Quantitative assessment of barrel wear from solid copper bullets.

Natali L Hassett, Eric F Law, Stephanie C Luehr
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Abstract

Forensic firearm and toolmark identification is based on the principle that firearms possess and impart individual characteristics on fired ammunition, which can be used to identify a bullet back to the barrel from which it was fired. However, these characteristics may change due to use, presenting challenges for firearm examiners. The goal of this research was to determine how solid copper bullets affect barrel-rifling characteristics after firing 500 rounds through a new Glock 19 Gen 5 pistol with a Glock Marksman Barrel (GMB). Of the 500 bullets, the first 10, last 10, and every tenth in-between were collected in consecutive order for a total of 68 bullets. Three-dimensional (3D) scans of the land engraved areas (LEAs) were generated using the Cadre Forensics Versa system. Cadre Forensics' implementation of the Congruent Matching Profile Segments (CMPS) algorithm was used to compare the linear striation profiles of the LEAs. These data were analyzed using the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) time series test, resulting in a statistically significant decreasing trend (p < 0.01) across CMPS scores from bullet 1 to bullet 500. Based on the algorithm scores and visual comparisons of the LEA scans, this study demonstrated that solid copper bullets eroded barrel characteristics over 500 shots to where an identification likely would not be made when comparing bullet 1 to bullet 500. These results indicate that firearm examiners need to consider the time between a shooting event and firearm recovery to account for potential changes to barrel characteristics that may impact bullet comparison interpretation.

实心铜弹枪管磨损的定量评定。
法医鉴定火器和工具痕迹的原则是,火器具有并赋予发射的弹药个人特征,这些特征可用于识别子弹,直到射出子弹的枪管。然而,这些特征可能会因使用而改变,这给枪支审查员带来了挑战。这项研究的目的是确定固体铜子弹在用格洛克射手枪管(GMB)发射500发子弹后对枪管膛线特性的影响。在这500发子弹中,前10发、后10发和中间每10发子弹中有1发是连续收集的,总共68发。使用Cadre Forensics Versa系统生成了陆地雕刻区域(LEAs)的三维(3D)扫描。使用Cadre Forensics实现的一致性匹配轮廓片段(CMPS)算法来比较LEAs的线性条纹轮廓。使用kwiatkowski - philips - schmidt - shin (KPSS)时间序列检验对这些数据进行分析,结果显示具有统计学上显著的下降趋势(p
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