Research Papers

Characterizing the Effects of Learning When Reverse Engineering Multiple Samples of the Same Product

[+] Author and Article Information
Shane K. Curtis

Research Assistant
e-mail: shanekcurtis@gmail.com

Stephen P. Harston

Ph.D. Candidate
e-mail: sharston@gmail.com

Christopher A. Mattson

Associate Professor
e-mail: mattson@byu.edu
Department of Mechanical Engineering,
Brigham Young University,
Provo, UT 84602

1Corresponding author.

Contributed by the Design Innovation and Devices of ASME for publication in the Journal of Mechanical Design. Manuscript received November 1, 2011; final manuscript received September 12, 2012; published online November 21, 2012. Assoc. Editor: Janis Terpenny.

J. Mech. Des 135(1), 011002 (Nov 21, 2012) (8 pages) Paper No: MD-11-1443; doi: 10.1115/1.4007918 History: Received November 01, 2011; Revised September 12, 2012

Reverse engineering is the process of extracting information about a product from the product itself. An estimate of the barrier and time to extract information from any product is useful for the original designer and those reverse engineering, as both are affected by reverse engineering activities. The authors have previously presented a set of metrics and parameters to estimate the barrier and time to reverse engineer a product once. This work has laid the foundation for the developments of the current paper, which address the issue of characterizing the reverse engineering time and barrier when multiple samples of the same product are reverse engineered. Frequently in practice, several samples of the same product are reverse engineered to increase accuracy, extract tolerances, or to gather additional information from the product. In this paper, we introduce metrics that (i) characterize learning in the reverse engineering process as additional product samples are evaluated and (ii) estimate the total time to reverse engineer multiple samples of the same product. Additionally, an example of reverse engineering parts from a control valve is introduced to illustrate how to use the newly developed metrics and to serve as empirical validation.

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Schnaars, S. P., 1994, Managing Imitation Strategies: How Later Entrants Seize Markets From Pioneers, The Free Press, NY.
Zhou, K. Z., 2006, “Innovation, Imitation, and New Product Performance: The Case of China,” Ind. Mark. Manage., 35, pp. 394–402. [CrossRef]
Urban, G. L., Carter, T., Gaskin, S., and Mucha, Z., 1986, “Market Share Rewards to Pioneering Brands: An Emperical Analysis and Strategic Implications,” Manage. Sci., 32, p. 6. [CrossRef]
Greenstein, S., 2004, “Imitation Happens,” Technical Report, IEEE Computer Society.
Kim, L., 1997, Imitation to Innovation: The Dynamics of Korea’s Technological Learning, Harvard Business School Press, Boston, MA.
McLoughlin, I., 2008, “Secure Embedded Systems: The Threat of Reverse Engineering,” ICPADS’08: Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems, IEEE Computer Society, pp. 729–736.
Shapiro, C., 1985, “Patent Licensing and R & D Rivalry,” Am. Econ. Rev., 75, pp. 25–30.
Nelson, R., and Winter, S., 1982, An Evolutionary Theory of Economic Change, Belknap Press, Cambridge, Massachusetts.
Macmillan, I., McCaffery, M. L., and van Wijk, G., 1985, “Competitors’ Responses to Easily Imitated New Products-Exploring Commercial Banking Product Introductions,” Strategic Manage. J., 6(1), pp. 75–86. [CrossRef]
Mansfield, E., Schwartz, M., and Wagner, S., 1981, “Imitation Costs and Patents: An Empirical Study,” Economic J., 91(364), pp. 907–918. [CrossRef]
Knight, D., 2011, “Return on Investment Analysis for Implementing Barriers to Reverse Engineering and Imitation,” Master’s thesis, Brigham Young University, Provo, UT.
Harston, S. P., and Mattson, C. A., 2010, “Metrics for Evaluating the Barrier and Time to Reverse Engineer a Product,” ASME J. Mech. Des., 132, p. 041009. [CrossRef]
Varady, T., Martin, R. R., and Cox, J., 1997, “Reverse Engineering of Geometric Models—An Introduction,” Comput.-Aided Des., 29(4), pp. 255–268. [CrossRef]
Thompson, W. B., Owen, J. C., de St. Germain, H. J., Stark, S. R., and Henderson, T. C., 1999, “Feature-Based Reverse Engineering of Mechanical Parts,” IEEE Trans. Rob. Autom., 15(1), pp. 57–66. [CrossRef]
Montgomery, D. C., Runger, G. C., and Hubele, N. F., 2006, Engineering Statistics, 3rd ed., John Wiley & Sons, New York.
Kaisarlis, G. J., Diplaris, S. C., and Sfantsikopoulos, M. M., 2007, “Position Tolerancing in Reverse Engineering: the Fixed Fastener Case,” Proc. Inst. Mech. Eng. Part B. J. Eng. Manuf., 221, pp. 457–465. [CrossRef]
Chase, K. W., and Parkinson, A. R., 1991, “A Survey of Research in the Application of Tolerance Analysis to the Design of Mechanical Assemblies,” Res. Eng. Des., 3, pp. 23–37. [CrossRef]
Chase, K. W., 1999, “Tolerance Allocation Methods for Designers,” Technical Report, ADCATS Report No. 99–6.
Kaisarlis, G. J., Diplaris, S. C., and Sfantsikopoulos, M. M., 2007, “Geometrical Position Tolerance Assignment in Reverse Engineering,” Int. J. Comput. Integr. Manuf., 21(1), pp. 89–96. [CrossRef]
Jamshidi, J., Mileham, A. R., and Owen, G. W., 2006, “Dimensional Tolerance Approximations for Reverse Engineering Applications,” Proceedings of the 9th International Design Conference (Design 2006), The Design Society, pp. 855–862.
Kaisarlis, G. J., Diplaris, S. C., and Sfantsikopoulos, M. M., 2000, “A Knowledge-Based System for Tolerance Allocation in Reverse Engineering,” Proceedings of the 33rd International Matador Conference.
Anonymous, 2006, U.S. Army Reverse Engineering Handbook (Guidlines and Procedures), Department of Defense, Washington DC.
Prerau, M. J., Smith, A. C., Eden, U. T., Kubota, Y., Yanike, M., Suzuki, W., Graybiel, A. M., and Brown, E. N., 2009, “Characterizing Learning by Simultaneous Analysis of Continuous and Binary Measures of Performance,” J. Neurophysiol., 102, pp. 3060–3072. [CrossRef] [PubMed]
Usher, M., and McClelland, J., 2001, “The Time Course of Perceptual Choice: The Leaky, Competing Accumulator Model,” Psychol. Rev., 108(3), pp. 550–592. [CrossRef] [PubMed]
Adler, P. S., and Clark, K. B., 1991, “Behind the Learning Curve: A Sketch of the Learning Process,” Manage. Sci., 37(3), pp. 267–181. [CrossRef]
Argote, L., 1999, Organizational Learning: Creating, Retaining, and Transfering Knowledge, Kluwer Academic, Boston.
Curtis, S. K., Harston, S. P., and Mattson, C. A., 2011, “The Fundamentals of Barriers to Reverse Engineering and Their Implementation Into Mechanical Components,” Res. Eng. Des., 22(4), pp. 245–261. [CrossRef]
Rizzoni, G., 2004, Principles and Applications of Electrical Engineering, McGraw-Hill, New York.
Fogiel, M., 2002, Basic Electricity, Research and Education Association, Piscataway, NJ.
Anderson, N., 2011, “Characterization of the Initial Flow Rate of Information During Reverse Engineering,” Master’s thesis, Brigham Young University, Provo, UT.
Flowserve Corporation, 2006, 3400IQ Digital Positioner FCD LGENIM3401-00 – 06/06 User Instructions.


Grahic Jump Location
Fig. 1

Unextracted information in a product as a function of time—the curves for multiple reverse engineering samples are compared

Grahic Jump Location
Fig. 2

The process for predicting the time to reverse engineer multiple samples of the same product

Grahic Jump Location
Fig. 3

Flowserve 3400IQ digital positioner with spool block valve and spool shown—image adapted from Ref. [31]

Grahic Jump Location
Fig. 4

Unextracted geometric dimensions of the spool as a function of time for individual # 1—samples 2, 5, 10, and 30 are shown



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