ComputerAided_Design_Engineering_amp_Manufactur.pdf

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universities have demonstrated CBR in a wide variety of domains including law, cooking, and American football. Schank’s student, Hammond (1989), described the architecture and the implementation of a case-based planner for the generation of Szechwan cooking recipes. Some key components of a CBR system are • A means to store and index past cases. • A means to retrieve the nearest case. • A means to modify the old case to meet the goals of the new case. Today, the implementation of these components has been restricted to relatively ‘‘simple’’ domains such as generation of cooking recipes, criminal sentencing, legal reasoning in patent law, etc. In such domains, it is possible to describe the criteria used to index the cases and describe the goals as textual statements for storage and manipulation in a program. One of the greatest challenges facing the development of a case-based system for die design is to find a way to describe the product (which is basically geometrical in nature) in simple descriptive statements such that it can be indexed and stored in a library of cases. Now, it is believed that the feature-tree of a product can be used to index a metal-stamping workpiece for storage as a library of cases. The featuretree can also be used to search and retrieve the nearest case for adaptation. However, the feature-tree of a product can be topologically rather complicated and the pattern matching process required to locate a similar tree or the nearest tree from a library of products can be tedious and computationally rather complex. A complete treatment of a case-based approach for die design is outside the scope of this chapter. Furthermore, there are still many problems that need to be addressed. However, we believe that the techniques described in this chapter provide some of the initial building blocks to achieve the ultimate objectives. 7.15 Conclusion The tool and die industry is facing three severe challenges: First, the increasingly competitive market has forced companies to push out new products more frequently to attract consumers. This has put a tremendous amount of pressure on product and tooling designers who are required to work under the constraints of increasingly shorter lead times. Second, as consumer expectation becomes more sophisticated, the high product quality demanded invariably requires the use of new materials with high standards of accuracy and finishing. The tools and dies required to make these products need to be more precise and sophisticated, stretching the expertise of experienced tool and die designers to their limits. Third, in the newly industrialized and industrializing countries, young people are reluctant to take up craftsmanshiprelated courses that require long apprentice training programs. Hence, the tool and die industry will find it very difficult to maintain the pool of well-trained, experienced personnel it requires to sustain the growth needed to keep pace with the continuous demands for new and innovative products by the consumer. The use of conventional CAD/CAM systems has helped to automate the drafting, NC programming work associated with design and tool making. This has partly helped to alleviate the problems posed by the challenges identified above. However, it has not replaced the experience-based, trial-and-error approach. A longer term solution is to introduce intelligent CAD/CAM practices to the die making industry to help it meet with these challenges. Further Information The authors were awarded funding from the National Science and Technology Board (NSTB) of Singapore on November 1, 1996 to develop an Intelligent Progressive Die (IPD) system, a knowledge-based software for the planning, design, and manufacture of progressive dies.
References Cheok, B.T. et al. (1994). Some aspects of a knowledge-based approach for automating metal stamping die designs, Computers in Industry, Vol. 24, 81–96. Cheok, B. T., Foong, K. Y., and Nee, A. Y. C. (1996). An intelligent planning aid for the design of progressive dies, Proceedings of the Institution of Mechanical Engineers, Vol. 210, 25–35. Dym, C. L. and Levitt, R. E. (1991). An architecture for model-based reasoning, In Knowledge-based Systems in Engineering, McGraw-Hill, New York, 179–190. Hammond, K. J. (1989). Case-based planning: viewing planning as a memory task, Perspectives in Artificial Intelligence, Acadamic Press, Boston, MA. Kolodner, J. L. and Riesbeck, C. K. (1986). Experience, Memory and Reasoning, Lawrence Erlbaum Associates, Hillsdale, NJ. Lee, I. B. H., Lim, B. S., and Nee, A. Y. C. (1993). Knowledge-based process planning system for the manufacture of progressive dies, International Journal of Production Research, Vol. 31, No. 2, 251–278. Mantripragada, R., Kinzel, G., and Altan, T. (1996). A computer-aided engineering system for featurebased design of box-type sheet metal parts, Journal of Materials Processing Technology, Vol. 57, 241–248. Misumi. (1988) Face ’88–’89, Misumi Press Die Standard Components and System Technical Specifications, Misumi Shoji Co. Ltd., Tokyo, Japan. Schank, R. C. (1986). Explanation Patterns: Understanding Mechanically and Creatively. Lawrence Erlbaum Associates, Hillsdale, NJ. Wu, M. C., Chen J. R., and Jen S. R. (1994a). Global shape information modelling and classification of 2-D workpieces. International Journal of Computer Integrated Manufacturing, Vol. 7, No. 5, 261–275. Wu, M. C. and Chen J. R. (1994b). A skeleton approach to modelling 2-D workpieces, Journal of Design and Manufacturing, Vol. 4, 229–243.
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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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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
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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,
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 and 198: In a strict theoretical perspective
Page 199 and 200: 18. Domazet, D. S. and Lu, S. C. Y,
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: FIGURE 7.16 3-D CAD model of a part
Page 245 and 246: include the once forbidden generati
Page 247 and 248: A temporal matrix (T-Matrix) is pro
Page 249 and 250: FIGURE 8.1 A basic process. (From R
Page 251 and 252: FIGURE 8.4(a) Generate a new IG usi
Page 253 and 254: Note if the pair of nodes are both
Page 255 and 256: TABLE 8.1 Summary of the Rules Cond
Page 257 and 258: The correctness of these rules is e
Page 259 and 260: needs to show that after each full
Page 261 and 262: ule is violated, a warning should b
Page 263 and 264: Π1 Π3 Π4 FIGURE 8.8(a) After add
Page 265 and 266: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Page 267 and 268: A ik � ‘U’ and is in a cycle
Page 269 and 270: FIGURE 8.12(a) Add [p2 t7 p7 t8 p4]
Page 271 and 272: FIGURE 8.12(c) Add a token to p8 vi
Page 273 and 274: FIGURE 8.12(e) Add [p8 t9 p14 t10 p
Page 275 and 276: FIGURE 8.13 An example of rule TT.0
Page 277 and 278: FIGURE 8.14 A GPN model of a machin
Page 279 and 280: FIGURE 8.15(b) The first exclusive
Page 281 and 282: FIGURE 8.15(d) The last exclusive T
Page 283 and 284: FIGURE 8.15(f) After entering the a
Page 285 and 286: FIGURE 8.15(h) Completion of Compon
Page 287 and 288: FIGURE 8.15(j) Two PP-generations:
Page 289 and 290: FIGURE 8.15(l) The last exclusive T
Page 291 and 292: 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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