RECOGNITION OF INTERACTING PRISMATIC FEATURES
FROM FEATURE BASED MODELS
YU ZHENG
A project report submitted in partial fulfillment of the requirements for the award of the degree of Master of Engineering (Mechanical Engineering)
Faculty of Mechanical Engineering University Technology Malaysia
ABSTRACT
vi
ABSTRAK
TABLE OF CONTENTS
CHAPTER TITLE PAGE
TITLE i
DECLATATIONS ii
ACKNOWLEDGMENT iv
ABSTRACT v
ABSTRAK vi
LIST OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xii
1 INTRODUCTION 1
1.1 Background 1
1.2 Statement of Problem 2
1.3 Objective 3
1.4 Scopes 3
1.5 Research Stage 4
1.6 Significance 5
2 LITERATURE REVIEW 6
2.1 Introduction 6
2.2 Basic Concepts of Feature 6
2.2.1 Form Feature Taxonomy 7
viii
2.4 Interacting Features 11
2.5 Feature Representation 12
2.6 Feature Modeling 13
2.6.1 Boundary Representation (B-rep) 15 2.6.2 Constructive Solid Geometry (CSG) 16
2.7 Feature Model Generation 17
2.7.1 Design by Features 17
2.7.2 Feature Recognition 19
2.7.3 Feature Recognition Issues for Interact
Machining Features 20
2.7.4 Methods for Feature Recognition 21 2.7.4.1 Graph-based Approach 22 2.7.4.2 Rule-based Approach 23 2.7.4.3 Syntactic Pattern Recognition 24 2.7.4.4 Convex Hull Approach 25 2.7.4.5 Cell Based Decomposition 26 2.7.4.6 Process Centred Approach 27 2.7.4.7 Neural-network-based
Approach 28
2.7.4.8 Hint-based Method 29
2.7.4.9 Hybrid Feature Recognition 30
2.7.4.10 External Approach 32
2.7.5 Integrating of the Two Feature
Generation Approaches 33
2.8 Summary 34
3 METHOLOGY 40
3.1 Introduction 40
3.2 Selected Features 40
3.3 Definition of Features 42
3.4 Concept and Element of Proposed Method 45
3.5 Proposed Method 47
3.5.1 Design Feature Verification 47 3.5.2 Machining Feature Verification and
Particular Extraction 49
3.6 Summary 51
4 SYSTEM VERIFICATION 52
4.1 Introduction 52
4.2 Access to SolidWorks Information Structure 52
4.3 SolidWorks API Object Model 53
4.4 Visual Basic for Application (VBA) 53 4.5 Implementation of the Proposed Method for
Feature Recognition 55
4.5.1 Solidworks Input 56
4.5.2 Preparation Stage 57
4.5.3 Recognition Stages 58
4.5.3.1 Get Stock Size by Function GetStockSize (swBody as
sldworks, body2) as Variant 59 4.5.3.2 Differentiate the Features by
Loops 63
4.5.3.3 Primary Differentiation 65 4.5.3.4 Secondary Differentiation 71
4.5.3.5 Post Process 71
4.5.3.6 Machining Feature Extraction 72
4.5.4 Data Output 74
4.5.5 Experimentation of the Program for
Feature Recognition 76
4.5.6 Feature Information Result of
Recognition 78
x
5 RESULT AND DISCUSSION 82
5.1 Introduction 82
5.2 Achievement of Objective 83
5.2.1 Number of Feature VS Recognition Time 83 5.2.2 Feature Data Output for Different
Purposes 86
5.3 Achievement of Objective 87
5.4 Program Software for the Proposed Method 89
5.5 Approaches of the Research 89
5.6 Comparison with Other Methods 90
5.7 Advantages and Limitation of the Proposed
Method 91
5.8 Recommendation of Future Work 92
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Classification of manufacturing features according to
different perspectives 7
2.2 Classification of feature interactions 12
2.3 Classification of feature recognition 20
2.4 Summary of various methodologies to recognize feature 34
3.1 Features and loops 42
5.1 Relationship between the number of feature and the
recognition time 84
5.2 Recognition time VS number of features in pattern
xii
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Cindy’s form-feature taxonomy 8
2.2 Design to manufacturing 10
2.3 Feature-based manufacturing knowledge repositories 10
2.4 Feature interaction 11
2.5 Feature modeling of a box 13
2.6 Basic types of solid modeling 15
2.7 Data structure for B-rep models 16
2.8 Block diagram for the design-by-feature approach 18
2.9 Feature model generation 18
2.10 Common approaches for feature recognition 21
3.1 Selected features 40
3.2 Combinations of selected features to study 41 3.3 Definitions of features in manufacturing 43
3.4 Boss-Pocket interacting feature 44
3.5 Feature recognition framework 46
3.6 Dual-level recognition process 47
3.7 Procedures to extract the selected features 48 3.8 Coordinate point for machining features 49 3.9 Coordinate point and parameters for steps 49 3.10 Coordinate point and parameters for slot 50
4.1 Solidworks API object model 54
4.2 Steps of experimentation 55
4.6 Flow chart for function of different features by loops 63 4.7 Flow chart for function of primary differentiation 65
4.8 Boss inside the pocket 66
4.9 Flow chart for examine the cavity property 67 4.10 Geometrical principles for examining the cavity property 68 4.11 Data extraction for the rectangular boss 70 4.12 Machining features for the boss feature 73
4.13 Boss-Pocket interacting feature 74
4.14 Format of the data output 75
4.15 Executing the program 77
4.16 Total time for recognition 77
4.17 Result of feature recognition for feature covered and their
combination 78
4.18 Result of feature recognition for feature covered 80 5.1 Relationship between the number of pocket and
recognition time 85
5.2 Feature data output 87
CHAPTER I
INTRODUCTION
1.1 Background
Manufacturing organizations are always keen to find the ways to produce
high-quality products at the lowest cost and shortest time. In order to achieve this
objective manufactures are looking for an alternative ways to the traditional
approaches to design, manufacturing, and management. Computer aided process
planning (CAPP) is considered to be an effective way to adapt to the flexible
manufacturing and reducing the planning time and increasing consistency and
efficiency in modern industry.
However, parts’ geometry information is stored in CAD system in the form of
low-level entities such as faces, vertices, edges and so forth rather than the actual
manufacturing information of holes, slots, pockets etc. which can be virtually used in
the computer aided process planning (CAPP). The main problem is that features are
not neutral formats to transfer the CAD data to computer-aided manufacturing (CAM)
system. On the other hand, there is a lack of absolute common or standard structure
of database which is used to store the information related to the design due to the
different types of CAD packages. An urgent solution should be found to overcome
the obstacles. This chapter consists of five parts: statement of problem, objective,
1.2 Statement of Problem
There are two approaches to create feature models are design by features and
feature recognition which are used to actualize the manufacturing process from the
CAD system.
Design by features, also known as feature-based design (FBD) enables the
feature structures to be achieved by certain operations or shaping which is related to
manufacturing information. In a typical feature-based design, the shapes that can be
used in design are limited to individual basic machining features. The designer may
be forced to consider the geometry from the machining point of view without using
functional considerations. (Yuan-jye Tseng, 1999) In addition the features used to
design the part do not necessarily represent the best way to manufacture it. (Regli,
1995)
Feature recognition is the mechanisms which are developed to extract the
high-level features from the CAD solid model to act as the interface between
computer aided design (CAD) and CAPP. Various methods for feature recognition
are introduced nowadays. These methods include pattern matching approach,
hint-based approach, volumetric decomposition approach and neural-network-hint-based
approach. A general consensus is reached that the recognition of interacting feature
is the most important and challenge issue in feature recognition.
Despite promising research, many problems in feature recognition remain
unsolved. Current research is looking at various hybrid methods combining basic
recognition algorithms with rules or constraints to improve the accuracy of
recognition. (Mantyla, Nau, and Shah, 1996) The initial idea of this thesis is to obtain
the prismatic feature information from the feature base models with interacting
3
This study will concentrated on the extraction of interacting prismatic
features rectangular boss from feature based modeling with the presence of blind
pocket and through pocket by feature recognition approach using Solidworks
software.
1.3 Objective
The purpose of this project is to develop a system based on the algorithm
structured by using VBA programming of Solidworks which is using Application
Programming Interface (API) to achieve the recognition of the interacting features
from the model which has been created on feature based modeling in the form of 3D.
1.4 Scopes
This project is conducted within the following:
i Involve the topology and geometrical data from solid modeling;
ii The algorithm that will be developed will recognize interacting
features using the feature definition of the model;
iii The system developed will use this algorithm;
iv Facilities used are: Solidworks for representation of solid models and
1.5 Research Stage
The study will proceed in the following five stages, the tasks of each stage are
listed below.
i. Literature Review
Survey the feature recognition development and methodology in CAD system
on previous and current research.
ii. Development of a Suitable Method
Survey the utilization of Application Programming Interface in developing
algorithm for feature recognition in order to develop a specific algorithm.
iii. Development of the Proposed Algorithm
Develop a specific algorithm for automatic feature recognition as listed in the
scope according to the chosen method.
iv. Conducting test and experimentations on the Algorithm and Verify the Results
v. Evaluation
Evaluate the compatibility and efficiency of the method to determine its
93
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