利用核平均机械不匹配增强多相材料纳米机械特性图的相聚类
Enhancing Phase Clustering in Nanomechanical Property Maps of Multiphase Materials Using Kernel-Averaged Mechanical Mismatch
AI总结：
通过引入核平均机械不匹配方法，提升多相材料纳米机械特性图中相聚类的准确性与鲁棒性。
摘要：
本工作提出了一种新的方法，以改进多相材料纳米机械特性图中的相识别，如通过纳米压痕或原子力显微镜（AFM）获得的数据。在验证这些数据的聚类策略时，主要挑战在于实验测量中缺乏真实的相标签，以及过于简单的合成数据集倾向于人为提高算法性能。为解决这一差距，我们构建了受控但非平凡的合成基准，具有可调的机械对比度、渐变界面、弯曲边界和弥散形态，从而能够严格而真实地评估聚类鲁棒性。传统仅基于弹性模量（E）和硬度（H）的聚类方法在机械对比度低或存在弥散相界面时往往难以分离相。我们引入了核平均机械不匹配（KAMM），这是一种基于邻域的特征，通过在（E，H）空间中将每个点与其邻居进行比较来量化局部机械异质性。当将其纳入三维聚类空间（E，H，KAMM）时，该框架提高了相分离性，增强了相界面检测，并提高了对噪声的鲁棒性。通过在现实对比条件下实现更可靠的机械域分割，所提出的方法促进了代表性体积元素（RVEs）的生成，并支持在异质微观结构中更准确地提取相特异性性质。
英文摘要：
This work presents a novel approach to improve phase identification in nanomechanical property maps of multiphase materials, such as those obtained by nanoindentation or atomic force microscopy (AFM). A major challenge in validating clustering strategies for these data is the lack of ground-truth phase labels in experimental measurements, along with the tendency of overly simplistic synthetic datasets to artificially inflate algorithm performance. To address this gap, we construct controlled yet non-trivial synthetic benchmarks with tunable mechanical contrast, graded interfaces, curved boundaries, and diffuse morphologies, enabling rigorous and realistic evaluation of clustering robustness. Conventional clustering methods based only on elastic modulus (E) and hardness (H) often struggle to separate phases when mechanical contrast is low or when diffuse interphase regions are present. We introduce the Kernel-Averaged Mechanical Mismatch (KAMM), a neighborhood-informed feature that quantifies local mechanical heterogeneity by comparing each point to its neighbors in (E, H) space. When incorporated into a three-dimensional clustering space (E, H, KAMM), this framework improves phase separability, enhances interphase detection, and increases robustness to noise. By enabling more reliable segmentation of mechanical domains under realistic contrast conditions, the proposed method facilitates the generation of representative volume elements (RVEs) and supports more accurate extraction of phase-specific properties in heterogeneous microstructures.
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