Systematic investigation into laser powder bed fusion of Ti-5553 through single-track and multi-layer studies for tailored manufacturing solutions
Bibliographic record
Abstract
Laser Powder Bed Fusion (LPBF) is recognized as an appealing fabrication process for producing metallic parts with customized properties. In the current research, a comprehensive approach is employed to systematically correlate single-track and multi-layer fabrication, aiming to generate a reliable process map, assess the effect of process parameters on the properties of LPBF-made Ti-5553, and guide the manufacturing of tailored structures. Based on single-track morphology, melt pool geometry, and multi-layer density, 30 combinations of laser power and scanning speed were categorized into three groups to identify the desirable process parameters. The investigation of single-tracks and multi-layers reveals that deeper melt pools, created with higher energy input, result in a more elongated grain structure, higher α phase content, increased strength and hardness, and reduced ductility. It is observed that achieving higher ductility involves a slight decrease in strength. Specifically, a substantial increase of ∼65% in ductility occurs with only a ∼3.5% reduction in strength. Also, it is found that the volumetric energy density ( VED ) alone is not sufficient as a design parameter, and the significant process parameters (e.g., laser power and scanning speed) should be considered, as two samples with the same VED yield different properties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".