ComputerAided_Design_Engineering_amp_Manufactur.pdf

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within the CAPP system. However, the hard coding of manufacturing data has some serious limitations. For ex<strong>amp</strong>le, if any new machine is added (or removed or breaks down), significant modifications need to be done either in the data files or in the program to reflect the change on the performance of the CAPP system. It is thus desirable to develop MRIR external to the CAPP system. Other advantages of maintaining MRIR as an independent data base management system (DBMS) are (a) Flexibility: The same data bases can be used for other manufacturing functions, e.g., for inventory control; (b) Customization: The data base can be adapted to the unique character of the manufacturing environment at a given instant; and (c) Enhancements: Without affecting the CAPP systems, these data bases can be modified or changed. 5.8 Modeling of the Process Plan for CAPP Process planning involves the selection of machine tools, setups, machining operations, cutting tools, process parameters, etc. All experts agree that the decisions about items in this list must be made at some point during the planning process, but whether the order of the decisions in the list has any relevance is a debatable question. Furthermore, the list is highly interdependent. The order in which the planning decisions are made is a factor of a given manufacturing system. Irrespective of this order, there must be some “container” for keeping the partial plans (or the intermediate results of the planning). In this context, process planning internal representation (PPIR) serves the purpose of a container which can be referred to in various stages of process planning. In addition, the instructions for manufacturing a part can exist in one of several forms: (a) Textual Process Plans: the plans used by the machinists on a shop floor (b) Graphical Simulation: the format used by the automated systems for showing the process plan graphically (c) Pictorial Process Plans: the status of the component (or machining) after each setup for illustrating the part, fixtures, setup, cl<strong>amp</strong>ing, etc. (d) NC Programs: the format used by numerical control machines for executing the process plans. The basic set of manufacturing instructions from which these formats can be derived is the same for a given part. To enable a CAPP system to generate the process plans in one or all of these formats, it is necessary to represent the planning details in a structured format. In this context, the role of PPIR in a CAPP system can be appreciated (Jasthi et al., 1995). To represent the process plan, it is first necessary to gain an insight into the manufacturing instructions. At any instant of manufacturing, the blank (or semi-finished part) is set on a machine tool. In this setup, some portions of the material will be removed so as to move towards the final part specification. These chunks of material can be called machinable volumes (or pockets). The parameters for machining are governed by the part specification and the manufacturing resources. Thus, if (a) the specification of the machine being used, (b) the details of the setup on the selected machine, (c) the number and sequence of pockets being removed in each setup, and (d) the parameters (cutting tools, process parameters, etc.) for machining each pocket are available, then the manufacturing instructions (or partial process plan) at that instant can be specified (Figure 5.10). To summarize, the set comprising machine, setup, pocket, and parameters can be considered the core of the process planning content in the machining domain. If all such sets applicable to a given part can be obtained, the process plan for that part can be generated. Thus the need to represent these ordered sets in a structured format forms the basis for PPIR (Figure 5.11). The implementation of this model is explained in later sections.
FIGURE 5.10 Process plan content.
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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
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 and 138: process plan for a part (Figure 5.3
Page 139 and 140: FIGURE 5.5 leading to the developme
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: FIGURE 5.9 Framework for building a
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 and 198: In a strict theoretical perspective
Page 199 and 200: 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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