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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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Figures

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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