Enhancing Phase Clustering in Nanomechanical Property Maps of Multiphase Materials Using Kernel-Averaged Mechanical Mismatch

TL;DR

This paper introduces KAMM, a neighborhood-based feature that improves phase clustering in nanomechanical maps of multiphase materials, especially under challenging conditions like low contrast and diffuse interfaces.

Contribution

The study presents KAMM, a novel neighborhood-informed feature, and demonstrates its effectiveness in enhancing phase identification in synthetic and realistic nanomechanical datasets.

Findings

KAMM improves phase separability in clustering.

Enhanced detection of interphase regions.

Increased robustness to noise in mechanical maps.

Abstract

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.