ComputerAided_Design_Engineering_amp_Manufactur.pdf

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FIGURE 3.14 CAD analysis for a final fixture configuration. (From Reference 24. With permission.) with the workpiece. The left hand column shows the normalized values of the twist. The twist, or the scalar multiple of the twist, would break the contact with all the fixture module. Therefore, the virtual coefficients formed by this twist and wrenches applied by the fixture modules are greater than zero (i.e., repelling). Further, this also means that the wrench exerted by the robot manipulator, in placing the workpiece onto the initial fixture layout, is required to be opposite and thus contrary to the twist obtained for the initial fixture configuration. In fact, the components of the twist for the initial layout are used to determine the approach vector with which the robot moves to place the workpiece onto the fixture layout. Figure 3.14 shows the final fixture configuration with two horizontal and vertical clamps added to the initial fixture layout. 3.7 Interference Detection and Post Processing The next important phase in any computer-assisted process planning and programming is the interference detection and automated generation of the machine programs. A number of sophisticated robot programming systems have been developed in recent years. 30–32 These systems combine the capabilities of modeling, task description, manipulator-level programming, simulation, and collision avoidance. 33 In general, these systems rely on interactive user specification of the objects in the workcell, and path planning is performed manually. The collision detection is usually carried out during simulation. 34–35 Such simulation and programming packages include ROBCAD, Deneb’s Envision, CATIA, GRASP, and CIMstation. These generally utilize the “ideal” model parameters and robot controller algorithms to simulate the programmed motion. However, the ideal model generally differs from the actual model of the robot parameters. 36 Therefore, the programmed locations may require on-line correction using teachmode programming. In addition, computer-aided design (CAD) packages provide facilities to check for interference among the objects in a scene. However, when using CAD packages, each assembly must be modeled, and, depending on the details of the models, the interference detection would be time consuming and inefficient. Another shortcoming of such systems is their inability to take into account clearances as required in the physical world for any robotic assembly operation to succeed. Such clearances between fixture modules are necessary due to positioning errors which may be introduced by the robot manipulator. © 2001 by CRC Press LLC
Fixture Module Location An important objective of a reconfigurable fixturing system is to provide means for a rapidly validating fixture construction program. 37 The fixture construction program, consisting of the placement, adjustment, and retrieval operations, is too complex and must be generated automatically. The information required by the interference detection module consists of the locations of the reference frames of the fixture modules with respect to the assembly station coordinate frame. All tool center point (TCP) locations for robot movements are determined by translation and rotation transformations of the reference frame position on the fixture modules. The positioning and orienting of the fixture module are performed in four-dimensional space: three to position and one to orient. Therefore, only rotation about the z-axis is required to orient the fixture module on the fixture bed, in the X-Y plane. The following expression describes the nomenclature for the location description of fixture modules: © 2001 by CRC Press LLC HC Fij → Module TypePosition Frame Designation i� ( X, Y, Z�� , , , ) j Station (3.22) As an example, the location of reference frame for a horizontal clamp, retrieved from the planning and verification software modules, is provided to the interference detection software module and is specified by a six parameter field as below: HC Fij A � HC FXj , , , , , HC FYj HC FZj HC F�j HC F�j HC F�j (3.23) The first three parameters define the position (x, y, and z) and the last three parameters describe the orientation (roll �, pitch �, and yaw �) of the parent reference frame (F) of the fixture module. Furthermore, j is the number of fixture modules in any particular category (e.g., HC: horizontal clamp). It must be noted that the roll and pitch parameters are zero but included only to be consistent for offline programming purposes. Equation (3.24) describes the pose of the robot TCP during the placement of the horizontal clamp on the fixture bed. HC Pij A � HC PXj , HCPYj, �ZHCP, HCF�j, HCF�j, HC F�j (3.24) where �ZHCP denotes the height of the robot end-effector with respect to the fixture bed during the placement of the fixture module on the bed. The transformation from the reference datum on the fixture module to various critical locations at which the robot is required to interact with the fixture modules may be given as follows: [ P] � [ T] [ �r] (3.25) where P is the pose of the robot TCP, T is the transformation containing the position and orientation of the fixture module’s reference datum on the fixture bed, and �r is the appropriate distance from the functional locations on the fixture modules. Thus, the above formulation may be expressed as follows: cos� �sin� 0 X 1 0 0 �R sin� cos � 0 Y 0 1 0 0 0 0 1 Z 0 0 1 �Z 0 0 1 1 0 0 0 1 (3.26)
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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
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TABLE 1.6 Tolerance synthesis is de
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FIGURE 1.12 Display of the NC tool
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component geometric entities and va
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FIGURE 1.14 The system architecture
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TABLE 1.10 User Input for the Toler
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TABLE 1.11 Tolerance Specifications
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7. Z. Young and I. R. Groose. A rul
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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 and 80: Bijan Shirinzadeh Monash University
Page 81 and 82: FIGURE 3.1 Trends in flexible manuf
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
Page 93 and 94: een identified, the normals are use
Page 95: one of choosing the variables: such
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
Page 131 and 132: FIGURE 4.11(d) Inspection results f
Page 133 and 134: FIGURE 4.12(c) Maximum deviation (p
Page 135 and 136: 5.1 Introduction Developments in th
Page 137 and 138: process plan for a part (Figure 5.3
Page 139 and 140: FIGURE 5.5 leading to the developme
Page 141 and 142: the previously stored process plan
Page 143 and 144: FIGURE 5.7 Techniques of defining a
Page 145 and 146: 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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