ComputerAided_Design_Engineering_amp_Manufactur.pdf

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• Provision for support functions and methodologies for design critiquing, recording of design rationale, and planning and execution of design changes • Facilitation of group interaction between different groups of designers and software systems used for product development activities • Facilitation of synchronization between different product design tasks • Mechanisms for resolving conflicts among competing multiple product design perspectives • Provision for infrastructural support for the integration of the different product design modules • Development of a suitably structured product model for every stage of the product design process • Algorithms for mapping functional requirements of the component into appropriate types and associated values of product specifications (e.g., tolerances, surface finishes, geometry specifications, etc.). Which of the above tasks an IPD system ought to do and how it should go about doing them have been the subject of continued debate. 2–4 As a first step toward realization of IPD systems, researchers have focused their attention on the use of Intelligent Computer-Aided <strong>Design</strong> (ICAD) techniques for the development of IPD systems. The domain of ICAD techniques among other things includes the organization, integration, information management and transfer, and control of knowledge bases, expert systems, and data bases within the framework of the IPD system. A conventional CAD system with drafting, solid modeling, and design analysis capabilities often forms the basis on which an IPD system is built. Knowledge-based expert systems and data bases are the other key elements of an IPD system. In the past, the role of ICAD techniques was limited to the task of enabling interoperability between different CAD systems and design analysis tasks. 5–8 However, the issue of an effective integration of all product design tasks has still not been solved even with the introduction of such knowledge engineering systems. The issues that need to be addressed are mainly from the standpoint of a generic framework for design data representation, effective integration and coordination of design tasks, information processing, retrieval of similar designs, and design modification. The objective of this chapter is to report on the application of ICAD techniques for the implementation of IPD systems for various tasks associated with product design, such as design analysis, process planning, and tolerance assignment. Several case studies are presented to demonstrate the salient features of the application of ICAD techniques for product design activities. 1.2 Elements of IPD Systems: ICAD Techniques Expert System/Knowledge Base As outlined in the previous section, one of the most useful functions of an IPD system is to embody the knowledge available for a given design task. Currently, this task is performed by a technical specialist who uses various high-level intelligent functions such as intuition, creativity, association, induction, recognition, and deduction, as well as low-level functions such as computation and information retrieval. Among the above list of functions, only a few low-level functions involving computation and search have been analyzed and algorithms have been devised for automatically performing these tasks. To facilitate higher levels of knowledge processing in an IPD system, the design artifact needs to be defined completely along with its full functional description. However, there are two obstacles in embedding such knowledge into an IPD system. First, the expert knowledge is characteristic of an individual or an organization based on the past experiences and design practices and is often difficult to encapsulate in appropriate design rules and guidelines; furthermore, this knowledge is extremely context sensitive and cannot be generalized. Additionally, when several such sources of knowledge are available within an organization, there is no algorithmic procedure or guideline to resolve the conflicting suggestions. In spite of these difficulties, expert system shells, together with knowledge bases, can be used for various types of inferencing tasks such as exploration of the space of all design solutions, generation of design alternatives, etc. © 2001 by CRC Press LLC
In our IPD system implementations, we have provided various mechanisms through which the designer can specify a complete description of design requirements for the object to be designed. Various design primitives, which are generalizations of past designs, are provided in order to assist the designer in the description of the design artifact. Each design primitive is represented as a frame consisting of a set of attributes that denotes various design parameters (geometry, physical, and material properties), design specifications, and requirements. These design primitives can be built using the various geometric and information primitives provided by the Concept Modeller system26 and can be made available for subsequent designs. <strong>Design</strong> Intent Capturing the design intent during the design process is an uphill task, although it provides an important insight into how the design evolves. This is because design intent is not quantifiable, and it refers to the actions and their rationale used in arriving at the final design. However, it is possible to capture the design intent of the designer by recording the reasons and the conditions that prompt a change in the original design. This can be facilitated in an IPD system, since a deviation from the suggested design can be recorded along with the accompanying explanations. This information is useful in developing new alternatives, should a similar situation arise in the future. This concept of knowledge induction and reuse is similar to the case based reasoning (CBR) approach, 9 although IPD differs from CBR because CBR requires the indexing of a large number of designs in the design knowledge base, and the stored designs can only be used for a product design very similar to the present design. To record the design intent in the first place, one needs to carefully develop a faithful representation of all necessary design attributes and functions that are present in the design object. Moreover, object representation in a design synthesis stage is a dynamic process and involves the following steps: (a) build a tentative model, (b) evaluate it and compare the results with the design requirements, and (c) modify the model and then go back to step (b) to see if the model fails to meet the requirements, or to terminate the process otherwise. The representation scheme must be such that this dynamic procedure, based on the defined model, is performed conveniently. In our IPD system implementations, the above issue is supported through the development of: (a) a geometric information representation scheme to represent the shape and topology of the object; (b) a descriptive information representation scheme to represent design attributes, object behaviors, and functions of the design object; and (c) an information management representation scheme to store information pertinent to the management of product data within the IPD system and the coordination of various design subtasks. The object-oriented programming environment of the Concept Modeller CAD system, along with its knowledge processing capability, offers the necessary information representation infrastructure to support each one of the above representation schemes. Feature- and Function-Based Modeling A large number of design artifacts can be regarded as a combination of a base part and various types of features. Features have been traditionally used by designers to correlate the design intent of the design object with other manufacturing functions and tasks. However, this concept has only recently been implemented and advocated for use within CAD systems. Feature-based design systems allow the design object to be defined in terms of a set of attributes or features and not merely as a desired geometric shape. The feature-based design process provides a useful method for assessing the design intent. Since features are primarily classified based on their functionality, the choice of one feature over another clearly identifies the functional intent as desired by the designer. Moreover, parametric modeling of features would allow for the design object to be defined by a set of parameters or attributes that could be associated with either dimensional or functional information. The parametric-based feature modeling approach would allow for the incorporation of implicit and explicit relations between different design © 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 and 8: Chapter 1 Chapter 2 Chapter 3 Chapt
Page 9: the implementation of the IPD syste
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
Page 59 and 60: 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
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Quantity analysis methods have the
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y a revolute joint. By selecting al
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