Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Constrained Neural Network
arXiv:2604.27775v2 Announce Type: replace-cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases, and other volume-constrained materials, but the measured hardness is inflated by the indentation size effect (ISE). Classical corrections such as Nix-Gao require a deep linear regime and fail when only shallow measurements are accessible. We present a data-efficient workflow that recovers a high-load reference hardness directly from shallow, size-affected indentation data. Over 700 indentations on three certified steel reference blocks (2-6.5 G
The rapid advancement in machine learning and neural network techniques is enabling data-efficient solutions for complex material science challenges.
This development allows for more accurate and efficient mechanical characterization of materials, which is crucial for advanced manufacturing and engineering design.
Traditional limitations in material characterization, particularly for shallow measurements and thin films, can now be overcome with less data.
- · Material scientists
- · Manufacturing industry
- · AI/ML researchers
- · Traditional material testing methods
- · Industries reliant on extensive, costly material characterization
Improved material selection and design efficiency in various engineering applications.
Accelerated development of new materials with optimized mechanical properties.
Potential for AI-driven automated material discovery and characterization pipelines.
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