ComputerAided_Design_Engineering_amp_Manufactur.pdf

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The optimization module will get the details of the part data, both geometrical and technological, from PDIR which is the output of the modeling module. The details pertaining to the material removal volumes (called “pockets”), such as size and shape of the pockets, type of machining operations, and sequence of operations, are obtained from the process planning module. The machine and cutting tool specifications are obtained from the respective modules. The details of work-holding devices and the holding method are taken from the setup planning module. Apart from the part model, the other inputs mentioned above are obtained from process PPIR, which is the internal storage format of the process plan. PPIR gets filled up successively in each of the above mentioned modules and when the control passes through the optimization module, it becomes complete. In addition to these, the coefficients for the empirical equations used in this module, such as tool-life, cutting power, and cutting force equations, are stored in a machinability data base for different combinations of work material, workpiece hardness, tool material, and tool geometry. The proper coefficients are retrieved from the data base, based on the above mentioned inputs. Formulation of the Mathematical Model The mathematical model for the optimization problem involves the formulation of the objective function and the formulation of the constraints. Formulation of Objective Function The optimization is usually carried out to achieve any one or a combination of the following objectives: (1) minimization of production cost, (2) minimization of production time or maximization of production rate, and (3) maximization of profit rate. Proper selection of criteria depend on the policy of the company. The minimization of production time is taken as the basis for formulating the objective function below. The unit production time (Tpu) can be expressed as the sum of the loading and unloading time, the machining time, tool changing time, and tool resetting time, i.e., Tpu � Tl � Tm � ----- ttch � T The machining time for turning operation is given by T m � At low cutting speeds, tools have a higher life but productivity is low, and at higher speeds the reverse is true. This suggests that there is an optimum that balances tool life and cutting speed. Based on experimental work, the following tool life formula is proposed: V T n where T is the tool life in minutes, V is the cutting speed, m/min, and C and n are constants. Though this is a fairly good formula, it does not take all the affecting parameters into account. As a result, the applicability of the above formula is restricted to very narrow regions of cutting process parameters. This formula was extended to reduce this deficiency: This is the most common tool life equation employed by a number of researchers. The constants for the above equation for some common work materials are given in the table below (Widia, 1986). Besides the cutting process parameters, tool life depends on work material as well as tool material. The constants, therefore, are given for each combination of work and tool material. T m �DL --------------- 1000vf � C V T n f n1 n2 d � C T rs
When these values are substituted, the expression for T pu becomes In this equation, T l is a constant. In a single-pass operation, the term T rs does not have relevance. Also, the depth of cut, d, is fixed and is known in advance. Hence, there are effectively only two variables to be determined in a single-pass operation, i.e., the cutting speed and the feed rate. The final form of objective function for a single-pass operation will be to minimize where Tool material ISO Grade Work material AISI Tool life, n Exponent for Constant C Similarly, the objective function for a multipass turning operation with m passes can be formulated as given below: Formulation of Constraints Practical limitations are imposed on speed, feed, and depth of cut through constraints. The power constraint assumes significance only in case of rough machining and hence can be ignored in the case of finish machining. The power consumed, P, during a turning operation can be expressed as This value of cutting power should not exceed the maximum power, P max available on the machine tool. In the above expression, the value of specific cutting force is constant for a particular work material. Hence, the expression for this constraint can be written as The surface finish constraint limits the maximum feed that can be used to attain the required surface finish on the machined feature. This constraint becomes active during finish turning. The expression for Feed, n 1 Depth of cut, n 2 P01, P10 1020 �0.38 �0.06 �0.10 1150 P20, P30 1020 �0.38 �0.17 �0.11 780 P01, P10 1045 �0.22 �0.21 �0.11 350 P20, P30 1045 �0.22 �0.34 �0.12 226 T pu � T pu T l � T 1 T� pu C 01 �DLt tch -- �DL � 1 � n n1 n2 � --------------- � ------------------v f d � 1000vf 1000C � � f C 01v 1 �1 � 1 1 1 ---- 1 ---- 1 1 -- � 1 ---- � 1 n n1 C02v f 1 ---n2 �DLttchd �DL � ----------- C02 � ------------------------- 1000 1000C m � �L Di �Lttch n � ---------- ------- � -------------- D 1000 i�1 vi fi 1000C ivi i�1 P � m � v ap b f pd cpkc ----------------------- 60.1000.� 1 1 1 -- � 1 ---n � 1 ---- 1 n2 fi di Cpv ap b f pdc k p c � Pmax, Cp � ----------------------- 60.1000.� T rs � m.T rs
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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
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 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: FIGURE 5.32 Inputs to optimization
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,
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
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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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