发表机构
Indian Institute of Science（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对流加工中DEM模拟磨损场计算成本高的问题，提出不确定性引导的主动学习代理框架，仅用13%数据训练的模型可准确预测磨损相关场，不确定性估计可靠且重构磨损场精度高。
AI 中文摘要
在流加工中，工件所受磨损强烈依赖于其在旋转磨料介质中的朝向。确定能实现均匀磨损的合适朝向，需要评估所有可行朝向上的磨损速率场。尽管离散元法（DEM）能准确解析颗粒间的相互作用，但针对新几何结构模拟数百个可行朝向的计算成本极高。我们提出一种不确定性引导的代理框架，可直接从几何结构预测控制侵蚀的三个场：每个三角形的法向冲击速度、切向冲击速度和颗粒冲击通量。这些场通过Finnie磨损模型结合，以重构磨损速率分布。该代理采用深度集成模型，其分歧估计认知不确定性，支持主动学习策略，仅对最不确定的朝向选择性执行DEM模拟。仅使用696个可行朝向上的13%数据进行训练，该代理在法向冲击速度、切向冲击速度和颗粒冲击通量上分别达到0.93、0.89和0.93的斯皮尔曼秩相关系数。此外，预测的不确定性校准良好，可可靠预判预测误差及重构磨损场的保真度，低不确定性朝向上的重构磨损场与DEM的斯皮尔曼秩相关系数最高达0.97，且随不确定性增加呈可控退化。
英文摘要
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 Finnie wear model to reconstruct the wear-rate distribution. The surrogate employs a deep ensemble whose disagreement estimates epistemic uncertainty, enabling an active-learning strategy that selectively performs DEM simulations for the most uncertain orientations. Trained using only $13\%$ of the $696$ feasible orientations, the surrogate achieves Spearman rank correlations of $0.93$, $0.89$, and $0.93$ for the normal impact velocity, tangential impact velocity, and particle impact flux, respectively. Moreover, the predicted uncertainty is well calibrated, reliably anticipating prediction error and the fidelity of the reconstructed wear field, which matches DEM with a Spearman rank correlation of up to $0.97$ for low-uncertainty orientations and degrades in a controlled manner as uncertainty increases.
