Indentation-driven machine learning approach for estimating rate-dependency in cohesive-frictional materials
Bibliographic record
Abstract
The indentation technique offers an efficient, semi-destructive, and high-throughput approach for characterizing mechanical properties across various length scales. While extensively studied for metals and ceramics, its application to time- and pressure-sensitive materials, such as polymers, remains limited. For time-dependent materials, the strain rate sensitivity (SRS) of flow stress has been shown to correlate with the SRS of hardness, with a constraint factor governed by the hardness-to-effective elastic modulus ratio (H/E∗). This study introduces a finite element (FE)-based parametric study to investigate the relationship between the SRS of hardness and the SRS of flow stress in materials exhibiting both time-dependent and pressure-sensitive behavior, incorporating the parameter H/E∗. Finally, a neural network model is trained to predict the SRS of flow stress directly from the nano-indentation data, achieving an R2 score of 0.91, an RMSE of approximately 0.04, and an MAE of around 0.03. The findings of this study provide an integrated data-driven and high-throughput framework for the accelerated characterization of materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".