1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME
119
AN INTELLIGENT FEATURE RECOGNITION METHODOLOGY STUDY
FOR 2.5 D PRISMATIC PARTS
Viswa Mohan Pedagopu1
, Dr. Manish Kumar2
1
Associate Professor,
Dept. of Mechanical Engineering, Shoolini University, HP, India
2
Associate Professor,
JNV University, Jodhpur, Rajasthan, India
ABSTRACT
The intelligent feature recognition methodology is the methodology which helps for
extraction of features for a given prismatic part by using feature based modeling system as an input.
Various researchers have come up with different ways and means to integrate CAD and CAM
technology. Automatic feature recognition from CAD solid systems highly impacts the level of
integration. CAD files contain detailed geometric information of a part, which are not suitable for
using in the downstream applications such as computer aided process planning approach. Different
CAD or geometric modeling approach store the information related to the design the prismatic part
in their own databases. Structures of these databases are different from each other. This paper
proposes an intelligent feature recognition methodology (IFRM) to develop a feature recognition
system for a prismatic part by using feature based modeling system as input method.
Key Words: Methodology, Prismatic, Feature, CAD and Process Planning.
1 INTRODUCTION
The developments of computer based geometric systems to aid in the description of object's
geometry, which is the main activity to design and manufacture of mechanical parts [1]. This
resulted research into the development of Computer Aided Design and Computer Aided
Manufacturing. Preliminary systems used electronic drafting and prismatic models to represent the
shape of three dimensional objects [4].
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 4, April (2014), pp. 119-125
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME
120
The development of the intelligent feature recognition methodology helps to developed new
mathematical models for representing solids and identified the relevant properties of an
informational complete representation [2]. This methodology identified the mathematical operations
that could be used to manipulate the prismatic models. This approach aims to achieve the integration
between CAD and CAM [8].
Figure 1. A CAPP is a bridge between CAD and CAM
Different CAD or geometric modeling approaches store the information related to the design
in their own databases. Structures of these databases are different from each other. The tools (IFRM)
methodology can be used or combined with other application specific tools to developed prismatic
part [3]. Intelligent Feature Recognition Methodology (IFRM) which has the ability to communicate
with the different CAD/CAM systems.
The prismatic part design is introduced through CAD software and it is represented as a solid
model by using CSG technique as a design tool. The solid model of the part design consists of small
and different solid primitives combined together to form the required part design [5].
The CAD software generates and provides the geometrical information of the part design in
the form of an ASCII file (IGES) that is used as standard format which provides the proposed
methodology the ability to communicate with the different CAD/CAM systems as structure of
proposed methodology shown in Fig. 2
CAD GEOMETRIC
DETAILS CAMCAMCAMCAM
CAPPCAPPCAPPCAPP
RECEIVES
DELIVERS

3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME
121
Figure 2: Shows the structure of proposed (IGRM) methodology [6]
The intelligent feature recognition methodology (IFRM) presented in this paper consists of
three main phases that the first phase converts a CAD data in IGESIB-rep format into a proposed
object oriented data structure [7]. The second phase classifies different part geometric features
obtained from the data file converter into different feature groups. The third phase maps the extracted
features to process planning's point of view.

4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME
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2. PHASES OF INTELLIGENT FEATURE RECOGNITION METHODOLOGY
The detail of phases of intelligent feature recognition methodology as below
(1) A Data file converter
The IGES is a standard file format for the data defining the object drawing in 3D CAD
systems in B-rep structure. The entry fields in ICES format consist of an object's geometric and
topological information. The geometric information includes the definition of lines, planes, circles
and other geometric entities for a given object [21]. The fundamental IGES entities, which are
related to representing a solid in B-rep structure, are discussed below
1. Line
A line in IGES file is defined by its end points. The coordinates of start point and terminate
point are included in parameter data section of this entity.
2. Circular Arc
To represent a circular arc in modeling space, IGES provides the information including a
new plane (Xn YT) in which the circular lies, the coordinates of center point, start point, and
terminate point. A new coordinate system (XT, YT, &) is defined by transferring the original
coordinate system (Xo, Yo, Zo) via a transformation matrix [20]
3. Direction
Direction entity is a non-zero vector in 3D that is defined by its three components with
respect to the coordinate axes. The normal vector of surface can be determined by this entity.
4. Plane surface
The plane surface is defined by a point on the plane and the normal direction to the surface.
5. Vertex
This entity is used to determine the vertex list which contains all the vertexes of the object.
6. Edge
This entity is used to determine the edge list which contains all the edges of the object.
7. Face
This entity is used to determine faces which consist of the object.
8. Shell
The shell is represented as a set of edge-connected, oriented used of faces. The normal of the
shell is in the same direction as the normal of the face.
9. Right Circular Cylindrical Surface
The right circular cylindrical surface is defined by a point on the axis of the cylinder, the
direction of the axis of the cylinder and a radius is entity is used to determine the loops which
involved in all facets of the object [11].

5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME
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(2) An object form feature classifier
In order to have a good generic representation of the designed object for CAM applications
especially for computer aided process planning, the overall designed object description and its
features need to be represented in a suitable structured database [17]. The step toward automatic
feature extraction will be achieved by extracting the geometric and topological information from the
(IGESBrep) CAD file and redefining it as a new object oriented data Structure. An object consists of
manufacturing features that can be classifies into form features which decomposed of either simple
or compound or intersecting features [19]. A simple feature is the result of two intersecting general
geometric surfaces while compound/intersecting feature is one that results from the interaction of
two or more simple features [10].
(3) A manufacturing features classifier
Features are further classified into concave or convex as attributes in the generic feature
class. Concave features consist of two or more concave faces, and convex features are decomposed
of either one or more convex faces [18].
3. ALGORITHMS FOR FEATURE EXTRACTION FOR A GIVEN 2.5 D PRISMATIC
PART BY USING FEATURE BASED MODELING SYSTEM AS INPUT
In general, the following steps are the proposed methodology for feature's extraction for any
given prismatic part by using feature based modeling system as input as below [16]:
Step 1: Extract the geometry and topology entities for the designed object model from IGES file:
Identify vertices, edges, faces, loops of the object.
Step 2: Extract topology entities in each basic surface and Identify its type:
(a) Identify the total number of loops in each surface.
(b) Identify the basic surface due to total number of loops.
(c) Classify the loops into different types (concave, convex, and hybrid) [15].
Step 3: Test the feature's existence in the basic surface based on loops.
Step 4: Identify feature type:
(a) Identify Exterior Form Features by searching for hybrid loop.
(b) Identify Interior Convex Form Features for convex loop
(c) Identify Interior Concave Form Features searching for concave loop [12].
Step 5: Identify the detailed features and extract the related feature geometry parameters:
(a) Identify feature's details (number of surfaces, surface type) [9].
(b) Identify the parameters of each feature (length (L), width (W), height (H), radius (R) [13].
(c) Identify the relative location of each feature due to the origin coordinates of the object.
Step 6: Identify the detailed machining information for each feature and the designed part:
(a) Identify the operation sequence of the designed part.
(b) Identify the operation type, the machine, and the cutting tool for each feature.
(c) Identify the tool approach in machining direction for each feature.
(d) Identify the removed machining volume for each feature [14]

6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME
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4. CONCLUSIONS
In this paper, a methodology for feature extraction for a given 2.5 D prismatic part by using
feature based modeling system as input is proposed and the implemented system is presented. This
approach aims to achieve CAD, CAM integration. Different CAD or geometric modeling packages
store the information related to the design in their own databases and the structures of these
databases are different from each other. As a result no common or standard structure has so far been
developed yet that can be used by all CAD packages. For this reason this proposed methodology will
develop a feature recognition algorithm which has the ability to communicate with the different
CAD, CAM systems. The CAD software generates and provides the geometrical information of the
part design in the form of an ASCII file (IGES) that is then used as standard format which provides
the proposed methodology.
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