ComputerAided_Design_Engineering_amp_Manufactur.pdf

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firms will be penalized for not training their managers. It might be speculated that the inability of management to truly understand the CAD system may create long-term compromises in the firm’s ability to find innovative ways of using CAD or in the ability of management to manage CAD workers effectively. Thus, de-skilling benefits in the short term may imply strategic costs in the long term. The results of this study further suggest that firms concerned with gaining specific competitive advantages through CAD can choose the method of training to achieve their goals. Clearly, firms that wish to leverage the de-skilling process should select more informal training techniques, while firms concerned with training all levels of the firm should opt for a more formal program. Thus, the choice of the training strategy becomes a deliberate decision aimed at achieving strategic objectives, rather than a predestined selection, determined only by the nature of the organizational structure. 6.6 Conclusions The implementation of a new CAD system presents many issues which the firm must confront if the true potential of the system is to be realized. The discussion here provides general empirical support for the argument that the type of training program chosen by a firm is closely related to the organizational structure. For many firms, the choice of training program involves determining the best fit to the specific organizational structure, as well as considering the required level of resource commitment. The costbenefit analysis for a CAD system often focuses on financial requirements during the pre-adoption stage and on efficiently achieving the benefits of CAD during the implementation stage. De-skilling has been cited as one benefit that has financial implications for the firm as well as the potential to enhance value added by design work. These findings suggest that if firms are particularly concerned with benefits associated with the de-skilling process, then it might be in their best interest to use more informal methods to train their workers. One mechanistic firm in the sample adopted informal training methods in order to take advantage of the de-skilling process. It might be suggested that other mechanistic firms with similar goals alter their training format to do the same. By allowing for informal, less-constrained flows of information, these mechanistic firms might be able to achieve the same quality of CAD education that their organic counterparts seem to enjoy. Organic firms that are concerned with management training, on the other hand, might be advised to pursue more formal methods of educating CAD managers. As noted above, this training may promote the effective management of the CAD system as well as the development of a strategic vision for the use of CAD. Finally, the apparent difference between training programs for mechanistic and organic CAD firms suggests many interesting questions. For example, would it be advantageous for mechanistic firms to adopt more organic structures, at least in their CAD design groups, or is the simple adoption of different training methods enough? Is it ever cost-effective for a firm to use a hybrid training program consisting of both formal and informal methods, as this research seems to suggest? Are there other CAD training strategies which would emerge if the study were broadened to encompass different industries? These questions merit future research. References 1. S. A. Abbas and A. Coultas, Skills and Knowledge Requirements for CAD/ CAM in CAD/CAM, in Education and Training: Proceedings of the CAD ED 83 Conference, P. Arthur (Ed.), Anchor Press, 1984. 2. P. S. Adler, CAD/CAM: Managerial Challenges and Research Issues, IEEE Trans. Eng. Mgmt. Vol. 36, No. 3, pp. 202–215, 1989. 3. P. S. Adler, New Technologies, New Skills, California Management Review, Vol. 29, No. 1, pp. 9–28, 1986. 4. P. Attewell, The De-skilling Controversy, Work and Occupations, Vol. 14, No. 3, pp. 323–346, 1987. 5. T. T. Baldwin and J. K. Ford, Transfer of Training: A Review and Directions for Future Research Personnel Psychology, Vol. 41, pp. 63–84, 1988.
6. C. A. Beatty, Tall Tales and Real Results: Implementing a New Technology for Productivity, Business Quarterly, Vol. 51, No. 3, pp. 70–74, 1986. 7. C. A. Beatty and J. R. Gordon, Barriers to the Implementation of CAD/CAM Systems, Sloan Management Review, Vol. 29, No. 4, pp. 25–33, 1988. 8. P. Botsman and P. Rawlinson, Trade Unions and New Technology: Talking to Pelle Ehn, Work and People. Vol. 12, No. 1, pp. 8–10, 1986. 9. H. Braverman, Labor and Monopoly Capital: The Degradation of Work in the 20th Century, New York: Monthly Review Press, 1974. 10. R. D. Bretz and R. E. Thompsett, Comparing Traditional and Integrative Learning Methods in Organizational Training Programs, Journal of Applied Psychology, Vol. 77, No. 6, pp. 941–951, 1992. 11. L. S. Brooks and C. S. Wells, Role Conflict in Design Supervision, IEEE Trans. Eng. Mgmt., Vol. 36, No. 4, pp. 271–282, 1989. 12. T. Burns and G. M. Stalker, The Management of Innovation, London: Tavistock Press, 1961. 13. J. R. Carter, R. M. Monczka, K. S. Clauson, and T. P. Zelinski, Education and Training for Successful EDI Implementation, Journal of Purchasing and Materials Management, pp. 13–19, 1987. 14. T. D. Cook and D. T. Campbell, Quasi-Experimentation: Design and Analysis Issues for Field Settings, Houghton Mifflin Company, 1979. 15. J. A. Courtright, G. T. Fairhurst, and L. E. Rogers, Interaction Patterns in Organic and Mechanistic Systems, Academy of Management Journal, Vol. 32, No. 4, pp. 773–802, 1989. 16. G. C. Covin and D. P. Slevin, Strategic Management of Small Firms in Hostile and Benign Environments, Strategic Management Journal, Vol. 10, pp. 75–87, 1989. 17. P. Davis and M. Wilkof, Scientific and Technical Information Transfer for High Technology: Keeping the Figure on its Ground, R&D Management, Vol. 18, No. 1, pp. 45–58, 1988. 18. W. D. Engelke, How to Integrate CAD/CAM Systems: Management and Technology, New York: Marcel Dekker Inc., 1987. 19. J. Forslin and B. M. Thulestedt, Computer Aided Design: A Case Strategy in Implementing a New Technology, IEEE Trans. Eng. Mgmt. Vol. 36, No. 3, pp. 191–201, 1989. 20. M. Gist, B. Rosen, and C. Schwoerer, The Influence of Training Method and Trainee Age on the Acquisition of Computer Skills, Personnel Psychology, Vol. 41, pp. 255–265, 1988. 21. I. L. Goldstein, Training in Work Organizations, Annual Review of Psychology, Vol. 31, pp. 229–272, 1980. 22. P. S. Goodman and S. M. Miller, Designing Effective Training through the Technological Life Cycle, National Productivity Review, Vol. 9, No. 2, pp. 169–177, 1990. 23. S. W. Hubbard, CAD/CAM: Applications for Business, Phoenix, AZ: Oryx Press, 1985. 24. K. Hughes, Office Automation: A Review of the Literature, Industrial Relations, Vol. 44, No. 3, pp. 654–677, 1989. 25. D. F. Jennings and S. L. Seaman, Aggressiveness of Response to New Business Opportunities Following Deregulation: An Empirical Study of Established Financial Firms, Vol. 5, No. 3, pp. 177–189, 1990. 26. G. L. Lee, Managing Change with CAD and CAD/CAM, IEEE Trans. Eng. Mgmt. Vol. 36, No. 3, pp. 227–233, 1989. 27. J. K. Liker and M. Fleisher, Implementing Computer Aided Design: The Transition of Nonusers, IEEE Trans. Eng. Mgmt. Vol. 36 No. 3, pp. 180–190, 1989. 28. A. N. Link and R. W. Zmud, Organizational Structure and R&D Efficiency, R&D Management, Vol. 16, No. 4, 317–323, 1986. 29. A. Majchrzak, A National Probability Survey on Education and Training in CAD/CAM, IEEE Trans. Eng. Mgmt., Vol. 33, No. 4, pp. 197–206, 1986. 30. A. Majchrzak and H. Salzman, Social and Organizational Dimensions of Computer-Aided Design, IEEE Trans. Eng. Mgmt., Vol. 36, No. 3, pp. 174–179, 1989. 31. F. Manske and H. Wolfe, Design Work in Change: Social Conditions and Results of CAD Use, in Mechanical Engineering, IEEE Trans. Eng. Mgmt. Vol. 36, No. 4, pp. 282–297, 1989.
Page 1 and 2:
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
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the previously stored process plan
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FIGURE 5.7 Techniques of defining a
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Feature Recognition and Extraction
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in relation to another feature (e.g
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FIGURE 5.9 Framework for building a
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FIGURE 5.10 Process plan content.
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FIGURE 5.12 Schematic sketch of the
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Several techniques of defining a fe
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TABLE 5.1 Data Structure for Repres
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FIGURE 5.16 Classification of the t
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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: informal training programs, felt th
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
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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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