A further look at the nano/micro-indentation method for measuring and ranking Young’s modulus and hardness of materials
Bibliographic record
Abstract
Abstract Determination of the intrinsic Young’s modulus ( E ) is essential for material design and applications. However, the commonly used micro/nano-indentation method does not give accurate intrinsic Young’s modulus, since the measured modulus comes from the damaged zone under the indent tip. In this study, we analyze the intrinsic Young’s modulus or that without local damage caused by indentation, and determine that the intrinsic Young’s modulus can be determined by extrapolation of the E ∼ load curve as the indentation load approaches zero. To support this finding, indentation behaviors of five ceramic materials (Al 2 O 3 , Si 3 N 4 , ZrO 2 , glass and cemented WC/Co) were analyzed and compared with those determined using an acoustic method. The intrinsic Young’s modulus measured, e.g., using the acoustic method, are appropriate for material ranking, while Young’s moduli of different materials measured by indentation under the same load could give misleading information because of different degrees of local damage to the materials under the indenter. Underlying mechanisms for the observed phenomena shown in this novel and unique study are elucidated based on the interatomic bonding. Hardness versus load curves show trends similar to those of Young’s modulus. However, unlike the Young’s modulus, the hardness values measured under the same load can be directly used to rank materials; the reason behind is also discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".