ComputerAided_Design_Engineering_amp_Manufactur.pdf

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Acknowledgements The work reported here has been done at Indian Institute of Technology, New Delhi. The majority of the work reported is done under the sponsorship of the Department of Electronics, Government of India, over a period of five years. A large number of students (A.V.S.R.K. Prasad, G. Manidhar, Vijay Arora, S.K. Gupta, and others) have worked in this period for their masters and doctoral programs in the area of CAPP and are responsible for developing part of the ideas. The assistance received from Bharat Heavy Electricals Limited, Bhopal, in the system development and proving is gratefully acknowledged. References 1. Arora, V., 1991, Pocket Identification and Setup Planning in Computer Aided Process Planning of Rotational Parts, Unpublished M. Tech. Thesis, NC Laboratory, Department of Mechanical Engineering, Indian Institute of Technology, New Delhi, India. 2. Balakrishnan, P. and DeVries, M. F., 1982, A review of computerized machinability data base systems. Proceedings of 10th North American Manufacturing Research Conference, 348–386. 3. Beightler, C. S. and Phillips, D. T., 1976, Applied Geometric Programming, John Wiley and Sons, Inc, NY. 4. Brewer, R. C. and Rueda, R., 1963, A simplified approach to the optimum selection of machining parameters. Engineers’ Digest, 24(9), 133–151. 5. Brown, R. H., 1962, On the selection of economical machining rates. International Journal Production Research, 1(1), 1–22. 6. Butterfield, W. R., Green, M. K., Scott, D. C., and Stoker, W. J., 1986, Part features for process planning, CAM-I report: C-85-PPP-03. 7. Case, K. and Gao, J., 1993, Feature technology: an overview, Int. J. Computer Integrated Manufacturing, 6(1&2), 2–12. 8. Chandrupatla, T. R. and Belegundu, A. D., 1991, Introduction to Finite Elements in Engineering, Prentice-Hall, New Delhi. 9. Chang, T. C. and Wysk, R. A., 1981, An integrated CAD/automated process planning system, AIIE Transactions, 13(3), 223–233. 10. Chang, T. C. and Wysk, R. A., 1984, Integrating CAD and CAM through automated process planning, International Journal of Production Research, 22(5), 877–894. 11. Chen, S. J., Hinduja, S., and Barrow, G., 1989, Automatic tool selection for rough turning operations, Int. Journal of Machine Tools and Manufacture, 29(4), 535–553. 12. Chua, M. S., Loh, H. T., Wong, Y. S., and Rahman, M., 1991, Optimization of cutting conditions for multipass turning operations using sequential quadratic programming. Journal of Materials Processing Technology, 28(1/2), 253–262. 13. Clark, A. C. and South, N. E., 1987, Feature based design of mechanical parts, Proc. AUTOFACT 87, 169–176. 14. Colding, B. N., 1992, Intelligent selection of machining parameters for metal cutting operations: the least expensive way to increase productivity. Robotics & Computer Integrated Manufacturing, 9(4/5), 407–412. 15. Crookall, J. R., 1969, The performance-envelope concept in the economics of machining. International Journal of Machine Tool Design and Research, 9, 261–278. 16. Cunningham, J. J. and Dixon, J. R., 1988, Designing with features: the origin of features, Proc. ASME Computers in Engineering Conf., v. 1, San Francisco, USA, 237–243. 17. Dixon, J. R., Cunningham, J. J., and Simmons, M. K., 1987, Research in designing with features, Intelligent CAD, I, (Ed.), Yoshikawa, H. and Gossard, D., Proc. IFIP TC5/WG 5.2 Workshop on Intelligent CAD, Boston, MA, 137–148.
18. Domazet, D. S. and Lu, S. C. Y, 1992, Concurrent design and process planning of rotational parts, Annals of the CIRP, 41(1), 181–184. 19. Dong, X. and Wozny, M. J., 1990, Feature volume creation for computer aided process planning, in: Geometric Modeling for Product Engineering, (Ed.) Wozny, M. J., Turner, J. U. and Priess, K., Elsevier Science Publishers B. V., North Holland, 385–403. 20. Drake, S. and Sela, S., 1989, A foundation for features, Mechanical Engineering, 111, 66–73. 21. Ermer, D. S. and Kromodihardjo, S., 1981, Optimization of multipass turning with constraints. Journal of Engineering for Industry, Transactions of ASME, 103(4), 462–468. 22. Evershiem, W. and Holz, B., 1982, Computer aided programming of NC machine tools by using the system AUTAP-NC, Annals of the CIRP, 31(1), 323–327. 23. Faux, I. D., 1988, Modeling of components and assemblies in terms of shape primitives based on standard D & T surface features, in: Geometric Modeling for Product Engineering, (Ed.) Wozny, M.J., Turner, J. U., and Preiss, K., Elsevier Science Publishers B. V., North-Holland, 259–275. 24. Gindy, N. N. Z., Huang, X, and Ratchev, T. M., 1993, Feature-based component model for computer-aided process planning systems, Int. J. Computer Integrated Manufacturing, 6(1&2), 20–26. 25. Gindy, N. N. Z., 1989, A hierarchical structure for form features, Int. J. Prod. Res., 27, 2089–2103. 26. Gossard, D. C., Zuffante, R. P., and Sakurai, H., 1988, Representing dimensions, tolerances and features in MCAE systems, IEEE Computer Graphics & Applications, March, 51–59. 27. Gupta, N. and Kapoor, S., 1986, Computer Aided Process Planning and Part Programming for CNC Turning Centers Using Group Technology, Unpublished B.Tech. Thesis, N C Laboratory, Department of Mechanical Engineering, Indian Institute of Technology, New Delhi, India. 28. Halevi, G. and Weill, R., 1985, Influence of manufacturing tolerances on fixturing of machined parts in process planning systems, 1st CIRP Working Seminar on CAPP, Paris, 31–34. 29. Halevi, G. and Weill, R. D., 1995, Principles of Process Planning A Logical Approach, Chapman & Hall, London. 30. Hati, S. K. and Rao, S. S., 1976, Determination of optimal cutting conditions using deterministic and probabilistic approaches. Journal of Engineering for Industry, Transactions of ASME, 98(1), 354–359. 31. Hayes, C., 1990, Machine Planning: a Model of an Expert Level Planning process, Ph.D. thesis, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. 32. Hayes, G. M. and Davis, R. P., 1979, A discrete variable approach to machine parameter optimization, AIIE Transactions, 11(2), 155–159. 33. Henderson, M. R. and Anderson, D. C., 1984, Computer recognition and extraction of form features: A CAD/CAM link, Computers in Industry, 5(4), 329–339. 34. Hinduja, S. and Barrow, G., 1986, TECHTURN: a technologically oriented system for turned components, Proc. 1st Int. Conf. on CAPE, Edinburgh, UK, 255–260. 35. Hinduja, S. and Huang, H., 1989a, OP-PLAN: an automated operation planning system for turned components, Proc. of Inst. of Mech. Engrs., 203(B3), 145–158. 36. Hinduja, S. and Huang, H., 1989b, Automatic determination of workholding parameters for turned components, Proc. of Inst. of Mech. Engrs., 203(B2), 101–112. 37. Hinduja, S., Petty, D. J., Tester, M., and Barrow, G., 1985, Calculation of optimum cutting conditions for turning operations. Journal of Engineering Manufacture, Proceedings of Institution of Mechanical Engineers, 199(B2), 81–92. 38. Iwata, K. and Arai, E. ,1983, Development of integrated modeling system for CAD/CAM of machine products, in: Advances in CAD/CAM, (Ed.) Ellis, T.M.R. and Semenkov, O. I., North Holland Publishing Company. 39. Iwata, K., Murotsu, Y., and Oba, F., 1977, Optimization of cutting conditions for multipass operations considering probabilistic nature in machining processes. Journal of Engineering for Industry, Transactions of ASME, 99(1), 210–217.
Page 1 and 2:
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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FIGURE 2.11 Linear approximation of
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FIGURE 2.13 Circular approximation
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FIGURE 2.14(b) Circular approximati
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FIGURE 2.16 Types of biarcs. FIGURE
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FIGURE 2.18 Approximation of scanne
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FIGURE 2.20 A touch trigger probe c
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TABLE 2.4 Substitute Elements in CM
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FIGURE 2.21 CMM planning requiremen
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Product Model Representation for Co
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© 2001 by CRC Press LLC TABLE 2.5
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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
Page 147 and 148: in relation to another feature (e.g
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
Page 157 and 158: TABLE 5.1 Data Structure for Repres
Page 159 and 160: FIGURE 5.16 Classification of the t
Page 161 and 162: system to ensure that the part bein
Page 163 and 164: FIGURE 5.19 Graphical model of the
Page 165 and 166: TABLE 5.4 Data Structure for Repres
Page 167 and 168: FIGURE 5.20 Mapping between machini
Page 169 and 170: algorithms. (Some details of the pr
Page 171 and 172: FIGURE 5.24 An example rotational p
Page 173 and 174: FIGURE 5.26 Down_face-turn-up_face
Page 175 and 176: FIGURE 5.27 Procedure for machine t
Page 177 and 178: FIGURE 5.28 Determining the number
Page 179 and 180: DL is needed to be set only if the
Page 181 and 182: FIGURE 5.31 Operation sequencing co
Page 183 and 184: Cutting Tool Selection Selection of
Page 185 and 186: 1. A tool is searched for in the da
Page 187 and 188: FIGURE 5.32 Inputs to optimization
Page 189 and 190: When these values are substituted,
Page 191 and 192: Usually, maximum and minimum speed
Page 193 and 194: FIGURE 5.33 Solution methodology.
Page 195 and 196: TABLE 5.6 Process Plan Internal Rep
Page 197: In a strict theoretical perspective
Page 201 and 202: 63. Prasad, A. V. S. R. K., Rao, P.
Page 203 and 204: CAD systems. The sample consists of
Page 205 and 206: learn to master the new system. An
Page 207 and 208: Although researchers appear to agre
Page 209 and 210: Link and Zmud28 found that organic
Page 211 and 212: Informal Training Informal CAD trai
Page 213 and 214: informal training programs, felt th
Page 215 and 216: 6. C. A. Beatty, Tall Tales and Rea
Page 217 and 218: A.Y.C. Nee National University of S
Page 219 and 220: FIGURE 7.1 Planning, design, and ma
Page 221 and 222: A metal stamping can have the follo
Page 223 and 224: FIGURE 7.3 Strips used to notch out
Page 225 and 226: A Skeletal Approach for the Recogni
Page 227 and 228: These findings can be used to devel
Page 229 and 230: Semi-Direct Piloting In cases where
Page 231 and 232: a larger value, the die operations
Page 233 and 234: FIGURE 7.9 Symbolic relationship be
Page 235 and 236: FIGURE 7.11 The shape of the envelo
Page 237 and 238: TABLE 7.1 Schema for the Generation
Page 239 and 240: strong reasons to support a move to
Page 241 and 242: FIGURE 7.16 3-D CAD model of a part
Page 243 and 244: References Cheok, B.T. et al. (1994
Page 245 and 246: include the once forbidden generati
Page 247 and 248: 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
Page 301 and 302:
• 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
Page 329 and 330:
Quantity analysis methods have the
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y a revolute joint. By selecting al
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