论文标题
使用可解释的深度学习模型桥接保真度以预测纳米凹痕尖端半径
Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models
论文作者
论文摘要
随着对微型结构和功能材料的需求增加了，对精确材料的需求也扩大了。纳米引导是一种流行的方法，可用于测量材料机械行为，可以实现高通量实验，在某些情况下还可以通过扫描提供凹痕区域的图像。缩进和扫描都会导致尖端磨损，从而影响测量值。因此，需要精确的尖端半径表征来改善数据评估。介绍了一种数据融合方法，该方法使用有限元仿真和实验数据，以有意义的方式使用可解释的多保真深度学习方法来估算尖端半径。通过解释机器学习模型，可以证明这些方法能够准确捕获物理压痕现象。
As the need for miniaturized structural and functional materials has increased,the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior which enables high-throughput experiments and, in some cases, can also provide images of the indented area through scanning. Both indenting and scanning can cause tip wear that can influence the measurements. Therefore, precise characterization of tip radii is needed to improve data evaluation. A data fusion method is introduced which uses finite element simulations and experimental data to estimate the tip radius in situ in a meaningful way using an interpretable multi-fidelity deep learning approach. By interpreting the machine learning models, it is shown that the approaches are able to accurately capture physical indentation phenomena.
