INTRODUCTION: Techniques are proposed here to understand how ski edge sharpening improves performance and increases risks of disfiguring and life-threatening lacerations (Holden et al. 2022). Previous models for edge snow interactions were from machining by chip formation for movement perpendicular to an edge (Brown 2009), essentially sideslipping. In contrast, slicing is studied for movement parallel to an edge, essentially carving. Skidded turns can have both. Slicing mechanisms have been studied, and edges characterized for teeth (Frazzetta 1988), knives (McGorry 2005), and stone tools (Stemp et al. 2019), although these are essentially blades having narrow solid angles. Edge sharpness has been characterized traditionally by their solid angles and estimating edge radii, e.g., Denkena (2011) for metal cutting tools with large solid angles, similar to ski edges. METHODS: The proposed techniques are based on reductionism applied to recent advances in topographic principles. Topographic studies should be based on scales and characterizations pertinent to fundamental interactions comprising macroscopic phenomena of interest (Brown 2021). Topographic measurements must include relevant scales and statistics appropriate to situations of interest. RESULTS: Ski edges` measured topographies can be characterized for sharpness to quantify differences between hand and electrical side tuning and compared with slicing performance. Multiscale curvature characterizations (Stemp et al. 2019) can be used as functions of position perpendicular to edges to capture nuances missed by traditional methods. In addition, profiles along edges can be analyzed for serrations occurring over scales from micrometers to millimeters. Pertinent scales can be found by multiscale regression analyses and univariant ANOVA that compare sharpening methods. Preliminary measurements, characterizations, and tests have been developed. By conference time, more specific results should be available. DISCUSSION/CONCLUSION: Multiscale analysis methods and pertinent geometric characterizations have proven successful in finding strong correlations between topographies and their processing or performance and in confidently discriminating topographies based on processing and performance (ASME B46.1 2019). It is reasonable to expect that this should extend to ski edge topographies as well.
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