Forensic toolmark analysis traditionally relies on subjective human judgment, leading to inconsistencies and lack of transparency. The multitude of variables, including angles and directions of mark generation, further complicates comparisons. To address this, we first generate a dataset of 3D toolmarks from various angles and directions using consecutively manufactured slotted screwdrivers. By using PAM clustering, we find that there is clustering by tool rather than angle or direction. Using Known Match and Known Non-Match densities, we establish thresholds for classification. Fitting Beta distributions to the densities, we allow for the derivation of likelihood ratios for new toolmark pairs. With a cross-validated sensitivity of 98% and specificity of 96%, our approach enhances the reliability of toolmark analysis. This approach is applicable to slotted screwdrivers, and for screwdrivers that are made with a similar production method. With data collection of other tools and factors, it could be applied to compare toolmarks of other types. This empirically trained, open-source solution offers forensic examiners a standardized means to objectively compare toolmarks, potentially decreasing the number of miscarriages of justice in the legal system.

翻译：传统的法庭工具痕迹分析依赖于主观人为判断，导致结果不一致且缺乏透明度。痕迹生成角度和方向等多重变量进一步增加了比对复杂性。为解决此问题，我们首先使用连续制造的平口螺丝刀生成多角度、多方向的三维工具痕迹数据集。通过PAM聚类分析，发现痕迹按工具而非角度或方向形成聚类。利用已知匹配与已知非匹配密度分布，我们建立了分类阈值。通过将Beta分布拟合至密度数据，实现了对新工具痕迹对似然比的推导。该方法经交叉验证达到98%的敏感度与96%的特异度，显著提升了工具痕分析的可信度。本方法适用于平口螺丝刀及采用类似生产工艺的螺丝刀。通过收集其他工具及影响因素的数据，可扩展应用于其他类型工具痕迹的比对。这种基于经验训练的开源解决方案为法庭检验人员提供了标准化客观比对工具，有望减少司法系统中的误判案例。