Elastic Modulus Prediction from Indentation Using Machine Learning: Considering Tip Geometric Imperfection

Kim, Jong-hyoung; Kim, Dong-Yeob; Lee, Junsang; Kwon, Soon Woo; Kim, Jongheon; Kang, Seung-Kyun; Hong, Sungeun; Kim, Young-Cheon

doi:10.1007/s12540-024-01666-0

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…In recent years, there has been significant progress in methods for assessing local mechanical properties. Notably, instrumented indentation technique (IIT), as a non-destructive method, has demonstrated a strong correlation with standardized mechanical testing procedures [5][6]. Furthermore, IIT facilitates in-site evaluations of in-service structural materials, increasingly emerging as a pivotal technique for micro/nanoscale mechanical property assessments [7].…”

Section: Introductionmentioning

confidence: 99%

A novel method for predicting local plastic mechanical properties of metal structures by integrating microstructure and instrumented indentation technique

Zhang,

Xue

2024

Preprint

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Most important mechanical structures produce complex variations in local material mechanical properties during molding and in-service processes. Accurate testing of material mechanical properties is fundamental to structural integrity assessment. Instrumented indentation technique (IIT) plays a critical role in on-site testing of local structural mechanical properties. However, establishing a precise correlation between the indentation response and uniaxial stress-strain remains challenging in IIT. In this study, we utilized finite element inversion method to explore the quantitative relationship between the indentation response and plastic mechanical properties. Considering that the plastic deformation stages of these metals may exhibit either linear hardening or power-law hardening behavior, a single constitutive model cannot meet the accuracy requirements of IIT. Hence, methods for acquiring mechanical properties suitable for power-law hardening and linear hardening were established respectively based on the analysis of the microstructures of materials exhibiting different hardening behaviors. An integrated IIT approach, considering both microstructure and corresponding material constitutive models, was proposed.Moreover, the influence of elemental composition on microstructure and hardening behavior was investigated. The accuracy of the method was validated with various materials, including austenitic stainless steel, structural steel, and low-alloy steel. Given the universality and reliability of this approach, it is expected to provide theoretical and technical references for the advancement of IIT and structural integrity assessment in important mechanical structures.

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Section: Introductionmentioning

confidence: 99%

A novel method for predicting local plastic mechanical properties of metal structures by integrating microstructure and instrumented indentation technique

Zhang,

Xue

2024

Preprint

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Nanoindentation Response of Structural Self-Healing Epoxy Resin: A Hybrid Experimental–Simulation Approach

Spinelli,

Guarini,

Ivanov

et al. 2024

Polymers

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In recent years, self-healing polymers have emerged as a topic of considerable interest owing to their capability to partially restore material properties and thereby extend the product’s lifespan. The main purpose of this study is to investigate the nanoindentation response in terms of hardness, reduced modulus, contact depth, and coefficient of friction of a self-healing resin developed for use in aeronautical and aerospace contexts. To achieve this, the bifunctional epoxy precursor underwent tailored functionalization to improve its toughness, facilitating effective compatibilization with a rubber phase dispersed within the host epoxy resin. This approach aimed to highlight the significant impact of the quantity and distribution of rubber domains within the resin on enhancing its mechanical properties. The main results are that pure resin (EP sample) exhibits a higher hardness (about 36.7% more) and reduced modulus (about 7% more), consequently leading to a lower contact depth and coefficient of friction (11.4% less) compared to other formulations that, conversely, are well-suited for preserving damage from mechanical stresses due to their capabilities in absorbing mechanical energy. Furthermore, finite element method (FEM) simulations of the nanoindentation process were conducted. The numerical results were meticulously compared with experimental data, demonstrating good agreement. The simulation study confirms that the EP sample with higher hardness and reduced modulus shows less penetration depth under the same applied load with respect to the other analyzed samples. Values of 877 nm (close to the experimental result of 876.1 nm) and 1010 nm (close to the experimental result of 1008.8 nm) were calculated for EP and the toughened self-healing sample (EP-R-160-T), respectively. The numerical results of the hardness provide a value of 0.42 GPa and 0.32 GPa for EP and EP-R-160-T, respectively, which match the experimental data of 0.41 GPa and 0.30 GPa. This validation of the FEM model underscores its efficacy in predicting the mechanical behavior of nanocomposite materials under nanoindentation. The proposed investigation aims to contribute knowledge and optimization tips about self-healing resins.

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Testing, Experimental Design, and Numerical Analysis of Nanomechanical Properties in Epoxy Hybrid Systems Reinforced with Carbon Nanotubes and Graphene Nanoparticles

Spinelli,

Guarini,

Batakliev

et al. 2024

Polymers

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Hybrid nanocomposites incorporating multiple fillers are gaining significant attention due to their ability to enhance material performance, offering superior properties compared to traditional monophase systems. This study investigates hybrid epoxy-based nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs) and graphene nanosheets (GNs), introduced at two different weight concentrations of the mixed filler, i.e., 0.1 wt% and 0.5 wt% which are, respectively, below and above the Electrical Percolation Threshold (EPT) for the two binary polymer composites that solely include one of the two nanofillers, with varying MWCNTs:GNs ratios. Mechanical properties, such as contact depth, hardness, and reduced modulus, were experimentally assessed via nanoindentation, while morphological analysis supported the mechanical results. A Design of Experiments (DoE) approach was utilized to evaluate the influence of filler concentrations on the composite’s mechanical performance, and Response Surface Methodology (RSM) was applied to derive a mathematical model correlating the filler ratios with key mechanical properties. The best and worst-performing formulations, based on hardness and contact depth results, were further investigated through detailed numerical simulations using a multiphysics software. After validation considering experimental data, the simulations provided additional insights into the mechanical behavior of the hybrid composites. This work aims to contribute to the knowledge base on hybrid composites and promote the use of computational modeling techniques for optimizing the design and mechanical performance of advanced materials.

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Elastic Modulus Prediction from Indentation Using Machine Learning: Considering Tip Geometric Imperfection

Cited by 3 publications

References 37 publications

A novel method for predicting local plastic mechanical properties of metal structures by integrating microstructure and instrumented indentation technique

A novel method for predicting local plastic mechanical properties of metal structures by integrating microstructure and instrumented indentation technique

Nanoindentation Response of Structural Self-Healing Epoxy Resin: A Hybrid Experimental–Simulation Approach

Testing, Experimental Design, and Numerical Analysis of Nanomechanical Properties in Epoxy Hybrid Systems Reinforced with Carbon Nanotubes and Graphene Nanoparticles

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