ComputerAided_Design_Engineering_amp_Manufactur.pdf

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FIGURE 1.9 and to modify the BB data base after the completion of their tasks. The BB data base in each one of these phases is closely monitored by the control expert. At the end of each one of the five phases, the data present in the BB data base are written to an output file. These data constitute a segment of the process plan. The working of the BBPP system consists of the following steps: 1. The control expert initiates the process planner through the specifications of the PDM and the initialization of the phase 1 BB (Figure 1.9). 2. Next, the machining expert determines the set of all possible machining processes that can be used for generating the shape and form of each one of the features in the part. This selection is based on prior machining knowledge or can be obtained through geometric reasoning algorithms. © 2001 by CRC Press LLC TABLE 1.5 Template of the Blackboard Data Structure Blackboard Data Structure Feature Description ID Feature identification number <strong>Manufactur</strong>ing-process Set of all possible manufacturing processes that can be used for generating the feature <strong>Manufactur</strong>ing-setup List of setups that are required for machining the feature using different operations Machining-parameters Machinability parameters for machining operations Finishing-op-parameters Machinability parameters for finishing operations Hardness-operation Specifications of heat-treatment operations for imparting the required hardness in the part Process plan control flow.
Depending on the type of feature to be machined, the machine-tool, material, cost and machiningsetup experts are also activated. The activation of these experts is controlled through the control expert. 3. For each one of the machining processes, the machine-tool expert determines the number of setups and the setup times required for machining the feature on a machine tool capable of generating that feature; the material expert estimates the tool life of a standard cutting tool for machining the feature; the cost expert determines the production cost for machining the feature. 4. Next, the surface-finish expert estimates the amount of surface roughness zone that can be achieved by the machining process for a desired surface quality. This expert also establishes the machining cost required by the finishing process in order to achieve the desired surface roughness zone after the feature has been machined using a roughing operation. 5. The control expert then determines the machining index, which is a weighted sum of the inputs provided by the machine-tool, material, cost and surface-finish experts. Subsequent to this, the control expert selects the machining process with the best machining index. The setup expert determines the setup face to be used for machining the feature. 6. After establishing the machining processes for each one of the features, the control expert passes control from the phase 1 BB to the phase 2 BB. The setup expert sequences the setups so that the maximum number of features can be machined in one setup. The sequence-planning expert then studies the setup sequences and determines the sequence in which the different part features would be machined. The phase 3 BB is next activated by the control expert. 7. For each one of the machining processes to be used for machining the features, the surface-finish expert determines the amount of material to be removed in the individual machining processes. The different experts from the machining, machine-tools, cutting-tools, and machinability domains determine the machinability parameters using standard data handbooks and machining process models. On the basis of the data posted on the phase 3 BB data base, the control expert determines if the features require any additional finishing operations. In the even that the feature does not require any additional finishing operations, control is passed over directly to the phase 5 BB. On the other hand, if the feature requires additional finishing operations, then the phase 4 BB is initiated. 8. The surface-finish expert determines the finishing operations to be used for generating the desired surface roughness zone of the form feature. This selection is based on the capability of the finish machining processes. Next, the machinability parameters for the finishing operations are determined using standard data handbooks and machining process models in conjunction with the machinetools, cutting-tools, and machinability experts. These data items are posted on the phase 4 BB, and the control expert then activates the phase 5 BB. 9. Depending on the desired surface hardness of the part, the heat-treatment operations expert estimates the depth of hardening and the heat-treatment operations that can achieve it. 10. After the successful generation of the process plan, the NC tool path planning module is activated in order to generate the NC tool path to be adopted for machining the features on an NC machine tool. The NC tool path planning module requires the following input information: (a) feature geometry information as obtained from the FBDS system, and (b) feature processing information as obtained from the BBPP. The four stages involved in the generation of the NC tool path (Figure 1.10) are • Selection of appropriate machining approach (either contour or edge-based), depending on the type of feature to be machined • Generation of the NC path depending on the type of feature to be machined, geometric configuration of the feature base surface, and cutting-tool specifications for both contour and edgebased machining approaches • Generation of machining volumes that represent the volume of material to be removed from the raw stock for generating the specific form feature, depending on the NC path, machining criteria, and cutting-tool specifications © 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 and 22: FIGURE 1.7 through the control expe
Page 23 and 24: 2. machining facility (e.g., gang m
Page 25: from the FBDS. The user also specif
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
Page 73 and 74: © 2001 by CRC Press LLC TABLE 2.5
Page 75 and 76: 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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