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  • 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 & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 4, April (2014), pp. 119-125 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
  • 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 122 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 123 (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 124 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. REFERENCES [1] A. Haque (2006), Manufacturing Feature Recognition of Parts Using DXF Files, Fourth International conference on Mechanical Engineering, Dhaka, Bangladesh, Vol.1, No1, pp 1- 15. [2] B. Downie (2012), Migrating from IGES to STEP: One to One Translation of IGES Drawing to STEP Drafting Data, Computers in Industry, Vol.41, No.3, pp 26-39. [3] Lee and A.Y.C. Nee (2013), An Approach to Identify Design and Manufacturing Features From a Data Exchanged Part Model, Computer Aided Design, Vol 5, No.1, pp. 56-69. [4] J. Rossignac (2008), A Road Map to Solid Modeling, IEEE Transactions on visualization and Computer graphics, Vol.2, No.3, pp. 2006. [5] D-B. Perng, and Z. Chen(2009), Automatic form Feature Recognition and 3D part Recognition from 2D CAD Data, Computer and Industrial Engineering, Vol.13, No.3, pp.67-79. [6] M. Gonzalez and J. Chen (2010), Development of An Automatic Part Feature Extraction and Classification System Taking CAD Data as Input, Computers in Industry, Vol.34, No.2, pp. 156-169. [7] G. Zhang et al. (2012), Measuring Information Integration Model for CADICMM, Chinese Journal of Manufacturing Engineering, Vol.23, No.3, pp. 45-67. [8] S. Mansouretal, et al.(2012),Automatic Generation of Part Programs foe Milling Sculptured Surfaces, Journal of Materials Processing Technology, Vol. 23, No.4, pp. 99-109. [9] X. Zhang et al.(2013), Constructive Solid Analysis: A Hierarchal, Geometry-Based Meshless Analysis Procedure for Integrated Design and Analysis, Computer Aided Design, Vol.5, No. 4, pp. 39-49. [10] H. Voelcker et al. (2008), Solid Modeling: A Historical Summary and Contemporary Assessment, IEEE Computer Graphics and Applications, Vol.3, No. 6, pp. 104-120. [11] O.W. Salomons et al. (2009), Review of Research in Feature Based Design, International Journal of Manufacturing Systems, Vol.5, No.4, pp. 122-130. [12] W. Michael et al. (2007), An Overview of Automatic Feature Recognition Techniques for Computer-Aided Process Planning, Computers in Industry, Vol. 3, No.4, pp. 45-69. [13] P.K. Wright et al.( 2012), Volumetric Feature Recognition For Machining Components With Freeform Surfaces, Computer Aided Design, Vol. 12, No. 4, pp.34-46. [14] R. Roy et al. (2011), Feature-based representational scheme of a solid modeler for providing dimension and tolerancing information, International Journal of Robotics &Computer- Integrated Manufacturing. Vol.4, No.6, pp.109-123.
  • 7. 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 125 [15] Kerry and Lee (2009), Automated process planning for parametric parts. International Journal of Production Research, Vo. 4, No. 1, pp. 122-140. [16] Sharma and Roy (2011), Implementation of STEP application protocol 224 in an automated manufacturing planning system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol 23, No.3, pp. 234-245. [17] S.Shee (2010), computer integrated manufacturing system for rotational parts. International Journal of Computer Integrated Manufacturing. Vo. 12, No.3, pp.345-366 [18] Shee and Linn (2008), Representation scheme for defining and operating from features. Computer Aided Design. Vol.13, No.3, pp. 234-244. [19] A. Stage (2010), Resource based flexible form manufacturing features through objective driven clustering. Computer Aided Design. Vol. 13, No.2, pp.123-139 [20] P. Staley (2007), Syntactic pattern recognition to extract feature information from a solid geometric database. International Journal of Mechanical Engineering. Vol 34, No. 2, pp. 144-155. [21] R. Tseng (2009), Recognizing of interacting rotational and prismatic machining features from 3D mill-turn parts. International Journal of Production Research. Vol. 12, No. 3, pp. 156-167. [22] Ajeesh S. S. and Indu M.S., “Feature Extraction Techniques on CBIR-A Review”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 467 - 474, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.