Critical Review of Nanoindentation-Based Numerical Methods for Evaluating Elastoplastic Material Properties

Xu Long, Ruipeng Dong, Yutai Su, Chao Chang

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

Abstract

It is well known that the elastoplastic properties of materials are important indicators to characterize their mechanical behaviors and are of guiding significance in the field of materials science and engineering. In recent years, the rapidly developing nanoindentation technique has been widely used to evaluate various intrinsic information regarding the elastoplastic properties and hardness of various materials such as metals, ceramics, and composites due to its high resolution, versatility, and applicability. However, the nanoindentation process of indenting materials on the nanoscale provides the measurement results, such as load-displacement curves and contact stiffness, which is challenging to analyze and interpret, especially if contained in a large amount of data. Many numerical methods, such as dimensionless analysis, machine learning, and the finite element model, have been recently proposed with the indentation techniques to further reveal the mechanical behavior of materials during nanoindentation and provide important information for material design, property optimization, and engineering applications. In addition, with the continuous development of science and technology, automation and high-throughput processing of nanoindentation experiments have become a future trend, further improving testing efficiency and data accuracy. This paper critically reviewed various numerical methods for evaluating elastoplastic constitutive properties of materials based on nanoindentation technology, which aims to provide a comprehensive understanding of the application and development trend of the nanoindentation technique and to provide guidance and reference for further research and applications.

Original languageEnglish
Article number1334
JournalCoatings
Volume13
Issue number8
DOIs
StatePublished - Aug 2023

Keywords

  • contact stiffness
  • dimensionless
  • elastoplastic
  • finite element model
  • indentation
  • load-displacement curves
  • machine learning

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