Experimental Study on Mechanical Properties of 3D-Printed Specimens of Iron Oxide, Quartz, and Bedded Composites Under Uniaxial Compression and Indirect Tensile Strength
Bibliographic record
Abstract
The heterogeneity of natural rocks produces increased variations in the results of geomechanical and metallurgical tests making the repeatability of experimental work questionable. Fabricated test specimens have, therefore, become more attractive for fundamental studies. In this study, quasi-identical 3D-printed (3DP) specimens with 10 and 16 mm diameters were fabricated and tested to study material strength and understand the breakage characteristics at a scale more suitable for comminution. Cylinder specimens composed of quartz (named Si) and iron oxide (named Fe), with sorted grains of ∼100-150 μm in the form of homogeneous specimens (3DP-Si and 3DP-Fe) and heterogeneous specimens (bedded) (3DP-SiFeSi, 3DP-SiFe, and 3DP-FeSiFe) were tested. This article presents the results for experimental Unconfined Compressive Strength (UCS) and Brazilian Tensile Strength (BTS) tests. The elastic property was obtained from the UCS tests, while tensile strength was obtained from BTS tests. The strength of 3DP specimens of similar diameter decreases following the types: 3DP-Si (most competent), 3DP-Si-Fe, 3DP-SiFeSi, 3DP-FeSiFe, and 3DP-Fe (less competent). The results show that heterogeneous 3DP specimens were influenced by bedding angle, thickness, and mineral group composition. It also seems that the sequence of mineral composition and the number of beds play a role, rather than the overall grain percentage area for each cylinder, in influencing the strength and variability of fragments. Finally, the brittleness indices for 3DP specimens were calculated as a function of UCS and BTS.
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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 | 0.000 |
| 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) | 0.001 | 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".