AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials

Patrick Giolando, Sotirios Kakaletsis, Xuesong Zhang, Johannes Weickenmeier, Edward Castillo, Berkin Dortdivanlioglu, Manuel K. Rausch

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load-displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load-displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.

Original languageEnglish
Pages (from-to)6710-6720
Number of pages11
JournalSoft Matter
Volume19
Issue number35
DOIs
StatePublished - 25 Aug 2023

Fingerprint

Dive into the research topics of 'AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials'. Together they form a unique fingerprint.

Cite this