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Related Experiment Video

Updated: May 28, 2026

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Published on: February 27, 2013

Quantitative Wear Models for Microscale Material Removal.

Kailin Luo¹, Sijing Chen¹, Hai Li¹

¹SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, School of Integrated Circuits, Southeast University, Nanjing 210096, China.

Nanomaterials (Basel, Switzerland)

|May 26, 2026

Summary

This summary is machine-generated.

Predicting microscale material removal wear is challenging due to multiple loss pathways. This review analyzes wear models, highlighting that selecting the right model based on the active wear mechanism and contact state is most reliable.

Keywords:

critical factors microscale material removal prediction accuracy quantitative wear models

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Last Updated: May 28, 2026

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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Published on: February 27, 2013

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry

Mimicking and Measuring Occlusal Erosive Tooth Wear with the "Rub&Roll" and Non-contact Profilometry

Published on: February 2, 2018

Area of Science:

Materials Science
Mechanical Engineering
Tribology

Background:

Microscale material removal involves complex wear mechanisms like plastic deformation, adhesion, attrition, tribochemical reactions, oxidation, and fracture.
No single wear law accurately predicts material loss across all conditions due to varying mechanisms and operating environments.

Purpose of the Study:

To review and analyze quantitative wear models for microscale material removal.
To assess the assumptions, applicable regimes, and failure conditions of various wear models.
To provide guidelines for selecting appropriate wear models based on active mechanisms and contact states.

Main Methods:

Examination of classical wear models (Archard-type, Reye-type).
Analysis of atomistic (Arrhenius-type) and mechanism-specific models (adhesive, tribochemical, oxidation, fracture).
Evaluation of influencing factors: material properties, tool characteristics, operating conditions, and environment.

Main Results:

Wear models differ significantly in their underlying assumptions and applicability.
Model reliability is strongly dependent on the active wear mechanism and the evolving contact state.
Material properties, tool design, operating parameters, and environmental factors collectively impact wear behavior.

Conclusions:

Selecting wear models based on the dominant wear mechanism and contact state enhances prediction reliability.
Current limitations include poor handling of regime transitions, parameter identification challenges, and the gap between atomistic and engineering models.
Future advancements require multi-regime modeling, better integration of coupled effects, and improved in situ characterization.