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ICAD Techniques in Process Planning Process Planning Data Bases and Knowledge Bases The ability of the process planning procedure to generate process plans is largely dependent on the availability and accessibility of the required information during decision making. Thus, it is essential to have an effective representation scheme of the process knowledge along with the knowledge about the capabilities and specifications of available machine tools, cutting tools, and other accessories. The data base component of the process knowledge in a generative computer-aided process planning (CAPP) system should consist of the following: 1. 2. 3. 4. 5. 6. Machine tool/machining center data base: This data base contains the specifications and technical information pertaining to the capabilities of the different machine tools. Selection of a feasible set of machines from this data base is dependent upon the choice of a particular feature to be machined. The information includes details such as machine identification number, machine name, type of machinable features, horsepower rating, efficiency, size, speeds and feeds of the machine spindle, orientations of the spindle with respect to its table, repeatability, surface roughness and the accuracy that the machine can produce for different machining processes. Cutting tool data base: The data base for cutting tools is divided into two parts: (a) cutting tools, and (b) cutting tool holders. Cutting tools are related to a set of manufacturing features that can be generated by cutting tools and their respective machining processes, whereas tool holders are only associated with the geometry of the cutting tools and the machine tools in which they are used. For the proper selection of cutting tools, information concerning their geometrical and technological parameters is analyzed. The data base for cutting tools contains information regarding tool identification number, tool name, cutting tool holders, tool material specification, tool geometry specification, cutting condition requirements, tool wear characteristics, accuracy, and type of manufacturing features that it can generate in a particular machine tool. Fixture data base: Clamping devices (fixtures) are characterized in terms of their “resting” and ‘‘clamping’’ points, which are objects of the fixture’s geometry. Each resting/clamping point is described by its function (position or fixing), principle of its action, and the nature of its contact with the workpiece. The fixture data base contains information regarding the fixture identification number, fixture type, their generic configurations along with their geometric specifications, and nature of clamping forces at the points of contact with the workpiece. The nature of contacts of the resting/clamping points determines the constraint relationships on the tool approach direction of the machine tool with respect to the fixture for a particular fixture-workpiece setup. Machinability data base: Proper determination of the machining conditions and the required machining parameters depends on the selection of cutting tool geometry and its material specification, along with the type of workpiece material to be machined. This data base contains information pertaining to optimum machining conditions for different workpiece materials when specific tool materials with certain tool geometries are used within certain ranges of specified speeds, feeds, and depths of cut. Material data base: This data base provides information regarding the characteristics of a particular type of material and its performance under a particular machining process. Cost data base: The cost data base provides input regarding the standard costs (tool setup, tool change, etc.) incurred at any stage of the manufacturing process. The process planning system also requires appropriate knowledge bases for performing the following two tasks: 1. Integration and sequencing of machining operations: A component of this knowledge base deals with the issue of determining groups of manufacturing features that can be machined by a single machining operation (in other words, can be ‘‘simultaneously’’ machined). These simultaneous operations can be achieved in three different ways: (a) by using multiple tools in a single spindle © 2001 by CRC Press LLC
2. machining facility (e.g., gang milling), (b) by using multiple spindle machine tools, or (c) by using more than one machine during the same operation time (e.g., automatic transfer line). The knowledge base consists of a set of constraining rules that identifies all those features that can be machined in one operation. The constraining rules depend on the feature accessibility criteria, machinability conditions, and fixturing considerations for machining different features. For simultaneous machining operations, rules are defined mainly to guarantee an interference-free operation. The main criterion to allow multiple features to be machined simultaneously is that all of the involved machining operations must be mutually exclusive, i.e., the condition of generation of one feature should not depend on the existence of another feature of the same group. Furthermore, knowledge regarding the constraints imposed by fixturing devices and clamping positions for simultaneous machining operations is also provided in this knowledge base. The second component of the knowledge base deals with sequencing of machining operations. Most manufacturing features require more than one manufacturing operation for obtaining the desired functional properties of the finished workpiece. Some precedence relations always exist among the machining operations, which are also stored in the knowledge base. These precedence relationships are established on the basis of the following criteria: (a) precedence requirements due to a unidirectional chain of process operations, (b) precedence relationships between machined surfaces due to surface quality and datum considerations, and (c) precedence relationships among part features depending on accessibility and clamping considerations. Generation of workpiece orientations for machining: Stable workpiece orientations are determined on the basis of the workpiece configuration and the set of machining operations that are to be performed on it. The orientation must comply with the standard fixturing rules and should allow interference-free, concurrent operations. This knowledge base consists of two components; one part corresponds to the determination of orientation and location of the workpiece with respect to the machine spindle, and the other determines the required resting and clamping points on the workpiece. This knowledge base should also be capable of dealing with the issues of fixture selection from a standard fixture library. All of the above data bases and knowledge bases have been incorporated in the BBPP system. Knowledge Representation and Reasoning Frames and rule-based knowledge representation schemes are considered to be the most appropriate means of storing the different data types pertaining to the knowledge and data bases (described earlier). Factual knowledge is best represented by frames while problem solving knowledge is best described through declarative statements in the form of production rules. A forward chaining inference strategy using the blackboard architecture concept for problem solving has been adopted for developing the process plan. The process plans are evolved through the interactions of different domain experts (in the process planning domain) who use the blackboard data base for developing different segments of the process plan. The blackboard approach to problem solving reflects the cooperative problem solving approach normally followed by process planners (shop floor personnel). The process plan starts with the raw stock and involves different machining processes to transform the raw stock into the finished part. The process planning system requires an explicit representation of the geometrical and technological information of the part in order to make different process planning decisions. Thus, a product data model (PDM) of the part is required at each stage during the formulation of the process plan. It uses a feature-based representation scheme for representing the part geometry. 17 Along with the geometrical and topological information of the part, it also provides the surface finish and variational information (tolerance information) for the process planning procedure. The PDM of the part has been specifically designed for this purpose and is composed of two segments. The first segment consists of specifications of the raw stock used for manufacturing the part while the second segment consists of the specifications of the finished workpiece model of the part. The finished workpiece model consists of a collection of specifications, which is used for characterizing the different form features of the part (Table 1.2). © 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 and 10: the implementation of the IPD syste
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: FIGURE 1.7 through the control expe
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
Page 61 and 62: FIGURE 2.16 Types of biarcs. FIGURE
Page 63 and 64: FIGURE 2.18 Approximation of scanne
Page 65 and 66: FIGURE 2.20 A touch trigger probe c
Page 67 and 68: TABLE 2.4 Substitute Elements in CM
Page 69 and 70: FIGURE 2.21 CMM planning requiremen
Page 71 and 72: 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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