Considering Designer’s Intention for theDevelopment of Feature Library of a Process
Planning System
H. Muljadi1, K. Ando2, H. Takeda1 and M. Kanamaru1
1 National Institute of Technology Email:hendry@nii.ac.jp
2 Shibaura Institute of Technology

In this paper, the creation of ontology of manufacturing features for thedevelopment of a feature library is described. The designer’s intention
described in functional data of the feature constructing face elements is
considered for the creation of the ontology. The creation of the manufacturingfeature ontology is intended to make the feature library be useful for the
extraction of manufacturing information for process plans generation.

1.

INTRODUCTION

In the production stage, dynamic changes such as increased production, part typechanges, machine breakdowns etc are ordinary occurrences. To deal with these
dynamic changes, we presupposed the need to integrate design, manufacturing andscheduling activities. Our research puts its goal in the generation of a CAPP systemthat can integrate process planning, scheduling and manufacturing activities [Sakurai,
T. et al, 2000]. Figure 1 shows the overview of the proposed CAPP system. The
system consists of 3 steps.
Step 1: feature sets creation from the product design data (CAD data).
Step 2: generation of process plan of a part based on the created feature sets.
Step 3: determination of optimal set of process plans for product mix.
DesignActivities
CAD
data

Integrated Process Planning System

Step 1:Feature Sets
Creator

Step 2:
Generationof Multiple
Process
Plans of aPart Type

Step 3:
Determinationof Optimal
Sets ofProcess
Plans for
Product Mix

S
C
H
E
D
U
L
I
N
G

Shop
Floor
Monitoring

Figure 1: Overview of Integrated Computer-Aided Process Planning System

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The optimal set of process plans obtained in Step 3 is used for the shop floor
scheduling. During the shop floor monitoring, re-scheduling may occur to handle thedynamic changes in the manufacturing stage. In the re-scheduling stage, we canreturn to Step 3 to determine the optimal set of process plans for the present shop
floor or production planning condition.
In order to bring this integrated process planning system to realization, we haveproposed a Feature Sets Creator that can lead to the generation of multiple processplans [Muljadi, H. et al, 2002]. For the development of the Feature Sets Creator, we
implemented Super Relation Graph (SRG) Method [Kao, C.Y. et al, 1995]. We didsome modification to the SRG Method and proposed the Modified SRG Method.
The Feature Sets Creator uses the Modified SRG Method to extract manufacturingfeatures from the product design information. We have further modified theModified SRG Method, and proposed the Extended SRG Method that is able toextract not only single depression features, but also protrusion and compoundfeatures [Muljadi, H. et al, 2003]. Protrusion and compound features can also be
extracted by the Extended SRG Method since these features can be represented by
the Extended SRG. We store manufacturing features and their Extended SRG
representations in a feature library.
For the development of a feature library for the CAPP system, we collect andstore manufacturing features, their corresponding Extended SRG representations andthe manufacturing information needed to create the shape of the manufacturingfeatures. However, we found that instances of same type of manufacturing features
may require different manufacturing methods. For the automated extraction of
manufacturing information, the task will become easier if we have instances of afeature class in the feature library refer only to same possible manufacturingmethods.
In this paper, for the development of the feature library, we propose the creationof ontology of manufacturing features by considering the designer’s intention
described in the functional data of the face elements that construct the features. The
goal of this ontology creation is to make the feature library be useful for the
automated extraction of proper manufacturing information to create the
manufacturing features that are extracted by the Extended SRG Method.
The structure of this paper is as follows. In order to make this paper self-content,
the Extended SRG Method is described briefly in Section 2. In Section 3, we discuss
the creation of ontology of manufacturing features for the feature library. In Section
4, a case study is used to show the validity of the proposed manufacturing features
ontology to enable feature library to extract proper manufacturing information fromthe extracted manufacturing features.

2. EXTENDED SUPER RELATION GRAPH METHODIn Extended SRG Method, feature extraction is made possible by using three
relations between faces, super-concavity relation, face-to-face relation and convexity
relation, and also by using the edge elements which construct the features. Superconcavity relation, face-to-face relation and convexity relation can be defined by
Eq.1, Eq.2 and Eq.3 respectively.
n f+ .n f+ ≠ -1; f i ∩S(f j ) + ≠ Ø and f j ∩S(f i ) + ≠ Ø
i

j

(1)

H.Muljadi, K.Ando, H.Takeda and M.Kanamaru
Considering Designer’s Intention for the Development of
Feature Library of a Process Planning System3

n f+ .n f+ = -1; f i ⊂ S(f j ) + and f j ⊂S(f i ) +ijn f+ ・n f+ ≠ 1; n f+ ・n f+ ≠ -1; f i ∩S(f j ) |+| =Ø; f j ∩S(f i ) |+| =Ø; E fi ∩E f j ≠ Ø
i

j

i

j

(2)
(3)

where n f+ is the positive face normal of face f i (Figure 2(a)), and the strict positiveiT

half space of face f i , S(f i ) |+| = { x | n f+ x > k } is the positive half space whichiTexclude the embedding plane of face f i , P(f i ) = { x | n f+ x = k } (Figure 2(b),(c)),
i

E fi is the set of edges of face f i . n f+ , S(f j ) + and E f j are defined similarly as
jabove.
fi(a)Positive Face Normal of Face f i
P(f i )(b)The Embedding Plane of f i
S(f i )(c)Strict Positive Half Space of f i
Figure 2: Explanation of terms used in Extended SRG Method
Figure 3 shows the Extended SRG representation of a stepped-hole feature. A nodewith one circle in the Extended SRG corresponds to a plain face of the feature. A
double circle node corresponds to a curve face. Dotted links are used to represent
face-to-face relations. Solid links are used to represent super-concavity relations andface-to-face relations. To distinguish these two relations, 0 is used as the attribute of
the solid links to represent super-concavity relations and 1 to represent convexity
relations. Solid links with no attribute are used to represent the face-edge relations.
Plain edges are represented by e n and curve edges are represented by e +n . The
Extended SRG Method has the ability to extract not only single depression features,
but also protrusion and compound features, since protrusion and compound features
can have their Extended SRG representations too.
In the development of feature library, a collection of manufacturing feature types,
their corresponding Extended SRG representations and the possible manufacturing
information to create the manufacturing features is stored. In the next section, we
discuss the creation of manufacturing features ontology to make the feature library
be useful for the extraction of proper manufacturing information of manufacturingfeatures extracted by Extended SRG Method.

DIISM 2004

4+

e3 e+1 e+7

+

e5

e2

e4
2 0

1
2

e10

3
+

e6

4

5

+

+

+

+

5
+

+

+

e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12

+

e11
e8

3 1

0

1
+

1 4

+

e12

+

e9

Figure 3: Stepped-hole feature and its Extended SRG representation

3. FEATURE LIBRARY
3.1 Feature Library for the Generation of Process Plans

A manufacturing feature can be defined simply as a geometric shape that has its
manufacturing information to create the shape. This definition means that when amanufacturing feature is extracted from the product design information, the possible
manufacturing information to create the shape is also extractable at the same time
[Kanamaru, M. et al, 2004]. The linkage between manufacturing features and theircorresponding possible manufacturing information is stored in the database, which
we call as a feature library. Thus, a feature library plays a big role for the generationof process plans based on the recognized manufacturing features. As manufacturingfeatures are extracted, the manufacturing information can also be extracted from thefeature library.
3.2 Representation of Designer’s Intention

Manufacturing features are extracted from the product design information. However,in order to extract proper manufacturing information to create the manufacturingfeatures, we need to recognize the functions of the manufacturing features. To do so,
we have to know why the designer designs the geometrical shapes, or in other words,
we have to understand the designer’s intention. So, we can say that understanding
the designer’s intention is very important to extract the proper manufacturing
information to create the manufacturing features. However, since normally designer
does not design a part using manufacturing features, we suppose that it is better tounderstand the designer’s intention by considering the functions of the face elementsthat construct the manufacturing features.
The functional data of face elements can be described as basic function,mechanism utilized for realization of the basic function, and condition and directionof the motion. The detail explanation of the functional data of face elements is given
in another report [Ando, K. et al, 1989]. Table 1 shows the contents of functional

H.Muljadi, K.Ando, H.Takeda and M.Kanamaru
Considering Designer’s Intention for the Development of
Feature Library of a Process Planning System

5

properties of face elements that are used for the creation of manufacturing features
ontology. The scope of the functional properties of face elements shown in Table 1
is limited to machined products.
Table 1- Contents of Functional Properties
Basic Function

Mechanism
utilized
forrealization of the basic function

Transmissionof motion

1: friction-mech.
2: gear-mech.
3: link-mech.
4: cam-mech.

Constraintof motion

1: rigidity-mech.
2: ball-bearing-mech.
3: sliding-mech.

Fixationof motion

1: bolt-and-nut
2: bolt-only
3: friction-mech.
4: bearing-fit
5: key-fit
6: river-fit
7: shrinkage-fit

Condition and direction of the
motion1: liner
2: smooth-liner
3: very-smooth-liner
4: round
5: smooth round
6: very smooth round
1: liner
2: weak-radial
3: strong-radial
4: weak-thrust
5: strong-thrust

1: stationary-object
2: revolutionary-object

3.3 Creation of Manufacturing Features Ontology

Figure 4 shows the manufacturing features ontology and the functional data
ontology. New classes for manufacturing features ontology are created to have theirrelation with the functional data ontology. The relation between the class of the
manufacturing features ontology and the functional data ontology represents how the
manufacturing features should be manufactured to fulfill the required functions.
Each new class of the manufacturing features ontology will refer to a collection of
possible manufacturing information that can be used to create the shape of theinstances of the feature class.
In Figure 4, a “precise drilled thru hole” class is created to relate the thru holefeature type in the manufacturing features ontology with the “transmission by
friction in liner motion” class of the functional data ontology. This is done since the“precise drilled thru hole” can fulfill the required mentioned functions. And for the“precise drilled thru hole” feature class, a collection of possible manufacturing
information for the instances of the “precise drilled thru hole” feature class should
be prepared so that when a manufacturing feature extracted by the Extended SRG
Method falls to this class to fulfill the required functional data as intended by thedesigner, a proper manufacturing information can be extracted automaticallyThus, by creating the manufacturing features ontology, we can collect andmanage the knowledge of process planners to create manufacturing features, andalso that the manufacturing features ontology will make the feature library be usefulDIISM 2004

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for the extraction of proper manufacturing information that can lead to thegeneration of process plans of a part.
Manufacturing Features

Step

Thru Hole

Milled Thru Hole

Stepped-HoleFeature Classes thatcan be represented by
the Extended SRG

Drilled Thru HoleThreaded DrilledThru Hole

Precise Drilled Thru Hole
Functional
Data
Ontology

Transmission by Friction
with Liner Motion
Transmission realized by
Friction Mechanism

Transmissionof Motion

Constraint
realized by
Rigidity
Mechanism
Constraintof Motion

Fixed
with
Bolts
and Nuts
Fixation of
MotionFunctional Data
Figure 4 Ontology of manufacturing features and functional data

4. CASE STUDY
Using a sample part shown in Figure 5, we confirm the effectiveness of the proposed
manufacturing features ontology to allow the extraction of manufacturing
information from the feature library. Figure 5 shows a sample part with the required
functions of the face elements of the part.
First, Extended SRG Method is applied to extract manufacturing features fromthe sample part. As illustrated in Figure 6, there are 5 thru hole features extracted.
Then using the functional data shown in Figure 5 as the input, the extracted featuresfind the matched feature class from the feature library. As illustrated in Figure 7, one
thru hole feature falls to the “grinded thru hole” feature class, and four thru holefeatures fall to the “threaded drill thru hole” feature class. Then, manufacturing
information for each thru hole features are extracted by referring the instances of thefeature classes. The thru hole feature that falls to the “grinded thru hole” featureclass extracts a manufacturing method where cylindrical grinder is required toH.Muljadi, K.Ando, H.Takeda and M.Kanamaru
Considering Designer’s Intention for the Development of
Feature Library of a Process Planning System

7manufacture the shape. The four thru hole features that fall to the “threaded drill thruhole” feature class extract a manufacturing method where threading is required to
manufacture the shape. Thus it shows that the manufacturing features ontology iseffective to make the feature library be useful for the automated extraction of proper
manufacturing information to create the manufacturing features that are extracted by
the Extended SRG Method.
Basic Function: Fixation
Mechanism: bolt
Motion: Stationary-Object

Basic Function: Fixation
Mechanism: bolt
Motion: Stationary-Object

Basic Function: Constraint
Mechanism: rigidity
Motion: weak radial
Basic Function: Fixation
Mechanism: bolt
Motion: Stationary-Object

Basic Function: Fixation
Mechanism: bolt
Motion: Stationary-Object

Figure 5: A sample part and the functions of the face elements
Thru Hole Feature

Thru Hole Feature

Thru Hole Feature

Thru Hole Feature

Thru Hole Feature

Figure 6: Extracted Manufacturing Features
Thru Hole Feature
Feature Class: “Threaded Drill
Thru Hole”

Thru Hole Feature
Feature Class: “Threaded Drill
Thru Hole”

Thru Hole Feature
Feature Class: “Grinded
Thru Hole”
Thru Hole Feature
Feature Class: “Threaded Drill
Thru Hole”

Thru Hole Feature
Feature Class: “Threaded Drill
Thru Hole”

Figure 7: Extracted Manufacturing Features and Their Proper Feature Classes

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DIISM 2004

5. CONCLUSION
In this paper, we presented the creation of ontology of manufacturing features
for the development of a feature library by considering the designer’s intention
described in the functional data of the feature constructing face elements. New
classes for manufacturing features ontology are created to have their relation withthe functional data ontology. Each new class of the manufacturing features ontology
will refer to a collection of possible manufacturing information that can be used tocreate the shape of the instances of the feature class. As shown in the case study, thecreation of manufacturing features ontology will make the feature library be usefulfor the automated extraction of proper manufacturing information for the generationof process plans.
For the automated generation of process plans, further works need to be done on
how the extracted manufacturing information to create manufacturing features canbe used for lower stream of process planning activities, such as setup generation,
process sequencing etc.

6. REFERENCES
Ando, K, Yoshikawa, H (1989) Generation of Manufacturing Information in Intelligent CAD. In Ann. of
the CIRP, 38(1). pp133-136
Kanamaru, M, Ando, K, Muljadi, H, Ogawa, M (2004) Manufacturing Feature Library for the Machining
Process Planning. In Proceedings of the JSPE Autumn Conference. CD-ROM Paper C74 (inJapanese)
Kao, C.Y, Kumara, S.R.T, Kasturi, R (1995) Extraction of 3D Object Features from CAD Boundary
Representation Using the Super Relation Graph Method. In IEEE Trans. on Pattern Analysis andMachine Intelligence, 17(2), December. pp1228-1233
Muljadi, H, Ando, K, Ogawa, M (2002) Creation of Feature Sets for Developing Integrated Process
Planning System. In Proceedings of the International Conference on DIISM 2002. pp59-66
Muljadi, H, Ando, K, Ogawa, M (2003) Practical Application of Manufacturing Feature Recognition
Method. In Proceedings of the International Conference on Leading Edge Manufacturing on 21stCentury. pp813-818
Sakurai, T, Muljadi, H, Ando, K, Ogawa, M (2000) Research of Dynamic, Flexible and Integrated
Process Planning System. In Proceedings of the JSPE Spring Conference. pp44 (in Japanese)