Three-dimensional indentation test system for observing the distribution of internal mechanical properties in materials
Bibliographic record
Abstract
This paper describes the development of a three-dimensional (3D) indentation test system capable of observing the distribution of mechanical properties in structural materials. Serial sectioning with destructive treatment has traditionally been used as a method for observing microstructure within materials in three dimensions. The serial sectioning methods using precision cutting has attracted particular attention as it enables the observation of large sample volumes. However, those methods can only observe the microstructure as image, not the mechanical properties such as hardness and elastic modulus. To measure the 3D distribution of the mechanical properties of the material, it is effective to combine repeated cutting and indentation tests on each cutting surface. Morever, combining the image observation and mechanical property tests could allow a more sophisticated analysis of the interior of material. To implement this method, we have constructed an indentation test system on a precision machine using a Berkovich indenter, micro-force sensor, and micro-movement stage. In order to achieve a 3D indentation test, it is considered necessary to unify the measurement positions in the depth direction. Furthermore, the unloading rate needs to be controlled in order to carry out stable indentation tests. Therefore, we propose a method of 3D indentation test that can precisely control the maximum depth of indentation and unloading speed. In this paper, we devise a method for driving the constructed system and a method for obtaining data and confirm the accuracy of these methods by experiment. In addition, we determine indentation depth and unloading speed which are suitable for our method by performing indentation tests on a block for ultra-microhardness. Finally, we practice 3D indentation test in which the cutting and indentation tests are repeated on specimens with different mechanical properties in the depth direction. Experimental results show that our indentation test system is appropriate to measure three-dimensional mechanical properties inside the material. • The system was devised to enable indentation test on the precision machining. • The system allowed repeatable indentation tests on the cutting surface. • The indentation test system was built using a piezoelectric stage and force sensor. • This indentation test system could control indentation depth and unloading speed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".