ComputerAided_Design_Engineering_amp_Manufactur.pdf

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classified into two major groups by their relationships to datum: single features and related features. The single features are defined by the single datum; related features are defined by the relationship between the features. Form tolerances state how far the actual features are permitted to vary from the designed nominal form and consist of standard form features and non-standard form features. Standard form features include lines, planes, circles, and cylinders, while non-standard form features include curves and surfaces. Thus the corresponding form tolerances are defined as straightness, flatness, circularity, cylindricity, and profile of curves or surfaces. The form tolerances are described by tolerance zones, which set the limits of the extreme boundaries of features. The orientation tolerances specify the geometrical relationships to datum. Three types of orientation tolerances are (1) parallelism, (2) perpendicularity, and (3) angularity. Location tolerances state the permissible variation in the specified position of a feature in relation to some other features or data: true position, concentricity (coaxiality), and symmetry. Run-out tolerance is the deviation from the perfect form of a part surface of revolution directed by full rotation of the part on a datum axis. 7 Sculptured Surfaces: Mathematical Description There are several ways of describing sculptured surfaces mathematically. It is usual to consider a sculptured surface as consisting of several patches of cubic polynomial surfaces. Parametric polynomial surfaces and B-spline surfaces are considered in this section. 2,4 Parametric Polynomial Surface When u, v are the principal parameters defining the principal direction in a surface patch, the points data, P( u, v), on the surface can be expressed as the sum of parametric polynomials of the third order. That is, where Aij is the coefficient matrix of the column vector defining the polynomial. B-Spline Surface The points data, P( u, v), on the surface can be described in the B-spline form as follows: (4.1) (4.2) where Qij( i � 1, 2,…, n; j � 1, 2,…, m) are the control points data, ( m � 1) and ( n � 1) are the number of control points, and ( u), ( v) are the cubic blending functions for the u, v directions, respectively. 6 Ni,3 Nj,3 P( u,v) Aiju i v j � i�0 j�0 Normal Vector to the Sculptured Surface The normal vector, N( u, v), at ( u, v) location on the surface can be evaluated from the partial derivatives of the surface along the u, v directions. Thus, where the normal vector is divided by the absolute value to make the unit normal vector. n 3 3 � � m � � P( u,v) � QijNi,3( u)Nj,3( v) i�0 j�0 N( u, v) � ( � P( u,v)��u � �P( u,v)��v )� �P( u,v)��u ��P( u,v) ��v (4.3)
4.3 Measurement Points Sampling and Path Planning For the selected inspection features, the following inspection planning procedures are performed, i.e., measurement points sampling for sculptured surfaces and basic features. Measurement Points Sampling and Path Plan for Sculptured Surfaces The measurement points distribution, i.e., measurement points sampling, is the key process for inspection planning. In this section, three methods are presented for sculptured surfaces: uniform distribution, curvature dependent distribution, and a hybrid distribution of the two. Uniform Distribution Method The basic idea of the uniform distribution method is that the grids formed by the measurement points have a nearly square distribution, and this aims uniform coverage for the sculptured surfaces. Suppose N points are to be distributed over a sculptured surface consisting of M surface patches. Let � be the dimensional ratio of the u direction to the v direction of the sculptured surface. Then the number of the measurement points, , , along the u, v directions in a patch can be determined as follows: N u N v Nu � round ( N�M� ) N v � round ( �Nu) (4.4) (4.5) where the round( ) is the function for rounding off the values in the bracket. The parameters, ui, vj along a specified surface patch are determined based on the uniform allocation. That is ui � ( i � 0.5)�Nu( i � 1, 2… , , Nu ) Vj � ( j � 0.5)�Nv( j � 1, 2… , , Nv) (4.6) (4.7) where 0.5 is used so that the sampled points are located on the middle of the surface grids. Curvature Dependent Distribution Method For the high-curvature surface measurement, it is desirable to have more sampling points than for smallcurvature surface measurement. The measurement points can be distributed based on the normal curvature calculation along the sculptured surface. The normal curvature can be defined as: Normal Curvature � Curvu � N � Curvv � N (4.8) where the Curv u, Curv v are the curvature vectors along the u, v direction, respectively, and N is the unit normal vector of the surface at the measurement points. The Curv u, Curv v are Curv u Curv v � � P u P v 3 � Pu � Puu 3 /Pv� Pvv (4.9) (4.10) where P u, P v, P uu, P vv indicate the first and second derivative of the sculptured surface along the u, v directions, respectively. The measurement points are distributed so that relatively more points are sampled on the region of higher curvature.
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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
Page 61 and 62: FIGURE 2.16 Types of biarcs. FIGURE
Page 63 and 64: FIGURE 2.18 Approximation of scanne
Page 65 and 66: FIGURE 2.20 A touch trigger probe c
Page 67 and 68: TABLE 2.4 Substitute Elements in CM
Page 69 and 70: FIGURE 2.21 CMM planning requiremen
Page 71 and 72: Product Model Representation for Co
Page 73 and 74: © 2001 by CRC Press LLC TABLE 2.5
Page 75 and 76: TABLE 2.7 Partial Listing of Dimens
Page 77 and 78: 24. Makinouchi, S., Okamoto, M., an
Page 79 and 80: Bijan Shirinzadeh Monash University
Page 81 and 82: FIGURE 3.1 Trends in flexible manuf
Page 83 and 84: FIGURE 3.3 and constrain different
Page 85 and 86: 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
Page 91 and 92: and moments about the contact point
Page 93 and 94: een identified, the normals are use
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: FIGURE 4.1(b) Conceptual framework
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 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
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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
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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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