Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields
In stream finishing, the wear experienced by a workpiece depends strongly on its orientation within the rotating abrasive media. Determining suitable orientations to achieve uniform wear requires evaluating the wear-rate field over all feasible orientations. Although the discrete element method (DEM) accurately resolves particle interactions, simulating hundreds of feasible orientations for a new geometry is computationally expensive. We present an uncertainty-guided surrogate framework that predicts, directly from geometry, the three fields governing erosion: per-triangle normal impact velocity, tangential impact velocity, and particle impact flux. These fields are combined through the F...
| Authors | Anand Kumar, Puli Saikiran, Vineet Dawara et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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