ComputerAided_Design_Engineering_amp_Manufactur.pdf

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Further information pertaining to the inspection features has to be built up based on planning methodologies for setup planning, PCS selection, and surface data points extraction based on the number of sampling points and accessibility analysis of the probe. This example shows an approach of extracting information from a product model for computer-aided measurement and organizing this information into inspection features. The inspection features will form the basic building blocks which will help to facilitate the generation of the CMM part program. References 1. Chia, A. P. H., An Integrated CAD/CAM Approach for the Manufacture of Wind-Tunnel Models, B. Eng. Thesis, National University of Singapore, 1994. 2. Giam, K. Y., Manufacture of Wind-Tunnel Model Using CAD/CAM Technique, B.Eng. Thesis, National University of Singapore, 1993. 3. Tan, L. B., Manufacture of Models Employing Integrated CAD/CAM Techniques, B.Eng. Thesis, National University of Singapore, 1995. 4. Alting and Zhang, H. C., Computer-aided process planning: the state-of-the-art survey, International Journal of Production Research, Vol. 27, No. 4, 1989, 553–585. 5. Nolen, Computer-automated Process Planning for World-class Manufacturing, Marcel Dekker, Inc., NY, 1989, 65–70. 6. Eversheim W. and Schneewind, J., Computer-aided process planning—state of the art and future development, Robotics and Computer Integrated Manufacturing, Vol. 10, No. 1/2, 1993, 65–70. 7. Elmaraghy, H. A., Evolution and future perspectives of CAPP, Annals of the CIRP, Vol. 42, No. 2, 1993, 1–13. 8. Yeo, S. H., Wong, Y. S., and Rahman, M., Integrated knowledge-based machining system for rotational parts, International Journal of Production Research, Vol. 29, No. 7, 1991, 1325–1337. 9. Yeo, S. H., An integrated knowledge-based machining system for rotationally symmetric parts, Ph.D. Thesis, National University of Singapore, 1992. 10. Wong, Y. S. and Wang, Z., Automated process planning for CNC machining of spherical spaceframe nodes, Journal of Manufacturing Systems, Vol. 14, 1995, 369–377. 11. Eade, R., For flexibility, you can’t beat them, Manufacturing Engineering, May 1989, 49–52. 12. Coleman, J. R., Machining centers cut it in job shops, Manufacturing Engineering, March 1990, 41–45. 13. Hines, F., Specialized CAM: a success story, Manufacturing Engineering, May 1989, 88–89. 14. Bolton, K. M., Biarc curves, Computer-Aided Design, Vol. 7, No. 2, 1975, 89–92. 15. Su, B. Q. and Liu, D. Y., Computational Geometry—Curve and Surface Modeling, Academic Press, NY, 1989. 16. Moreton, D. N. and Parkinson, D. B., The application of a biarc technique in CNC machining, Computer-Aided Engineering Design, 8, 1991, 54–60. 17. Schonherr, J., Smooth biarc curves, Computer-Aided Design, Vol. 25, No. 6, 1993, 365–370. 18. Sharrock, N., Biarcs in three dimensions, in Martin, R.R., Ed., Mathematics of Surfaces �, Oxford University Press, UK, 1986. 19. Parkinson, D. B., Optimized biarc curves with tension, Computer-Aided Geometric Design, Vol. 9, 1992, 207–218. 20. Piegl, L., Curve fitting algorithm for rough cutting, Computer-Aided Design, Vol. 18, No. 6, 1986, 79–82. 21. Meek, D. S. and Walton, D. J., Approximation of discrete data by G arc splines, Computer-Aided Design, Vol. 23, No. 6, 1991, 411–419. 22. Meek, D. S. and Walton, D. J., Approximating quadratic NURBS curves by arc splines, Computer- Aided Design, Vol. 25, No. 6, 1993, 371–376. 23. Nutbourne, A. W. and Martin, R. R., Differential Geometry Applied to Curve and Surface Design, Volume I: Foundations, Ellis Horwood, NY 1988. 1 © 2001 by CRC Press LLC
24. Makinouchi, S., Okamoto, M., and Yamagata, K., Optimal curve fitting for NC machining by dynamic programming technology, Reports of the Osaka University, Vol. 16, No. 2, 1976. 25. Cantoni, A., Optimal curve fitting with piecewise linear functions, IEEE Transactions on Computers, C-20, No.1, 1971, 59–67. 26. Faux, I. D. and Pratt M. J., Computational Geometry for Design and Manufacture, Ellis Horwood Limited, N.Y., 1985. 27. Ding, Q. and Davies, B. J., Surface Engineering for Computer-Aided Design, Halsted Press, Chichester, England, 1987. 28. Vickers, G. W., Ly, M. H., and Oetter, Numerically Controlled Machine Tools, Ellis Horwood, N.Y. 29. Teng, C. S., Loh, H. T., and Wong, Y. S., A study of the deviation of best-fitted lower-order curves for the cubic Bezier curve, Internal Report, Mechanical and Production Engineering Department, National University of Singapore, 1993. 30. Wong, Y. S., Loh, H. T., and C. S. Teng, An optimal piecewise curve-fitting approach for CNC machining, Transactions of NAMRC, Vol. 23, 1995, 157–162. 31. Papalambros, P. Y. and Wilde, J. W., Principles of Optimal Design: Modeling and Computation, Cambridge University Press, 1988. 32. Jasbir, S. A., Introduction to Optimum Design, McGraw Hill, NY, 1989. 33. Hoschek, J., Circular splines, Computer-Aided Design, Vol. 24, 1992, 611–618. 34. Parkinson, D. B. and Moreton, D. N., Optimal biarc curve fitting, Computer-Aided Design, Vol. 23, 1991, 411–419. 35. Ong, C. J., Wong, Y. S., Loh, H. T., and Hong, X. G., An optimization approach for biarc curverfitting of B-spline, Computer-Aided Design, Vol. 28, No. 12, 1996. 36. Kao, J. H., Loh, H. T., and Prinz, F., Least-square biarc curve fitting for CNC machining, Proceedings of 17th Computers in Engineering Conference, 1997. 37. Evershiem, W. and Auge, J., Automatic generation of part program for CNC-coordinate measuring machine linked to CAD/CAM systems, Annals of the CIRP, Vol. 35, No.1, 1986. 38. DMIS, “Dimensional Measuring Interface Specification,” Version 2.1, Computer-Aided Manufacturing International Inc., 1989. 39. Intergraph/Mechanical Probe CMM Option, Intergraph Corporation, Huntsville, Alabama. 40. Pro/CMM, ProEngineer CMM Option, Parametric Technology Corporation, 128 Technology Drive, Waltham, MA 02154, USA. 41. ANSI Y14.5M-1982, Dimensioning and Tolerancing, American Society of Mechanical Engineers, USA, 1983. 42. Yau, H. T. and Menq, C. H., An automated dimensional inspection environment for manufactured parts using coordinate measuring machines, International Journal of Production Research, Vol. 30, No. 7, 1992, 1517–1536. 43. Zhang, Y. F., Nee, A. Y. C., Fuh, J. Y. H., Neo, K. S., and Loy, H. K., A neural network approach to determining optimal inspection sampling size for CMM, Journal of Computer Integrated Manufacturing Systems, Vol. 9, No. 3, 1996, 161–169. 44. Neo, K. S. and Wong, Y. S., Link between a CAD system and a computer-controlled CMM via the dimensional measuring interface specifications (DMIS), Proceedings of the Industrial Automation ’92 Conference, 20–23 May 1992, World Trade Centre, Singapore, 179–188. 45. Pao, Y. H., Komeyli, K., Shei, D., Leclair, S., and Winn, A., The episodal associative memory: managing manufacturing information on the basis of similarity and associativity, Journal of Intelligent Manfacturing., Vol. 4, 1993, 23–32. 46. Elmaraghy, H. A. and Gu, P. H., Expert system for inspection planning, Annals of CIRP, Vol. 36, No.1, 1987, 85–89. 47. Merat, F. L., Radack, G. M., Roumina, K., and Ruegsegger, S., Automated inspection planning within the rapid design system, IEEE International Conference on Systems Engineering, 1991, 42–48. 48. Merat, F. L. and Radack, G. M., Automatic inspection planning with a feature based CAD system, Robotics & Computer Integrated Manufacturing, Vol. 9, No. 1, 1992, 61–69. © 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
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
Page 73 and 74: © 2001 by CRC Press LLC TABLE 2.5
Page 75: TABLE 2.7 Partial Listing of Dimens
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 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
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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
Page 275 and 276:
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
Page 301 and 302:
• Programming logic and VLSI arra
Page 303 and 304:
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
Page 329 and 330:
Quantity analysis methods have the
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y a revolute joint. By selecting al
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