Forensic handheld toolmark examiners currently compare toolmarks (e.g. scratch marks on wire found as part of an explosive device, or on a door frame after someone broke into a house) by observing 2D images and subjectively deciding whether two marks were produced by the same source. Researchers have suggested that using algorithms could reduce the amount of human error. we present a clustering algorithm that performs very well on a dataset of 3D screwdriver marks generated in our lab under controlled conditions and a factorial design. The data allows us to cluster by screwdriver based on what force, angle, and side was used to make the mark. We hope our algorithm can be used by laboratories and help strengthen forensic science.