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t2 entity 1 FIGURE 8.19 An ex<strong>amp</strong>le of the arc-ratio rule ARR.1.a. on top of the TT.4 rule. 3. For each backward PP-generation, � 1 for any two in the same new circle. � � for any PSP in the new circle and any other PSP not in the same circle. 4. For TT-generation between two PSPs with d � �, their � 1 after the application of TT.3 rule. 5. For all other generations, the between any and any current PSP depends on their structure relationship. If they are concurrent or sequential (exclusive), then the value of is one (�). s d s d s � w The time complexity for the above steps is O ( n1) ; hence, the total time complexity is O ( n2) . Enhancement of the Synthesis Algorithm p2 p1 t1 Π1 p3 Π3 2 t3 2 p4 t4 2 t5 4 2 p5 d s t6 4 2 (D.1) Generations Between Exclusive Transitions Since now the generation where tg tj is permitted, Rule TT.0 must be modified to take out the corresponding phrase as follows: p6 (0) TT.0 If one of them is in a cycle which was solely generated using Rule PP.1, then signal “forbidden, delete the � ” and return. In addition, we add a new rule: w (5) TT.5 If tg | tj , then follow the synchronization rule to serialize tg and tj . Here we discuss the implementation. First, in practice, the designer may wish to serialize other transitions in addition to tg and tj . Thus, it is desirable that we flag all control transitions for the designer to select. Note that all control transitions must be mutually exclusive. As a result, they must have a common generation point, which is the decision place. Colors of all these control transitions must change to help the designer construct the cycle. Second, the designer needs to construct a cycle passing through the control transitions at his own choice. Whenever a control transition is included in the cycle, set its color to black. Put tokens into one place in the cycle and all nodes in the cycle will be automatically colored black (originally red). p7 t5' p5' t6' � w 3 3 Π4 4 p2' entity 2 t2' p3' t3' p4' 4 p1' t1' t4' d s d s Π2 3
Note that control transitions are output transitions of the prime start place, denoted , of and , and tj must be a control transition. Let CT be the set of control transitions. Procedure “find_prime_start gj ( tg, tj) ” is to find pps . gj 1. pps � find_prime_start ( tg, tj ){ Select one input place pin of tj such that ( pin → tg) , gj then pin is the pps ; } 2. find_CT(){ gj CT � { t | t � ( pps) •}. } The time complexities for “find_prime_start ( tg, tj) ” and “find_CT()” are both O(n). During the process of constructing the cycle, each new TT-path must have its tj , a control transition ( i.e., tj � CT) . It is desired that we generate these new TT-paths in a continuous fashion; i.e., it is better not to construct the cycle in a piecewise fashion. Let � be the PSP that has just been generated with and . The and for the next new PSP must satisfy the following conditions: w� t�g t� j tg tj 1. t� j → tg . 2. tj must be a control transition 3. tj � tc, tc�C T and tc � tj All such are colored “brown” for the designer to pick. Afterwards, all such brown transitions will be colored “black.” Procedure “color_generation_transitions()” extracted from our source code is to color all above . t g t g void color_generation_transitions (num,num1) int num,num1; { int i,j,k; for(i�1;i��;i��) { / �find PSP that are sequential later than PSP num1 and concurrent to PSP num� / if(T_Matrix[i][num1]��1 � T_Matrix[i][num1]��’N’� T_Matrix[i][num1]��’L’) { for(each �k�CT){ if(T_Matrix [i][k]!�’X’) continue;/ �failed try a new i� / } / �so far, �i � all PSP in C � T / for(each transition tj in PSP i) } } } strcpy(color[j],“brown”);/ �color brown� ti / t� j where num1 are the PSP containing , �i the total number PSPs, and T_Matrix[a][b] the temporal relationship between �a and �b. The total time complexity of the above procedure is O � . 2 ( ) (D.2) GPN We now consider how to implement the arc-ratio rule. Upon the generation of an NP, its least ratio in the NP must be literally smaller than that in N1 . This causes extra computation of the least ratio in N1 . To do so, we have to search all paths between ng and nj , which is quite time consuming (called the canonical method for later reference). To avoid this, we select a reference node and choose it to be the gj pps t g t j
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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,
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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
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
Page 293 and 294: FIGURE 8.17 An example of partial f
Page 295: Theorem 10: If pi → pk in a synth
Page 299 and 300: 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
Page 305 and 306: 53. Villaroel, J. L., J. Martinez,
Page 307 and 308: q u : the numerator of the least ra
Page 309 and 310: geometric modeling, engineering ana
Page 311 and 312: In dimension driven or parametric d
Page 313 and 314: FIGURE 9.3 configuration of bodies
Page 315 and 316: FIGURE 9.6 FIGURE 9.7 Geometric Int
Page 317 and 318: FIGURE 9.8 Intersection of degrees
Page 319 and 320: will introduce a way to propagate p
Page 321 and 322: Completeness of Dimensions Complete
Page 323 and 324: FIGURE 9.12 3-D constraints. © 200
Page 325 and 326: The distance constraint from pt �
Page 327 and 328: therefore: Now merge the lists and
Page 329 and 330: Quantity analysis methods have the
Page 331 and 332: y a revolute joint. By selecting al
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