A 3D-printed portable microindenter for mechanical characterization of soft materials
Bibliographic record
Abstract
In this paper, we report a 3D-printed portable microindenter for measuring the mechanical properties of soft materials. The system is composed of three major components: (i) a Z-axis stage, (ii) a force-sensing indentation probe, and (iii) a data acquisition and control system. We construct most of the system parts (including the force sensing probe) by 3D printing, and perform mechanical design and analysis to guarantee the mechanical robustness of the system. The system is capable of performing high-precision, non-destructive material indentation testing with displacement and force sensing resolutions as low as 0.625 μm and 73 μN, respectively. The force-deformation data obtained during indentation are used to determine the mechanical characteristics of a soft material sample based on a Hertz's mechanical model. We also integrate several user-friendly features into the system, including a touch screen and customized software for convenient user-machine interactions. Using the microindenter, we perform elastic testing of polydimethylsiloxane (PDMS) with three base/curing agent ratios (w/w), and achieve consistent results of the materials' Young's moduli in good agreement with the previous results reported in the literature. This proves that our 3D-printed microindenter, although being custom-made and low-cost, maintains the accuracy and repeatability required for soft material testing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".