Characterizing the effects of learning when reverse engineering multiple samples of the same product (Record no. 21065)

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fixed length control field a
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control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240527115456.0
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fixed length control field 240527b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 23458
Author Curtis, Shane K.
245 ## - TITLE STATEMENT
Title Characterizing the effects of learning when reverse engineering multiple samples of the same product
250 ## - EDITION STATEMENT
Volume, Issue number Vol.135(1), Jan
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York
Name of publisher, distributor, etc. ASME
Year 2013
300 ## - PHYSICAL DESCRIPTION
Pagination 1-8p.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4626
Topical term or geographic name entry element Mechanical Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 23459
Co-Author Harston, Stephen P.
773 0# - HOST ITEM ENTRY
Title Journal of mechanical design
Place, publisher, and date of publication New York ASME
856 ## - ELECTRONIC LOCATION AND ACCESS
URL https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/135/1/011002/375667/Characterizing-the-Effects-of-Learning-When?redirectedFrom=fulltext
Link text Click here
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Articles Abstract Database
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Barcode Date last seen Price effective from Koha item type
          School of Engineering & Technology School of Engineering & Technology Archieval Section 2024-05-27 2024-0633 2024-05-27 2024-05-27 Articles Abstract Database
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