On the Effect of Process Parameters on Manganese Content and Shape Memory Behavior of LPBF-Fabricated Fe-Based Alloys
Bibliographic record
Abstract
In this study, the effect of laser process parameters on Mn content and the shape memory behavior of Fe-20Mn-5Si-5Ni-9Cr-0.8V-0.2C-0.1N were studied. Laser Powder Bed Fusion (LPBF) method was employed to fabricate the parts using an EOS M290 system. The process aimed to achieve fully dense parts (>99.9%) with different Mn contents (between 15 to 20 wt%). Mn content measurements done with XRF revealed that scanning strategy (including stripe width, strip overlap, scan length, and rotation angle) significantly influence the Mn content in a part, despite being printed with identical power, speed, hatch spacing and layer thickness. The underlying causes were analyzed in the context of melt pool dynamics and vaporization tendencies of Mn during repeated scan path exposures. Next, differential scanning calorimetry (DSC) was employed to study the influence of Mn variation on shape memory transformation temperatures. Clear shifts in martensitic start (Ms), finish (Mf) and austenitic start (As), and finish (Af) temperatures were observed, with higher Mn content lowering the transformation range. Mechanical properties assessment showed that high-Mn samples had slightly reduced the strength, but improved ductility compared to low-Mn samples. Hardness measurements showed minor variation but aligned with the observed microstructural differences. Finally, the shape memory effect (SME) was assessed using a three-point bending recovery test. High-Mn samples exhibited significantly different behavior compared to low Mn samples, owing higher stability of Austenite in the high Mn samples. Moreover, in-situ heat treatment occurring due to high energy input in low-Mn samples resulted in formation of carbides and affected the shape memory behavior in the low Mn sample as compared to carbide free high Mn sample. This study highlights the sensitivity of Fe-Mn-Si shape memory alloys to Mn content and demonstrates how LPBF process parameters can be utilized to tailor chemical composition and mechanical behavior of this group of alloys.
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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 | 0.000 | 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".