Multi-scale mechanical microscopy via displacement-controlled, high-speed nanoindentation mapping

Abstract Mechanical microscopy allows for the imaging of mechanical properties at multiple length scales. Nanoindentation mapping is usually performed at the microstructural scale, but many manufacturing processes create property variations on larger scales. Using displacement-controlled nanoindentation mapping, multi-scale mechanical microscopy can be performed to connect large-scale variations in mechanical properties to those occurring on the microstructural scale. In this work, a nanoindentation mapping method is applied over three orders of magnitude in lateral resolution on a case-hardened medium-carbon steel. A Gaussian mixture machine-learning algorithm is employed to extract the properties of each phase as a function of length scale. Graphical abstract

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Publication Details

Journal
MRS Communications
Published
2025-09-12
DOI
https://doi.org/10.1557/s43579-025-00817-7
Primary Topic
Metal and Thin Film Mechanics
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi-scale mechanical microscopy via displacement-controlled, high-speed nanoindentation mapping

Jeffrey M. Wheeler, Jörg F. Löffler, Indranil Basu
MRS Communications
Metal and Thin Film Mechanics
article

Multi-scale mechanical microscopy via displacement-controlled, high-speed nanoindentation mapping

Jeffrey M. Wheeler, Jörg F. Löffler, Indranil Basu
article en

Abstract

Abstract Mechanical microscopy allows for the imaging of mechanical properties at multiple length scales. Nanoindentation mapping is usually performed at the microstructural scale, but many manufacturing processes create property variations on larger scales. Using displacement-controlled nanoindentation mapping, multi-scale mechanical microscopy can be performed to connect large-scale variations in mechanical properties to those occurring on the microstructural scale. In this work, a nanoindentation mapping method is applied over three orders of magnitude in lateral resolution on a case-hardened medium-carbon steel. A Gaussian mixture machine-learning algorithm is employed to extract the properties of each phase as a function of length scale. Graphical abstract

MRS CommunicationsVol. 15(5)
ETH Zurich (CH)
Eidgenössische Technische Hochschule Zürich
Openalex Percentile: Top 70%
Metal and Thin Film Mechanics
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Multi-scale mechanical microscopy via displacement-controlled, high-speed nanoindentation mapping — Jeffrey M. Wheeler, Jörg F. Löffler, et al. · MRS Communications (2025) | TGRS Research Map | TGRS