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Manipulative Virtual Tools for Tool Mark Characterization

Award Information

Award #
2009-DN-R-119-1
Funding Category
Competitive
Status
Closed
Funding First Awarded
2009
Total funding (to date)
$360,000

Description of original award (Fiscal Year 2009, $360,000)

Challenges to objective optical comparison of markings (e.g. firearms or tool marks) by an expert examiner have caused a need for accurate and reliable quantitative data analysis tools based on quantitative measurements to improve the scientific basis of toolmark identification. Tool mark examinations are inherently more difficult to quantify than firearms since standard conditions (e.g. shape, size) do not exist and a mark can vary depending upon the angle of attack, applied pressure, or twist of the tool employed. The “mark” left by the tool may be a series of markings of different size, depth, length, etc. What is desired is a quantitative technique where a single data set related to the employed tool can be matched to all of the disparate markings left on a surface. The goal of this proposal is to develop a methodology whereby a threedimensional (3-D) computer simulation of a tool tip is generated. This “virtual tool” can then be used to produce “virtual toolmarks” - a series of predicted markings where the applied force, twist of the tool, and angle of attack of the tool tip can be varied. Quantitative 3-D data from the suspected tool and evidence toolmark will be acquired and a virtual reality program developed that takes this data and reconstructs a “virtual tool” for computer manipulation to create “virtual tool marks”. Since the “virtual tool” can be manipulated to produce a range of markings, the exact parameters required to obtain the best possible match to the actual tool mark can be found. Duplicate marks based on these results can then be statistically compared and ranked on the basis of quantitative measurements. If successful the project should increase the accuracy and validity of toolmark identification by providing quantitative, scientifically testable and verifiable data.

Date Created: December 15, 2020