  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.
