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FIGURE 3.22 Example of the graphics display screen. (Reprinted from Reference 37, with permission from Elsevier Science.) FIGURE 3.23 Example of a fixtured workpiece. planning file. This file will contain all the necessary information regarding the position and orientation, the height adjustments, and the number of fixture modules required to locate and constrain the workpiece. The robot program to build the fixture, perform the assembly operation, 11 and dismantle the fixture layout can now be generated automatically by a robot task planner based on the configuration data file. Figure 3.23 shows an example of workpiece fixtured using the approach described. © 2001 by CRC Press LLC
3.8 Conclusion A reconfigurable fixturing technique must provide for accurate planning, analysis, and automated fixture construction program. A CAD-based planning and analysis approach for reconfigurable fixturing in CIM and FMS has been described in this chapter. The approach employs a number of adjustable fixture modules to locate and hold workpieces of various shapes and sizes. The planning and selection of the fixture configuration are performed within a CAD environment. This is followed by the kinematic analysis and verification using the generalized screw pairs carried out by a dedicated software program. The interference detection is performed next. Finally, the post processing is carried out to generate a configuration file and robot program. These will consist of all the appropriate information to set up and change the fixture layout and to perform the assembly operations. Some of the advantages of employing such a system include: 1. Reduction of the lead time and effort associated with the design and manufacture 2. Reduction of the non-productive time between batches in a manufacturing plant 3. Reduction of the overhead cost associated with storing and retrieving a multiplicity of traditional fixtures 4. Full exploitation of robotic and automated assembly for low- to medium-volume production batches in FMSs and CIMSs. Acknowledgments This research is partially supported by an Australian Research Council (ARC) Small Grant, a Monash Research Fund (MRF) grant, and a Harold Armstrong Research Fund. The author wishes to thank his research assistants at Robotics & Mechatronics Research Laboratory, Department of Mechanical Engineering, Monash University. References 1. Thompson, B. S. Flexible fixturing—a current frontier in the evolution of flexible manufacturing cells. ASME Transactions No. 84–WA/Prod–16, pp. 1–7, 1984. 2. Gandhi, M. V., Thompson, B. S., and Maas, D. J., Adaptable fixture design: an analytical and experimental study of fluidized-bed fixturing. Journal of Mechanics, Transmissions, and Automation in Design, Vol. 108, 15–21, 1986. 3. Hazen, F. B. and Wright, P. K., Workholding automation: innovations in analysis, design, and planning. Manufacturing Review, Vol. 3, No. 3, 224–237, 1990. 4. Krauskopf, B., Fixtures for small batch production. Manufacturing Engineering, 41–43, 1984. 5. Shirinzadeh, B., Strategies for planning and implementation of flexible fixturing systems in a computer integrated manufacturing environment. Computers in Industry, Vol. 30, No. 3, 175–183, 1996. 6. Shirinzadeh, B., Flexible and automated workholding systems. Industrial Robot, Vol. 22, No. 2, 29–34, 1995. 7. Gandhi, M. V. and Thompson, B. S., Automated design of modular fixtures for flexible manufacturing systems. Journal of Manufacturing Systems, Vol. 5, No. 4, 243–252, 1986. 8. Grippo, P. M., Gandhi, B. S., and Thompson, B. S., The computer-Aided design of modular fixturing systems. The International Journal of Advanced Manufacturing Technology, Vol. 2, No. 2, 75–88, 1987. 9. Beerma, J. R. and Kals, H. J. J., FIXES, a system for automatic selection of set-ups and design of fixtures. Annals of the CIRP, Vol. 37, 443–446, 1988. 10. Markus, A., Ruttkay, Zs. and Vancza, J., Automating fixture design—from imitating practice to understanding principles. Computers in Industry, Vol. 14, 99–108, 1990. 11. Pham, D. T. and Lazaro, A., Autofix—an expert CAD system for jigs and fixtures. Journal of Machine Tools Manufacturing, Vol. 30, No. 3, 403–411, 1990. 12. Cabadaj, J., Theory of computer-Aided fixture design. Journal of Computers in Industry, Vol. 15, 141–147, 1990. 13. King, L. S. and Hutter, I., Theoretical approach for generating optimal fixturing locations for prismatic workparts in automated assembly. Journal of Manufacturing Systems, Vol. 12, No. 5, 409–416, 1993. © 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
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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
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FIGURE 2.2 sented by instances of f
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TABLE 2.1 A CAD-Generated Hole Conf
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FIGURE 2.6 mounted on the rotary ta
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FIGURE 2.8 fixture for the machinin
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FIGURE 2.10 or best-fit, or the cur
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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
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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: FIGURE 3.21 Software structure for
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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Page 149 and 150: FIGURE 5.9 Framework for building a
Page 151 and 152: FIGURE 5.10 Process plan content.
Page 153 and 154: FIGURE 5.12 Schematic sketch of the
Page 155 and 156: 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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