�AkkF �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXm

i�1

jai;k�1�ai;kj2

vuut 5o (2)

kDBkkF �kBk�1�BkkF �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXn

j�1

jbj;k�1�bj;kj2

vuut 5o (3)

kDCkkF �kCk�1�CkkF �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXo

t�1

jct;k�1�ct;kj2

vuut 5o (4)

kDDkkF �kDk�1�DkkF �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXp

u�1

jdu;k�1�du;kj2

vuut 5o (5)

The maximum allowable contour deviation (o) can be

determined by considering the acceptable ranges of cusp

heights and of layer thicknesses. Cusp height, a digressive

distance between the designed surface of a model and the

actual layer of the prototype, has been widely applied in

slicing algorithms to determine a layer thickness (Dolenc

and Makela 1994).

Extra features do not exist on the surface between the two

contours when the number of edges at any positions between

the contours equals the number of edges on contour k.

ms�mk; ns�nk; os�ok; ps�pk When zkBzsBzk�1 (6)

where ms; ns; os; and ps are the number of edges at any

positions between the contours k and k�1 of the first,

second, third and fourth edge images, respectively.

3.4 Calculating new slicing positions

If the conditions for a simple layer are not satisfied at the

present position, the distance between the two contours will

be decreased according to the violated conditions. For the

first condition in which the two contours are different in size,

shape or location (i.e. the contour deviations are larger than

the maximum allowable contour deviation), the deviation is

assumed to be a linear function, and the new recommended

scanning position will be

hnew�max(hrec; hmin) (7)

when

hrec�min(hrec;1; hrec;2; hrec;3; hrec;4) (8)

For any hrec where the violation does not exist on the edge

image, hrec is equal to the present value. For any hrecwhere the

violation is on any edge image, hrec will be

hrec�o

kXk�1 � XkkF

�hcur (9)

where /hnew: a new scanning position relative to the

bottom contour of a layer,

hmin: the minimum allowable thickness,

hrec: a recommended scanning position,

hcur: the current scanning position,

hrec,1, hrec,2, hrec,3, hrec,4: a recommended scanning

position of the first, second, third and fourth edge

images,

Xk, Xk�1: a row matrix of pixel coordinates of

contour k and k�1 on an edge image.

Figure 7. Two sample models used to demonstrate this

proposed approach. (a) Penguin model. (b) Skull model.

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For the second condition in which an additional feature

exists on the surface between the two contours, the new

recommended scanning position will be equal to either half

of the current value or the minimum allowable thickness.

Table 1. The steps in the selective data acquisition process for the penguin model.

Step 1: Capturing four images at four vertical orthographic views and identifying the

boundary containing the entire part on each image.

Step 2: Applying the Canny edge algorithm to determine the edge images.

Step 3: Locally analysing the images layer by layer and recommending scanning positions

according to the two criteria: contour deviation and features on surface layer.

Note: The maximum thickness was 3 mm.

The minimum thickness was 1 mm.

Step 4: Acquiring data from the object at the recommended positions.

Step 5: Sending the scanning contours to the RP fabrication process.

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hnew�max(0:5hcur; hmin) (10)

4. Application of selective data acquisition

This section presents the implementation of the selective data

acquisition approach. Four perpendicular vertical planes,

front, right-side, back and left-side views, of an object

were captured and stored into bitmap files (i.e. *.bmp).

They were fed into the selective data acquisition algorithm

that has been implemented on LabVIEW software to

determine the scanning positions. After obtaining all the

recommended scanning positions, the data acquisition unit

scanned an object surface at those positions.

The user interface of the program is shown in Figure 5. In

order to complete program requirements, a user was asked

to assign the height of an object, the maximum and the

minimum thickness, the maximum allowable contour

deviation and the zoom factor (for display purposes),

before retrieving the four orthogonal images of an object.

The maximum and minimum layer thickness can be

specified according to the capability of the RP machine

used for fabricating the prototype. The four input images

are displayed on the top left section of the screen. The

program outputs the recommended scanning positions of

an object as shown on the bottom left of the screen.

Illustrated in Figure 6 is the data acquisition unit built in

house for demonstrating implementation of the proposed

concept. This point-based scanning unit consists of a simple

4-axis motion control system, X, Y, Z and c (rotating

around the Z-axis) axis, and the distance sensor attached

onto the X-axis. After receiving recommended scanning

positions from the algorithm, the distance sensor scanned

the object surface while the platform (c-axis) was rotated at

a constant rotational velocity for obtaining cross-sectional

points and the detected distances were recorded.

Two sample models presented in Figure 7 were used to

demonstrate the selective data acquisition algorithm. The

thickness of recommended scanning positions was set

between 1 and 3 mm, so the results can be clearly seen.

Table 1 illustrates the application steps of the selective

data acquisition technique. The parameter requirements fed

into the programme were: the object’s height (78 mm),

maximum thickness (3 mm), minimum thickness (1 mm)

and maximum allowable contour deviation (0.85 mm). The

programme started downloading four orthogonal images

and the minimum boundary that contains the entire part

was identified on each view in step 1. The edge images were

extracted by the Canny edge-detection algorithm where the

filter parameters are adjusted properly for indicating the

outer profile of the object images in step 2. For the penguin

model, the specified threshold and sigma were 0.25 and 1,

respectively. It was found that the result is highly dependent

on the quality of the images; therefore, contrast in the

Figure 8. Cross-section points of the penguin. (a) Uniformly scanned model with 1 mm. (b) Uniformly scanned model with

3 mm. (c) Selectively scanned model.

Table 2. The number of scanning layers resulted from scanning penguin model with three scanning methods.

Methods omax (mm) Layer thickness (mm) Number of layers Number of points

Uniform scanning (min.) 0.85 1 78 56,321

Uniform scanning (max.) 0.85 3 26 19,031

Selective scanning 0.85 1.053 68

1.5 1 71 53,692

1.935 1

2.969 1}

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images affecting edges should be properly adjusted before

they are analysed by the Canny edge detector. The

programme then analysed the four edge images and

recommended scanning positions in step 3. Next, surface

data were acquired from the penguin model according to

the recommendations. In this demonstration, a prototype

was made from rubber sheets with different recommended

thicknesses. The obtained cross-section points, resulting

from step 4, were used directly to generate tool paths for a

waterjet cutting machine in step 5 to perform contour

cutting on prepared rubber sheets layer by layer. The layers

were then stacked up to form a prototype.

The result of selective data acquisition was compared

with the result from the uniform scanning approach with

the thickness of 1 and 3 mm. respectively. They were

implemented with the maximum contour deviation of 0.85

mm. The results are shown in Figure 8 and Table 2. In the

case of uniform scanning with minimum and selective layer

thickness, the cross-section points preserved the geometric

shape of the object, while uniform scanning with maximum

layer thickness some features were missed. However, scan-

ning the object surface with a very thin thickness uniformly

resulted in 52 and 7 more layers than the uniformly thick

layer and selective scanned models, respectively. By apply-

ing the selective scanning approach, the number of layers

and the number of points were reduced by 8.97% and

4.67%, respectively, which has a direct impact on build time

for the rapid prototyping (RP) process.

Figure 9. Selective data acquisition on the skull model.

Figure 10. Cross-sectional points of the skull. (a) Uniformly scanned model with 1 mm. (b) Uniformly scanned model with

3 mm. (c) Selectively scanned model.

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Similar to the previous example, the selective data

acquisition approach has been applied to the skull model.

Illustrated in Figure 9 is the result of each step in the

proposed algorithm. The specified threshold and sigma for

Canny edge detection were 0.2 and 1.75, respectively. The

contour data of the different scanning methods (uniform

scanning with maximum and minimum layer thickness and

selective scanning) are presented in Figure 10 and Table 3.

Table 3. The number of scanning layers resulted from scanning skull model with three scanning methods.

Methods omax (mm) Layer thickness (mm) Number of layers Number of points

Uniform scanning (min.) 0.85 1 78 46,454

Uniform scanning (max.) 0.85 3 26 14,708

Selective scanning 0.85 1.113 53

1.222 1

1.502 1

1.969 1 59 40,796

2.131 1

2.335 1

2.917 1

}

Working process of RE interfacing with RP

(a) The steps of the new approach

Point acquisition

Extraction of curvatureinformation

Subregioning point data model

Reduction of point

Generation of slice data

RP part fabrication

(b)The steps proposed by Lee and

Woo (2000)

Capturing image

Extracting edge image

Recommending scanned positions

Acquiring object surface

RP part fabrication

Figure 11. The steps taken for direct interfacing of RE and RP. (a) The existing approach presented by Lee and Woo (2000).

(b) The proposed selective data acquisition.

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Scanning the skull model with a thickness of 1 mm

uniformly resulted in 19 more layers than the selective

scanned model. By using the selective scanning approach,

the number of layers and the number of points were reduced

by about 24.36% and 12.18%, respectively. According to the

results, the selective scanning can reduce the number of

layers and the number of points and shorten the data

acquisition time while maintaining the specified surface

quality of the part.

5. Conclusions

Selective data acquisition has been presented as an alter-

native to the existing approaches for interfacing between

reverse engineering and rapid prototyping to minimise

prototype development time. Rather than acquiring the

entire data, this approach collects data layer by layer which

can be directly interfaced with rapid prototyping. This new

approach can improve scanning performance by reducing

the number of scanning layers and the number of points

acquired. Figure 11 depicts the difference between the

proposed selective data acquisition and that of the existing

approach presented by Lee and Woo (2000) in the steps

taken for direct interfacing of RE and RP. This selective

data acquisition can also be applied to select data at

recommended positions from the entire data (the third

path) and be applied in adaptive direct slicing a 3D CAD

model (Rianmora and Koomsap 2009). Currently, its

performance depends upon image quality. With the Canny

edge detection there still exist some constraints in selecting

suitable parameters for the image smoothing phase and the

edge linking phase. For better performance of the algo-

rithm, an alternative edge detection method that automa-

tically adjusts should be considered. Also, during data

acquisition, scan speed is constant and independent of the

complexity of a contour. Data can be further reduced by

varying scan speed according to the contour complexity.

References

Carbone, V., et al., 2001. Combination of a vision system and a coordinate

measuring machine for the reverse engineering of freeform surfaces. The

International Journal of Advanced Manufacturing Technology, 17, 263�271.

Chang, C.C. and Chiang, H.W., 2003. Three-dimensional image reconstruc-

tions of complex objects by an abrasive computed tomography apparatus.

The International Journal of Advanced Manufacturing Technology, 22,

708�712.

Chen, X., et al., 2001. Direct slicing from PowerSHAPE models for rapid

prototyping. The International Journal of Advanced Manufacturing

Technology, 17, 543�547.

Chen, Y.H., Ng, C.T. and Wang, Y.Z., 1999. Generation of an STL file from

3D measurement data with user-controlled data reduction. The Interna-

tional Journal of Advanced Manufacturing Technology, 15, 127�131.

Chua, C.K., Leong, K.F. and Lim, C.S., 2009. Rapid prototyping: principles

and applications. 3rd edition. Singapore: World Scientific.

Ding, L. and Goshtasby, A., 2001. On the Canny edge detection. Pattern

Recognition, 34, 721�725.

Dolenc, A. and Makela, I., 1994. Slicing procedure for layered manufactur-

ing techniques. Computer Aided Design, 26, 119�126.

Fischer, A., 2000. Multi-level models for reverse engineering and rapid

prototyping in remote CAD systems. Computer Aided Design, 32, 27�38.

Jamieson, R. and Hacker, H., 1995. Direct slicing of CAD models for rapid

prototyping. Rapid Prototyping Journal, 1, 4�12.

Kumbhar, V.K., Pandey, P.M. and Rao, P.V.M., 2007. Improved inter-

mediate point curve model for integrating reverse engineering and rapid

prototyping. The International Journal of Advanced Manufacturing

Technology, 37, 553�562.

Lee, K.H. and Woo, H., 2000. Direct integration of reverse engineering and

rapid prototyping. Computer & Industrial Engineering, 38, 21�38.

Lee, K.H. Woo, H. and Kang, E. 2000. Efficient point handling methods

for reverse engineering. TCT 2000 Conference, 225�232.

Liu, G.H., et al., 2002. Error based segmentation of cloud data for direct

rapid prototyping. Computer Aided Design, 35, 633�645.

Liu, G.H., et al., 2003. Modeling cloud data for prototype manufacturing.

Journal of materials processing technology, 138, 53�57.

Liu, Z., Wang, L. and Lu, B., 2006. Integrating cross-sectional imaging

based reverse engineering with rapid prototyping. Computer in Industry,

57, 131�140.

Ma, W. and He, P., 1999. An adaptive slicing and selective hatching strategy

for layered manufacturing. Journal of Material Processing Technology,

89�90, 191�197.

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

layered manufacturing: review. Rapid Prototyping Journal, 6, 274�288.

Park, H.T. Chang, M.H. and Park, S.C. 2007. A slicing algorithm of point

cloud for rapid prototyping. Proceedings of the 2007 summer computer

simulation conference, San Diego, California, USA.

Rianmora, S. and Koomsap, P. 2008. Investigating the influence of color

light in data acquisition. Proceedings of 2008 The 9th Asia Pacific

industrial engineering & management systems conference (APIEMS), Bali,

Indonesia.

Rianmora, S. and Koomsap, P. 2009. Recommended slicing positions for

adaptive direct slicing by image processing technique. International

Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170�009�2162�0.

Sabourin, E., Houser, S.A. and Bøhn, J.H., 1996. Adaptive slicing using

stepwise uniform refinement. Rapid Prototyping journal, 2, 20�26.

Schoene, C. and Hoffmann, J., 1997. Reverse engineering based on multi-

axis digitizer data. International Conference on Manufacturing Automa-

tion (ICMA ’97), 1, 909�914.

Shi, M., et al., 2006. Triangular mesh generation employing a boundary

expansion technique. International Journal of Advanced Manufacturing

Technology, 30, 54�60.

Shin, H.Y., Park, S.Y. and Park, E.J., 2004. Direct slicing of a point set

model for rapid prototyping. Computer Aided Design and Applications, 1,

109�116.

Sokovic, M. and Kopac, J., 2005. RE (Reverse Engineering) as necessary

phase by rapid product development. Journal of Material Processing

Technology, 175, 398�403.

Sonka, M. Hlavac, V. and Boyle, R. 1999. Image processing. Analysis and

machine vision, 2nd edition. PWS Publishing.

Starly, B., et al., 2005. Direct slicing of STEP based NURBS models for

layered manufacturing. Computer Aided Design, 37, 387�397.

Suh, Y.S. and Wozny, M.J. 1994. Adaptive slicing of solid freeform

fabrication processes. Proceedings of solid freeform fabrication sympo-

sium, Texas, USA, 404�411.

Sun, S.H., Chiang, H.W. and Lee, M.I., 2007. Adaptive direct slicing of a

commercial CAD model for use in rapid prototyping. The International

Journal of Advanced Manufacturing Technology, 34, 689�701.

238 S. Rianmora et al.

Dow

nloa

ded

by [

UQ

Lib

rary

] at

16:

14 1

1 N

ovem

ber

2014

Sun, W., et al., 2001. Cloud data modeling employing a unified,

non-redundant triangular mesh. Computer Aided Design, 33,

183�193.

Sun, Y., Guo, D. and Jia, Z., 2006. B-Spline surface reconstruction and

direct slicing from point clouds. The International Journal of Advanced

Manufacturing Technology, 27, 918�924.

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

Journal, 4, 118�127.

Weir, D.J., et al., 1996. Reverse engineering physical models employing

wrap-around B-spline surfaces and quadrics. Proceedings of the Institu-

tion of Mechanical Engineers-Part B, 210, 147�57.

Wu, Y.F., et al., 2004. Modeling cloud data using an adaptive slicing

approach. Computer Aided Design, 36, 231�240.

Zhao, Z. and Laperriere, L., 2000. Adaptive direct slicing of the solid model

for rapid prototyping. International Journal of Production Research, 38,

69�83.

239Virtual and Physical Prototyping

Dow

nloa

ded

by [

UQ

Lib

rary

] at

16:

14 1

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ovem

ber

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