Machine learning corrects indentation size effect in steels with small datasets

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 3 sources

Researchers have developed a data-efficient method for correcting the indentation size effect (ISE) in steels using machine learning and physics-guided augmentation. By augmenting a dataset of approximately 700 experimental indentations, they trained a constrained neural network to predict reference hardness, achieving high accuracy ($R^2 > 0.98$) and stable estimates even in the shallow indentation regime. This approach offers a pathway for data-efficient mechanical characterization of volume-constrained materials. AI

Summary written by gemini-2.5-flash-lite from 3 sources. How we write summaries →

IMPACT Provides a novel data-efficient workflow for materials characterization, potentially applicable beyond steels.

RANK_REASON Academic paper detailing a new machine learning methodology for materials science.

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COVERAGE [3]

arXiv cs.LG TIER_1 · Radmir Karamov, Tagir Karamov · 2026-05-01 04:00

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

arXiv:2604.27775v1 Announce Type: cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hardness is often inflated by the indentation size effect (ISE), contact-area errors…
arXiv cs.LG TIER_1 · Tagir Karamov · 2026-04-30 12:12

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hardness is often inflated by the indentation size effect (ISE), contact-area errors and tip-geometry artifacts. Classical ISE correct…
Hugging Face Daily Papers TIER_1 · 2026-04-30 12:12

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hardness is often inflated by the indentation size effect (ISE), contact-area errors and tip-geometry artifacts. Classical ISE correct…

COVERAGE [3]

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

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