ComputerAided_Design_Engineering_amp_Manufactur.pdf

More documents

Recommendations

Info

FIGURE 5.6 Interaction of various elements in computer-aided process planning (CAPP). MRIR: manufacturing resources internal representation; PDIR: part design internal representation; PPIR: process plan internal representation. 5.6 Part Modeling for CAPP One of the problems concerning the automation of the process planning function is that of high input effort needed to describe a part. The reason can be attributed to the fact that the experience and knowledge of humans play a prominent role in interpreting and communicating the part data in a manufacturing system. The translation of the part data from human-interpretable format to computer-interpretable format is complex, but mandatory input for CAPP. Part modeling, through which a complete and unambiguous definition that captures the design content of the part is to be achieved, has become one of the key research issues since the inception of CAPP. There exist three basic sets of data which completely describe the design content of a part: • Geometrical data: The geometrical data give the basic description of the shape. For ex<strong>amp</strong>le, diameter of a hole, depth of a groove, width of a keyway, etc. constitute this type of data. • Technological data: The information pertaining to tolerances and surface finish can be referred to as technological data, e.g., circularity, diametrical tolerance, runout, etc. • General (or global) data: Certain global characteristics that are applicable to the part as a whole are often added to the design specification. These global attributes include quantity to be produced, work material, design number, part name, and other task-dependent details which normally appear on the drawing and the process plan. Representation of Geometrical Details There are many methods followed for part modeling as a part of CAPP system development. These can be categorized as (1) non-CAD models, (2) CAD models, and (3) feature-based models. Non-CAD Models These modeling methods are characterized by the absence of CAD systems. These can be classified as • Group technology (GT) coding which is based on GT coding schemes for the retrieval of existing process plans. Detailed process plans for several part families are developed and stored in the system’s data base. The major characteristics of the new part are matched with a similar part and
the previously stored process plan is retrieved. The retrieved process plan can then easily be modified to suit the new part. • Interactive/menu driven models which pose a series of questions (or menus) and interactively gather the part data from the user. Studying the product range and variation is essential before designing system-user interaction. Although it seems to be simple to answer such questions, the user may not have control over the way in which the system interacts with him, and hence he is compelled to navigate through a series of redundant questions to define even a simple part. This approach is limited to only simple parts and is characterized by the absence of graphic interface. • Keywords/description languages which can provide detailed information for CAPP systems. The output format of the part data can be designed such that the process planning function can easily accomplish its task. This approach offers limited flexibility to the user in the sense that the user can exercise some control over the system, but, because of its specialized nature, it is limited to simple parts. For complex parts, the translation of the original design to input language can be very tedious. Sometimes the resultant model may not be unique and the sequence with which a part is modeled may affect the planning logic. CAD Models Although the modeling methods discussed so far provide alternate solutions for linking design to manufacture, the main problem of generating the part data from a CAD data base is still largely unresolved. The increased use of CAD and the need to integrate it with CAM has led to the use of the internal CAD model as a means of driving the process planning function. Much of the information needed in the design and manufacturing functions is directly related to the geometric shape of the part. This observation has led to the interest in geometric modeling, with the hope that a solution for the geometrical aspects of product modeling will form the basis for producing effective CAD/CAM systems. Present CAD/CAM Systems can be regarded as the logical outcome of this approach. Unfortunately, the information stored in the CAD models is in terms of points, lines, arcs, and solids; as such it is not structured to facilitate CAPP. The gap in the abstraction levels of CAD and CAM domains needs to be bridged to obtain integrated CAD/CAM systems (Wright and Hannam, 1989). CAD systems also lack suitable facilities to associate tolerance information and other applicationbased data (non-geometrical) to the part model. The information in these models is incomplete and low level in nature (Shah and Rogers, 1988b). Hence such models cannot be used directly without further processing for manufacturing applications like CAPP. Another major problem with these modelers is that they allow a very restricted set of operations on the model. All operations must be expressed as Boolean operations. The primitives customarily supported in solid models do not easily lend themselves to the specification of user-definable features. This is particularly true for compound features such as stepped holes, pockets, etc. The steps involved in describing such features may be quite complex. Sometimes the model created by the solid models may not be unique because a part constructed by subtraction of operations can also be created using the corresponding union operations (Joshi et al., 1986). Absence of such uniqueness in part representation may severely affect the subsequent process planning logic. Sometimes, a part represented in a CAD system may not be manufacturable. Hence, it is essential to have modeling systems that can check the design for manufacturability (DFM) and geometrical validity of the parts. Feature-Based Models In order to overcome the above limitations, the concept of using form features (shape elements) for part modeling received the attention of researchers. It is perhaps fair to state that the concept of features was first introduced by researchers in the process of linking design, and manufacturing.
Page 1 and 2:
COMPUTER-AIDED DESIGN, ENGINEERING,
Page 3 and 4:
Library of Congress Cataloging-in-P
Page 5 and 6:
Editor Cornelius T. Leondes, B.S.,
Page 7 and 8:
Chapter 1 Chapter 2 Chapter 3 Chapt
Page 9 and 10:
the implementation of the IPD syste
Page 11 and 12:
In our IPD system implementations,
Page 13 and 14:
2. Specification of analysis-specif
Page 15 and 16:
FIGURE 1.2 base or the design insta
Page 17 and 18:
FIGURE 1.4 System architecture of t
Page 19 and 20:
of a beam is considered for FEA. Th
Page 21 and 22:
FIGURE 1.7 through the control expe
Page 23 and 24:
2. machining facility (e.g., gang m
Page 25 and 26:
from the FBDS. The user also specif
Page 27 and 28:
Depending on the type of feature to
Page 29 and 30:
TABLE 1.6 Tolerance synthesis is de
Page 31 and 32:
FIGURE 1.12 Display of the NC tool
Page 33 and 34:
component geometric entities and va
Page 35 and 36:
FIGURE 1.14 The system architecture
Page 37 and 38:
TABLE 1.10 User Input for the Toler
Page 39 and 40:
TABLE 1.11 Tolerance Specifications
Page 41 and 42:
7. Z. Young and I. R. Groose. A rul
Page 43 and 44:
FIGURE 2.1 Tool paths generated for
Page 45 and 46:
FIGURE 2.2 sented by instances of f
Page 47 and 48:
TABLE 2.1 A CAD-Generated Hole Conf
Page 49 and 50:
FIGURE 2.6 mounted on the rotary ta
Page 51 and 52:
FIGURE 2.8 fixture for the machinin
Page 53 and 54:
FIGURE 2.10 or best-fit, or the cur
Page 55 and 56:
FIGURE 2.11 Linear approximation of
Page 57 and 58:
FIGURE 2.13 Circular approximation
Page 59 and 60:
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 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: FIGURE 5.5 leading to the developme
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 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
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
Page 243 and 244:
References Cheok, B.T. et al. (1994
Page 245 and 246:
include the once forbidden generati
Page 247 and 248:
A temporal matrix (T-Matrix) is pro
Page 249 and 250:
FIGURE 8.1 A basic process. (From R
Page 251 and 252:
FIGURE 8.4(a) Generate a new IG usi
Page 253 and 254:
Note if the pair of nodes are both
Page 255 and 256:
TABLE 8.1 Summary of the Rules Cond
Page 257 and 258:
The correctness of these rules is e
Page 259 and 260:
needs to show that after each full
Page 261 and 262:
ule is violated, a warning should b
Page 263 and 264:
Π1 Π3 Π4 FIGURE 8.8(a) After add
Page 265 and 266:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Page 267 and 268:
A ik � ‘U’ and is in a cycle
Page 269 and 270:
FIGURE 8.12(a) Add [p2 t7 p7 t8 p4]
Page 271 and 272:
FIGURE 8.12(c) Add a token to p8 vi
Page 273 and 274:
FIGURE 8.12(e) Add [p8 t9 p14 t10 p
Page 275 and 276:
FIGURE 8.13 An example of rule TT.0
Page 277 and 278:
FIGURE 8.14 A GPN model of a machin
Page 279 and 280:
FIGURE 8.15(b) The first exclusive
Page 281 and 282:
FIGURE 8.15(d) The last exclusive T
Page 283 and 284:
FIGURE 8.15(f) After entering the a
Page 285 and 286:
FIGURE 8.15(h) Completion of Compon
Page 287 and 288:
FIGURE 8.15(j) Two PP-generations:
Page 289 and 290:
FIGURE 8.15(l) The last exclusive T
Page 291 and 292:
t1 t5 p1 p5 t6 (a) t2 t3 t4 2 2 p2
Page 293 and 294:
FIGURE 8.17 An example of partial f
Page 295 and 296:
Theorem 10: If pi → pk in a synth
Page 297 and 298:
Note that control transitions are o
Page 299 and 300:
t j than the lower at , it indicate
Page 301 and 302:
• Programming logic and VLSI arra
Page 303 and 304:
7. Chao, D. Y. and D. T. Wang, Appl
Page 305 and 306:
53. Villaroel, J. L., J. Martinez,
Page 307 and 308:
q u : the numerator of the least ra
Page 309 and 310:
geometric modeling, engineering ana
Page 311 and 312:
In dimension driven or parametric d
Page 313 and 314:
FIGURE 9.3 configuration of bodies
Page 315 and 316:
FIGURE 9.6 FIGURE 9.7 Geometric Int
Page 317 and 318:
FIGURE 9.8 Intersection of degrees
Page 319 and 320:
will introduce a way to propagate p
Page 321 and 322:
Completeness of Dimensions Complete
Page 323 and 324:
FIGURE 9.12 3-D constraints. © 200
Page 325 and 326:
The distance constraint from pt �
Page 327 and 328:
therefore: Now merge the lists and
Page 329 and 330:
Quantity analysis methods have the
Page 331 and 332:
y a revolute joint. By selecting al
show all

ComputerAided_Design_Engineering_amp_Manufactur.pdf

Create successful ePaper yourself

Delete template?

Save as template?