ComputerAided_Design_Engineering_amp_Manufactur.pdf

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adjusted and changed automatically by robotic devices without human intervention. The requirements for locating and constraining workpieces using such fixture modules are presented. Furthermore, the fundamental issues for planning and analysis of fixture setup are described. A technique for computeraided planning and analysis of reconfigurable fixture is also presented in this chapter. 3.1 Introduction Workpiece positioning and constraining, commonly referred to as workholding or fixturing, is an important issue in manufacturing processes. 1 In general, for any manufacturing operation to be successful, the workpiece must be located and held to remain in a desired position and orientation when subjected to external forces during manufacturing operations. Jigs and fixtures are the devices employed to position and constrain the workpiece for presentation to the manufacturing device. The traditional approach to fixturing involves designing and manufacturing special-purpose fixtures for specific workpieces and manufacturing operations. 2 In addition to this expensive process, a significant amount of intervention by skilled operators is needed in setting up and changing the fixtures when the manufacturing operation is completed or modified. Therefore the traditional approach is generally time consuming and thus costly. This is especially true for current and future manufacturing requirements characterized by rapid change of product design and reduced production cycle time. To reduce the dependence on dedicated fixtures, recent research efforts have been directed toward developing alternative approaches. Several flexible fixturing strategies have been proposed and developed. 3 These strategies include sensory-based techniques, modular fixturing, reconfigurable fixturing, conformable cl<strong>amp</strong>s, and many others. It is believed that reconfigurable fixturing is one of the most appropriate strategies in a computer-integrated manufacturing (CIM) environment. However, for any fixturing technique to be effective, it must be supported by a computer-based planning and analysis system. This chapter will briefly describe the economic justifications for flexible fixturing within a computer-integrated manufacturing (CIM) environment, and, more importantly, within flexible manufacturing systems. Flexible fixturing strategies will be briefly described and fixture design requirements will be presented. However, the main focus will be on requirements, guidelines, structure, and formulation for the CADbased planning and analysis of fixture configuration. These will be described in detail. 3.2 Economic Justification A great deal of attention has been directed towards the development of advanced manufacturing systems. <strong>Manufactur</strong>ing systems can be classified into two distinct categories: high volume and lowto medium-volume production systems. It is quite apparent that investments in manufacturing technology are more easily justified in high-volume production environments than in low- to mediumvolume environments. High-volume production systems typically produce a large number of similar parts with similar processes. Therefore, factors justifying such investments in automated manufacturing technology include: • Large production batches of same parts with similar processes • Very few design changes over a long period of time • Reduction of labor costs for support functions (such as materials handling and part positioning and constraining) by the deployment of dedicated and hard-tool fixturing • Simplified scheduling in conjunction with less need for program development due to the repetitive nature of tasks and large batches. On the other hand, low- to medium-volume production systems produce a larger number of diverse parts in much smaller batch sizes. 4 Therefore, in low- to medium-volume production systems, factors that prevent investments in automated manufacturing technology are basically the reverse of those in the high-volume production systems: © 2001 by CRC Press LLC
FIGURE 3.1 Trends in flexible manufacturing environment. (Reprinted from Reference 5, with permission from Elsevier Science.) • Small production batches of diverse parts with dissimilar processes • Frequent design changes • Little reduction of labor costs for support functions, such as materials handling and part presentation using traditional or dedicated fixtures • Sophisticated scheduling and control functions, together with a greater need for computer-aided program development to accommodate the variety. Therefore, as products become more and more customer-oriented, smaller batch production volume will be required and faster changes between component manufacture becomes an important issue. The environmental conditions of production systems characterized by increasing changes over time leading to the increase of variants per product as shown in Figure 3.1. 5 Some of the major design and development challenges presented by low- to medium-volume production systems include: 1. Flexible fixturing and tooling concepts 2. Automation of the fixturing process and tool delivery 3. CAD-based planning, analysis, automated program development 4. Development of flexible information systems. Having considered the practical and economical aspects and design challenges of low- to mediumvolume production systems, one can define a flexible manufacturing system as a production unit capable of manufacturing a family of products with a minimum amount of manual intervention. Such a system would consist of work stations equipped with multi-axis robotic systems to perform manufacturing operations. Furthermore, it would operate as an integrated system under full programmable control. The traditional approach to workholding is costly due to the long lead time and effort required to design and manufacture dedicated fixtures. In addition, these fixtures require manual setup and change of fixtures when a manufacturing operation is completed or modified. There is also an overhead cost associated with storing and retrieving a multiplicity of fixtures. Thus, the traditional approach is not suited to a modern and flexible manufacturing environment where production volume is low and product variation is high. If flexible manufacturing systems are to be truly flexible, therefore, the fixturing systems must also be automated and flexible. In order to replace or reduce the requirements for dedicated fixtures, recent research efforts have been directed towards developing alternative approaches to traditional fixturing methods. 3.3 Flexible Workholding Strategies Flexible fixturing involves employing a single fixturing system to hold workpieces of various shapes and sizes within a family of manufacturing operations. The implementation of such systems is strongly dependent upon development of advanced fixturing techniques, together with integration of automation/robotics © 2001 by CRC Press LLC
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COMPUTER-AIDED DESIGN, ENGINEERING,
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Library of Congress Cataloging-in-P
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Editor Cornelius T. Leondes, B.S.,
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Chapter 1 Chapter 2 Chapter 3 Chapt
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the implementation of the IPD syste
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In our IPD system implementations,
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2. Specification of analysis-specif
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FIGURE 1.2 base or the design insta
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FIGURE 1.4 System architecture of t
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of a beam is considered for FEA. Th
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FIGURE 1.7 through the control expe
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2. machining facility (e.g., gang m
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from the FBDS. The user also specif
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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
Page 73 and 74: © 2001 by CRC Press LLC TABLE 2.5
Page 75 and 76: TABLE 2.7 Partial Listing of Dimens
Page 77 and 78: 24. Makinouchi, S., Okamoto, M., an
Page 79: Bijan Shirinzadeh Monash University
Page 83 and 84: FIGURE 3.3 and constrain different
Page 85 and 86: Other Fixturing Techniques There ar
Page 87 and 88: FIGURE 3.6 contact point. The geome
Page 89 and 90: FIGURE 3.8 The vertical support fix
Page 91 and 92: and moments about the contact point
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Page 95 and 96: one of choosing the variables: such
Page 97 and 98: Fixture Module Location An importan
Page 99 and 100: FIGURE 3.15 Illustration of functio
Page 101 and 102: FIGURE 3.18 Minimum separation test
Page 103 and 104: direction) the face, respectively.
Page 105 and 106: FIGURE 3.21 Software structure for
Page 107 and 108: 3.8 Conclusion A reconfigurable fix
Page 109 and 110: Heui Jae Pahk Seoul National Univer
Page 111 and 112: FIGURE 4.1(b) Conceptual framework
Page 113 and 114: 4.3 Measurement Points Sampling and
Page 115 and 116: Surface S2 Measurement Path FIGURE
Page 117 and 118: FIGURE 4.4 Rough phase alignment ba
Page 119 and 120: deviation between the nominal CAD d
Page 121 and 122: FIGURE 4.6(a) Sum of squares distan
Page 123 and 124: FIGURE 4.7(c) Trailing edge. (c) th
Page 125 and 126: FIGURE 4.8(a) Profile tolerance of
Page 127 and 128: The calculated minimum form error i
Page 129 and 130: 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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