ComputerAided_Design_Engineering_amp_Manufactur.pdf

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make this approach realistic in conventional machining. While it is possible to accommodate changing depths of cut in the case of NC machines, the usefulness of employing varying depths of cut will depend upon whether there is a significant improvement with respect to objective function. It is therefore safe to assume that equal depths of cut will be used for all roughing passes. The solution procedure can proceed with finding optimal parameters for the finishing operation first. In case of finishing operations, it can be assumed that the finishing operation is a single-pass operation. This is a reasonable approximation as only very little material will be left to be removed in the finishing operation to achieve the required finish or tolerance on the surface being machined. The parameters can then be determined for roughing operations, based on the remaining depth of material. The optimization for multipass rough turning operations is carried out in two stages. In the first stage, a set of feasible depths of cut, based on the total depth of material to be removed and the permissible limits of depths of cut (resulting in an integral number of roughing passes), can be calculated. To determine the optimal number of passes and hence the depth of cut to be used, optimization will be carried out for each feasible subset of depth of cut and number of passes. Finally, the subset which results in the least production time is chosen to be the optimal pair. The corresponding cutting speed and feed rate are chosen to be the optimal parameters for the roughing operation. The solution methodology is shown in Figure 5.33. The individual optimization problem is solved using the geometric programming technique. The dual of the geometric programming (GP) problem is solved by using the techniques of separable programming (SP) and linear programming (LP) (Kochenberger et al., 1973). Simplex algorithm is used for the solution of the LP problem. The primal variables are determined from the dual solution using the method given in Beightler and Phillips (1976). The steps in GP solution procedure are given in Figure 5.34. Details on the solution procedure of GP dual problem can be found in Kochenberger et al. (1973) and Prasad (1994). Similar formulation can be carried out for facing and boring operations (Prasad et al., 1994a, 1994b). The explicit optimization of cutting parameters for secondary operations such as chamfering, grooving, filleting, knurling, and thread cutting is not necessary. Instead, the parameters for these operations are chosen as a fraction of turning conditions. Formulation for milling operations in a similar approach can be found elsewhere (Manidhar, 1995). Time and Cost Calculation The time required for machining a component is equal to the sum of cutting, setup, and handling times. Handling time includes the time involved in tool change, tool resetting, and component loading and unloading. Based on the outputs obtained so far (machining operation, pocket dimensions, and process parameters), the cutting times can be calculated using the machining formulas. However, the calculation of setup times and the handling times is a data-intensive activity since each company has its own set of standard elemental times. 5.15 Report-GIFTS: Report Generation Module When the control passes through all of the above modules, PPIR contains the necessary information a CAPP system is supposed to generate. The PPIR generated for the ex<strong>amp</strong>le part shown in Figure 5.18 is given in Table 5.6. As explained earlier, PPIR is basically a set of setups, with each setup pointing to a set of pockets. However, PPIR is an internal model and its format is meant for computer processing. Hence, it is not useful for any practical purpose unless it is converted to the required form, comprehensible by the people involved in using the CAPP system. A report generation module, called Report-GIFTS, is developed to serve this purpose. This module can be regarded as a postprocessor which transforms the data from one format to another format. When one looks at various formats of the process plans, four possibilities can be visualized:
FIGURE 5.33 Solution methodology. • Textual process plans (in one or more formats) • Pictorial process plans • Graphical simulation • NC programs and/or CL data. In the present study, these are termed process plan external representations (PPER) and are shown in Figure 5.35. Textual Process Plans The ability to generate the process plans in the prescribed formats specified by the company is one feature that is essential in a CAPP system. It is a common practice in industry to use different formats of the process plans depending upon the function of the personnel (or department). For ex<strong>amp</strong>le, the process plan with simple instructions is handed over to the operators on the shop floor while the same
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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
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
Page 189 and 190: When these values are substituted,
Page 191: Usually, maximum and minimum speed
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
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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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