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An improved NG-F model for capturing the three types of indentation size effect
Peina Wang1, Yu Gao2, Peihuan Wang3
1Department of Stomatology, Xi'an No. 3 Hospital, The Affiliated Hospital of Northwest University, Xi'an, 710018, Shaanxi, China.
Scientific Reports
|November 5, 2025
Summary
A new NG-F model accurately predicts three types of indentation size effect (ISE) in materials. The model
Area of Science:
- Materials Science
- Mechanical Engineering
- Solid Mechanics
Background:
- Experimental studies identify three types of Indentation Size Effect (ISE).
- Modeling these three ISE types presents a significant challenge in materials science.
- Existing models struggle to capture the complex, nonlinear relationship between indentation parameters.
Purpose of the Study:
- To systematically analyze the relationship between k (plastic zone to contact radius ratio) and h (indentation depth).
- To develop an improved NG-F model capable of capturing three types of ISE.
- To validate the model's predictive capabilities across different materials.
Main Methods:
- Systematic analysis of the k-h relationship.
- Modification of an exponential decay function to describe k-h.
- Improvement of the NG-F model using the modified exponential decay function.
- Qualitative parameter analysis to understand model behavior.
- Validation against experimental data for three ISE types.
Main Results:
- A modified exponential decay function accurately describes the nonlinear k-h relationship.
- The improved NG-F model successfully captures three types of ISE.
- Model validation shows accurate prediction of ISE for various materials.
- The competition between the relative rates of change of k^3 and h underlies the model's mechanism.
- Predictive accuracy varies, with lower DC values for NR-ISE and C-ISE materials due to differing dislocation mechanisms.
Conclusions:
- The improved NG-F model offers a robust framework for predicting three types of ISE.
- The model's success is attributed to capturing the interplay between k and h.
- Further research is needed to address variations in predictive accuracy for specific material types.

