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Balaji Bharadwaj Visteon Corporation Utpal Roy � Syracuse University Abstract © 2001 by CRC Press LLC 1 Intelligent Computer- Aided Design (ICAD) Techniques for Design Automation 1.1 Introduction 1.2 Elements of IPD Systems: ICAD Techniques Expert System/Knowledge Base • Design Intent • Feature- and Function-Based Modeling • Information Management 1.3 Application Domain: Design Analysis Role of ICAD Techniques in Design Analysis • System Architecture and Case Study 1.4 Application Domain: Process Planning ICAD Techniques in Process Planning • System Architecture and Case Study 1.5 Application Domain: Allocation of Tolerance Specifications ICAD Techniques in Tolerance Synthesis • Schema for Tolerance Synthesis within the TSS System • Implementation and Case Study 1.6 Discussion and Conclusions Conventional Computer-Aided Design (CAD) systems have dramatically reduced the workload of the human designer and reduced the duration of the product design cycle. However, the magnitude of effort and volume of information required to use these tools limit their application in various product design activities. Intelligent Computer-Aided Design (ICAD) techniques have been sought to assist in the development of Integrated Product Design (IPD) systems, thus providing a more ‘‘complete’’ design tool to assist the designer in all phases of the product design. In this chapter, the role of ICAD techniques in various types of product design and development activities will be examined. The viability and utility of applying ICAD techniques for design analysis, process planning, and tolerance assignment tasks within the framework of an IPD system environment are described and demonstrated through the implementation of various prototype design systems. The objective behind � Author to whom all correspondence should be addressed.
the implementation of the IPD system is an integrated approach, wherein all the primary components, such as the CAD tools, numerical analysis tools, data bases, knowledge bases, and expert systems, work cooperatively in order to perform various tasks associated with that design task. The techniques described in this chapter are relatively easy to implement, and are well suited to industrial needs. Keywords: Integrated Product Design, Intelligent Computer-Aided Design Techniques, Automated Design System, Automated Process Planning 1.1 Introduction Advances in product design methods coupled with the growing trend toward shorter product development cycles have resulted in an increased emphasis on the following issues: (a) reduction in product design lead time and costs, (b) improvement in product quality, (c) design automation, and (d) integration between product design and manufacturing tasks. The above objectives can only be realized through the synergic development of the following key product design-enabling methodologies (Figure 1.1): (a) development of product design methodologies, (b) techniques for synthesis of product specifications, (c) advanced information support infrastructure for management and distribution of product design information, (d) decision support mechanisms for various tasks associated with product design, and (e) development of frameworks for product design. The above five enabling methodologies represent a multitude of tasks and activities with far reaching implications on current product design practices. Their implementation within the framework of an Integrated Product Design (IPD) system is the subject for various current research initiatives (discussed further in Reference 1). A careful examination of these five enabling methodologies reveals the following characteristic features that should be considered during the development of an IPD system: • Suitable mechanisms for incorporation of different product design perspectives for the generation of product design information in a timely and useful manner • Provision for suitable interpretations of the product design information for the different product design tasks • Mechanisms for the simulation of the performance of the product from different product design perspectives • Suitable algorithms and procedures for performing design in the presence of incomplete and uncertain information about product design attributes • Provision for sharing of information among different product design tasks (e.g., process, assembly, and inspection planning) FIGURE 1.1 Enabling product design methodologies for IPD. © 2001 by CRC Press LLC
Page 1 and 2: COMPUTER-AIDED DESIGN, ENGINEERING,
Page 3 and 4: Library of Congress Cataloging-in-P
Page 5 and 6: Editor Cornelius T. Leondes, B.S.,
Page 7: Chapter 1 Chapter 2 Chapter 3 Chapt
Page 11 and 12: In our IPD system implementations,
Page 13 and 14: 2. Specification of analysis-specif
Page 15 and 16: FIGURE 1.2 base or the design insta
Page 17 and 18: FIGURE 1.4 System architecture of t
Page 19 and 20: of a beam is considered for FEA. Th
Page 21 and 22: FIGURE 1.7 through the control expe
Page 23 and 24: 2. machining facility (e.g., gang m
Page 25 and 26: from the FBDS. The user also specif
Page 27 and 28: Depending on the type of feature to
Page 29 and 30: TABLE 1.6 Tolerance synthesis is de
Page 31 and 32: FIGURE 1.12 Display of the NC tool
Page 33 and 34: component geometric entities and va
Page 35 and 36: FIGURE 1.14 The system architecture
Page 37 and 38: TABLE 1.10 User Input for the Toler
Page 39 and 40: TABLE 1.11 Tolerance Specifications
Page 41 and 42: 7. Z. Young and I. R. Groose. A rul
Page 43 and 44: FIGURE 2.1 Tool paths generated for
Page 45 and 46: FIGURE 2.2 sented by instances of f
Page 47 and 48: TABLE 2.1 A CAD-Generated Hole Conf
Page 49 and 50: FIGURE 2.6 mounted on the rotary ta
Page 51 and 52: FIGURE 2.8 fixture for the machinin
Page 53 and 54: FIGURE 2.10 or best-fit, or the cur
Page 55 and 56: FIGURE 2.11 Linear approximation of
Page 57 and 58: FIGURE 2.13 Circular approximation
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FIGURE 2.14(b) Circular approximati
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FIGURE 2.16 Types of biarcs. FIGURE
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FIGURE 2.18 Approximation of scanne
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FIGURE 2.20 A touch trigger probe c
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TABLE 2.4 Substitute Elements in CM
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FIGURE 2.21 CMM planning requiremen
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Product Model Representation for Co
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© 2001 by CRC Press LLC TABLE 2.5
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TABLE 2.7 Partial Listing of Dimens
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24. Makinouchi, S., Okamoto, M., an
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Bijan Shirinzadeh Monash University
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FIGURE 3.1 Trends in flexible manuf
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FIGURE 3.3 and constrain different
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Other Fixturing Techniques There ar
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FIGURE 3.6 contact point. The geome
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FIGURE 3.8 The vertical support fix
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and moments about the contact point
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een identified, the normals are use
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one of choosing the variables: such
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Fixture Module Location An importan
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FIGURE 3.15 Illustration of functio
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FIGURE 3.18 Minimum separation test
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direction) the face, respectively.
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FIGURE 3.21 Software structure for
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3.8 Conclusion A reconfigurable fix
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Heui Jae Pahk Seoul National Univer
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FIGURE 4.1(b) Conceptual framework
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4.3 Measurement Points Sampling and
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Surface S2 Measurement Path FIGURE
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FIGURE 4.4 Rough phase alignment ba
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deviation between the nominal CAD d
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FIGURE 4.6(a) Sum of squares distan
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FIGURE 4.7(c) Trailing edge. (c) th
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FIGURE 4.8(a) Profile tolerance of
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The calculated minimum form error i
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FIGURE 4.11(a) A typical mold havin
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FIGURE 4.11(d) Inspection results f
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FIGURE 4.12(c) Maximum deviation (p
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5.1 Introduction Developments in th
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process plan for a part (Figure 5.3
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FIGURE 5.5 leading to the developme
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the previously stored process plan
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FIGURE 5.7 Techniques of defining a
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Feature Recognition and Extraction
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in relation to another feature (e.g
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FIGURE 5.9 Framework for building a
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FIGURE 5.10 Process plan content.
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FIGURE 5.12 Schematic sketch of the
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Several techniques of defining a fe
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TABLE 5.1 Data Structure for Repres
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FIGURE 5.16 Classification of the t
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system to ensure that the part bein
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FIGURE 5.19 Graphical model of the
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TABLE 5.4 Data Structure for Repres
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FIGURE 5.20 Mapping between machini
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algorithms. (Some details of the pr
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FIGURE 5.24 An example rotational p
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FIGURE 5.26 Down_face-turn-up_face
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FIGURE 5.27 Procedure for machine t
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FIGURE 5.28 Determining the number
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DL is needed to be set only if the
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FIGURE 5.31 Operation sequencing co
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Cutting Tool Selection Selection of
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1. A tool is searched for in the da
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FIGURE 5.32 Inputs to optimization
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When these values are substituted,
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Usually, maximum and minimum speed
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FIGURE 5.33 Solution methodology.
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TABLE 5.6 Process Plan Internal Rep
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In a strict theoretical perspective
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18. Domazet, D. S. and Lu, S. C. Y,
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63. Prasad, A. V. S. R. K., Rao, P.
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CAD systems. The sample consists of
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learn to master the new system. An
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Although researchers appear to agre
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Link and Zmud28 found that organic
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Informal Training Informal CAD trai
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informal training programs, felt th
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6. C. A. Beatty, Tall Tales and Rea
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A.Y.C. Nee National University of S
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FIGURE 7.1 Planning, design, and ma
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A metal stamping can have the follo
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FIGURE 7.3 Strips used to notch out
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A Skeletal Approach for the Recogni
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These findings can be used to devel
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Semi-Direct Piloting In cases where
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a larger value, the die operations
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FIGURE 7.9 Symbolic relationship be
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FIGURE 7.11 The shape of the envelo
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TABLE 7.1 Schema for the Generation
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strong reasons to support a move to
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FIGURE 7.16 3-D CAD model of a part
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References Cheok, B.T. et al. (1994
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include the once forbidden generati
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A temporal matrix (T-Matrix) is pro
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FIGURE 8.1 A basic process. (From R
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FIGURE 8.4(a) Generate a new IG usi
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Note if the pair of nodes are both
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TABLE 8.1 Summary of the Rules Cond
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The correctness of these rules is e
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needs to show that after each full
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ule is violated, a warning should b
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Π1 Π3 Π4 FIGURE 8.8(a) After add
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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A ik � ‘U’ and is in a cycle
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FIGURE 8.12(a) Add [p2 t7 p7 t8 p4]
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FIGURE 8.12(c) Add a token to p8 vi
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FIGURE 8.12(e) Add [p8 t9 p14 t10 p
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FIGURE 8.13 An example of rule TT.0
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FIGURE 8.14 A GPN model of a machin
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FIGURE 8.15(b) The first exclusive
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FIGURE 8.15(d) The last exclusive T
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FIGURE 8.15(f) After entering the a
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FIGURE 8.15(h) Completion of Compon
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FIGURE 8.15(j) Two PP-generations:
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FIGURE 8.15(l) The last exclusive T
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t1 t5 p1 p5 t6 (a) t2 t3 t4 2 2 p2
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FIGURE 8.17 An example of partial f
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Theorem 10: If pi → pk in a synth
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Note that control transitions are o
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t j than the lower at , it indicate
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• Programming logic and VLSI arra
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7. Chao, D. Y. and D. T. Wang, Appl
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53. Villaroel, J. L., J. Martinez,
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q u : the numerator of the least ra
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geometric modeling, engineering ana
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In dimension driven or parametric d
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FIGURE 9.3 configuration of bodies
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FIGURE 9.6 FIGURE 9.7 Geometric Int
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FIGURE 9.8 Intersection of degrees
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will introduce a way to propagate p
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Completeness of Dimensions Complete
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FIGURE 9.12 3-D constraints. © 200
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The distance constraint from pt �
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therefore: Now merge the lists and
Page 329 and 330:
Quantity analysis methods have the
Page 331 and 332:
y a revolute joint. By selecting al
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