Increasing Density of 3D-Printed Sandstone through Compaction
Bibliographic record
Abstract
The geomechanical and transport properties of rocks are of great importance to geoscience and engineering, as these properties provide responses to external stresses and flow regimes in the subsurface. Typically, experiments conducted on cores from reservoir formations have a degree of uncertainty, due to the heterogeneous characteristics of rock samples. To combat this uncertainty, binder-jet additive manufacturing (3D printing) is an emerging technology to characterize natural porous media in a repeatable fashion. In this study, the 3D printing sandstone analogue involved sand powder and organic binder to mimic silica grains and cement in natural sandstone. The use of compaction rollers and the adjustment of printing parameters allowed one to test how the porosity and strength of 3D-printed samples can replicate the transport and geomechanical properties of natural sandstone. The densities of samples were increased by ~15% and compressive strength by ~65% with the use of the larger roller. This is a promising alternative to experimental testing to calibrate numerical models in geoscience and engineering. The significance of this approach is to allow for customizable porosity, permeability, and strength in rock samples, while preserving scarce natural rock samples.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".