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<sup>∗</sup>). 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<sup>∗</sup>. Finally, a neural network model is trained to predict the SRS of flow stress directly from the nano-indentation data, achieving an R<sup>2</sup> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".