Effect of Volumetric Energy Density on the Evolution of the Microstructure and the Degradation Behavior of 3D-Printed Fe-Mn-C Alloys from Water-Atomized Powders
Bibliographic record
Abstract
Additive manufacturing of metals opens new doors for innovation in custom-based productions in a wide range of fields, including medicine, even if it introduces new challenges that need to be addressed to guarantee the properties are equal to or superior to those of conventional fabrication processes. In this research, porous, biodegradable Fe-Mn-C alloys were fabricated using a 3D printing technique with four different printing energy densities ranging from 62.5 to 125.0 J/mm3. The effect of printing energy density on the microstructure and degradation behavior was investigated. Lower energy densities resulted in higher pore density and the presence of unmelted powder particles, while the alloy printed at 104.2 J/mm3 exhibited the lowest pore density and the smallest grain size. Degradation tests revealed that the highest pore density in the sample printed at 62.5 J/mm3, and the lowest grain size in the sample printed at 104.2 J/mm3 contributed to faster degradation rates. The alloy printed at the highest energy density, 125.0 J/mm3, demonstrated the largest grain size and the slowest degradation rate. Energy-dispersive spectroscopy and Fourier transform infrared spectroscopy analyses identified manganese carbonate as the primary degradation product, with calcium phosphate forming as a secondary product. These findings provide a significant understanding of the relationship between printing parameters, microstructure, and degradation behavior, which are essential for optimizing the performance of Fe-Mn-C alloys in biodegradable material applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".