ComputerAided_Design_Engineering_amp_Manufactur.pdf

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D.Y. Chao National Cheng Chi University Abstract 8 Computer-Aided Design (CAD) Technique for Flexible Manufacturing Systems (FMS) Synthesis Utilizing Petri Nets 8.1 Introduction 8.2 Preliminaries Terminology 8.3 The Synthesis Rules 8.4 Temporal Matrices for Petri Nets 8.5 The Algorithm and Its Complexity Determination of Entries for Pure-Generation • Determination of Entries for Interaction-Generation • Complexity of the Algorithm 8.6 X-Window Implementation Description of a Manufacturing System • Modeling and Synthesis Process 8.7 Enhancement of Synthesis Rules The Model • The Synchronization Rule • Arc-Ratio Rules for General Petri Nets • Enhancement of the Synthesis Algorithm • Synthesis Steps for the Net in Figure 8.14 8.8 Other Features of the Tool 8.9 Conclusions The knitting technique provides a set of simple synthesis rules to construct a large Petri net (PN), thus avoiding time-consuming verification. We show that the synthesized nets are well behaved and form a new class called synchronized choice nets. In order to determine the applicable rules and detect rule violations, we present the concept of temporal matrix that records relationship (concurrent, exclusive, sequential, … etc.) among processes in the PN. Upon each generation, the algorithm will consult and update the matrix. The complexity for the algorithm is O � where � is the total number of processes. This algorithm has been incorporated into our X-Window-based tool for design, analysis, simulation, testing, and synthesis of communication protocols, etc. The rules and algorithm have been extended to 2 ( )
include the once forbidden generations between exclusive transitions and arcs with multiple-weights, i.e., general Petri nets (GPNs). 8.1 Introduction Modern automated manufacturing systems face increasing pressure to improve efficiency and cost effectiveness and to ensure a quick return on large investments in new equipment. 27–29 They are also characterized by the fast dynamics of such systems that may be disturbed by various factors such as urgent orders, possible shortages of tools and materials, energy constraints, and unexpected failures of the equipment including machines, instruments, and computers. It is obvious that decisions have to be made in a short time within the environment of highly automated and interrelated manufacturing systems of the present day. The use of computers in the design and control of manufacturing systems thus becomes mandatory. Over the past 20 years, a new type of manufacturing system, flexible manufacturing system (FMS), has emerged. An FMS is a large and complex system consisting of many interconnected components of hardware and software. Typically an FMS consists of several machines interfaced with automated material handling and computer control. The main tasks in designing an FMS include process routing, the selection of a sequence of operations, and scheduling the assignment of time and resources. PN theory has been applied to specifications, validation, performance analysis, control code generation, and simulation for manufacturing systems. 4,8,9,27–29,31,32,37,42,52,53,55,56,62–64 The first step toward these applications is modeling (or synthesis) of PNs for FMS. 32,53 A PN model is constructed for an FMS. The analysis of this PN model is conducted and system properties are claimed. The PN must satisfy three properties: boundedness, liveness, and reversibility. 3,6 These properties are critical for an FMS to operate in a stable, deadlock-free, and cyclic way. Advantages of PN modeling include: 1. Specifications can be expressed in a highly formal way offering more promise for automated analysis. 2. The model has a natural graphical, as well a textual, representation. 3. Automated analysis is possible for properties, including deadlock-free, mean throughput, mean delay, buffer overflow, and resource contention. 4. Much research has been done on the use of timing with PNs, e.g., Molloy. 35 There are programs available to support automated analysis of timed Petri nets. Deterministic timing models are available for specifying real-time systems. 17,23 Stochastic PNs (SPN) are preferable for performance analysis in systems without real-time constraints. There are simulation programs available for cases where analytic analysis is not practical because of space or time complexity. 5. PNs are able to express solutions to asynchronous events and blocking. 6. It is possible to implement step-wise refinement of system specifications. 7. Some research has been done on the automatic translation of extended PN models into code. PN, although a good model for FMS, has its problems. As the system grows in complexity, the modeling process becomes crucial, the analysis is no longer an easy problem, and it takes a very long time to get the analysis results. It has been shown that the complexity of the reachability problem (also called the state explosion problem) of the Petri nets is exponential. To solve the complexity problem, two approaches have been proposed: reduction24–26,30,33,36,44 and synthesis. Reduction is to reduce a PN to a smaller one while retaining its properties. Analysis is then performed on the smaller PN with much less time required. Two different techniques have been implemented. Reduction is of limited value because modification and re-analysis may have to be conducted if the analysis methods have detected some undesired properties. Synthesis provides the designer a set of guidelines or rules to construct a large PN without logical errors; hence, no analysis is needed. Simulation and animation are time consuming, but are useful to observe evolution of states; thus, they are useful to verify the modeling correctness, to study system transient behavior, and to check analytical results and theories. Analysis provides only steady-state performance figures such as minimum
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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
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 and 242: FIGURE 7.16 3-D CAD model of a part
Page 243: References Cheok, B.T. et al. (1994
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
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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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