Description of original award (Fiscal Year 2020, $497,293)
The proposal seeks to characterize and improve the variability between 3D impressed toolmark data acquired from different forensic labs and its effect on objective similarity metrics. This will be accomplished through a round robin study of breech face and firing pin impression measurements.
The 2009 National Academies report  on forensic science in the United States expressed concern that “... the decision of the toolmark examiner remains a subjective decision based on unarticulated standards and no statistical foundation for estimation of error rates.” The report recommended development of scientifically justified protocols that yield an objective determination of a match or non-match with well-characterized confidence limits [1, 2]. In the decade that followed, there has been a major paradigm shift in firearm and toolmark analysis towards the use of 3D topography measurements. The new approach bolsters objectivity through SI traceable measurements. Several manufacturers are now offering specialized 3D microscopes for toolmark measurement which rely on differing measurement principles that each has advantages and challenges.
For impressed toolmarks, there is no comprehensive study that characterizes the resulting differences in 3D data obtained at different forensic labs and their effect on objective similarity scores. This is an important gap in the quest for objective comparison results and quantitative weight of evidence reporting. This gap needs to be addressed to ensure consistency in results among labs and provide associated foundational data for future Daubert hearings.
The proposed research aims to evaluate the effect of measurement source variations on similarity metrics. This will be accomplished through a round-robin study where each lab/instrument measures the same set of 120 cartridge cases fired from four sets of consecutively manufactured firearms. A similar inter-lab study  was conducted for striated toolmarks on bullets where significant deviations were observed between some labs and instruments. These findings were then used to improve measurement protocols, data processing, and analysis methods.
To quantify the differences between labs and technologies, each lab’s measurements will be analyzed using two well-established similarity scores: the normalized Areal Cross Correlation Function (ACCFMAX)  and the number of Congruent Matching Cells (CMC) . The results will be used to generate Known Matching (KM) and Known Non-Matching (KNM) score distributions which can be used to statistically analyze for differences between labs and systems. Results will help improve consistency of measurement results while providing the foundational research data required to defend the future use and interoperability of 3D measurements in case work.