ComputerAided_Design_Engineering_amp_Manufactur.pdf

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FIGURE 5.4 Decision table and tree as used in CAPP. (From Shah, 1991). Compared to decision tables, decision trees can be more easily updated and maintained. The flexibility in expanding and contrasting the tree, if necessary, is another advantage. Figure 5.4 illustrates the decision table and tree. APPAS (Chang and Wysk, 1981) is a good example for a decision tree application. One system that employs decision trees is AUTOPLAN (Vogel and Adard, 1981). TIPPS (Chang and Wysk, 1984) employs decision trees combined with AI techniques. Expert System Techniques (AI) The solution to the process planning task depends mainly on the empirical knowledge relevant to the organization, based upon the existing facilities. The popularity of expert systems in CAPP is due to this qualitative, subjective, imprecise and company-specific nature of the process planning knowledge. The expert systems designed to cope with such knowledge characteristics are also much easier to modify and customize than the fixed logic conventional systems, because the knowledge in expert systems is explicitly represented and segregated from the planning (inference) mechanism. In general, problems in a production system formalism can be represented by an initial state, a goal state, a set of operators, and a control structure. One of the frequently used methods to solve problems in AI is the theorem proving technique. Using this technique, we can proceed from the initial state to the goal state. Proving that the goal state can be reached from the initial state will involve the applications of the operators, thereby providing a solution to the problem. The process planning problem falls into this category. In the part manufacturing problem, the initial state is the raw material or workpiece from which the part is to be produced and the goal state is the finished part. The set of operators comprises the available machine tools, cutters, etc. Processing of the part involves proceeding from the raw material to the finished part. Application of the operators (machine tools and cutters) moves the problem from one state to another. The various stages represent the workpiece in work-in-progress condition: Sinitial → S1 → S2 → . . . . → Sfinal These stages are non-reversible as once the material is removed, it cannot be added back. However, in process planning, because the actual material removal has not been done, the stages can be reversed,
FIGURE 5.5 leading to the development of alternative process plans. This strategy is used in the development of expert CAPP systems. 5.5 Modeling for CAPP Conventional process planning in a manufacturing system. In the conventional manufacturing system, a process planner studies the engineering drawing for deciding the plan for its manufacture based on (a) the geometrical and technological constraints present in the part specification and (b) the manufacturing resources available on the shop floor (Figure 5.5). Thus the engineering drawing can be considered the communication link between the design and planning functions, while the process plan forms the link between the planning and manufacturing functions. The engineering drawings, manufacturing resources (machine and tool specifications), and process plans meant for interpretation by humans using their cognitive skills do not fit in the scheme where computers are used for automating the process planning function. Analogously, the development of a CAPP system thus involves the modeling of the following elements (Figure 5.6): 1. Design specification: The modeling of the design specification involves the representation of the part to be manufactured. The resultant part model establishes the link between design and CAPP functions. This model can be called the Part Design Internal Representation (PDIR). 2. Manufacturing resources: Preparation of the process plans is always guided by the status of manufacturing facilities on the shop floor. The information about these resources should be made available to the CAPP system in order to generate realistic plans. The model representing the manufacturing resources can be termed the Manufacturing Resources Internal Representation (MRIR). 3. Process plan: Modeling of the process plan involves the representation of the manufacturing instructions in a structured form. This model can be called the Process Plan Internal Representation (PPIR). CAPP can be thought of as the modeling of the above elements and the interaction between these. The following sections describe the various methods of modeling for each of these elements.
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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
Page 87 and 88: FIGURE 3.6 contact point. The geome
Page 89 and 90: FIGURE 3.8 The vertical support fix
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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
Page 127 and 128: The calculated minimum form error i
Page 129 and 130: FIGURE 4.11(a) A typical mold havin
Page 131 and 132: FIGURE 4.11(d) Inspection results f
Page 133 and 134: FIGURE 4.12(c) Maximum deviation (p
Page 135 and 136: 5.1 Introduction Developments in th
Page 137: process plan for a part (Figure 5.3
Page 141 and 142: the previously stored process plan
Page 143 and 144: FIGURE 5.7 Techniques of defining a
Page 145 and 146: 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
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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
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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
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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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