Towards sustainable additive manufacturing: Enhanced productivity via numerical-experimental melt pool engineering in laser powder bed fusion of Ti-alloy
Bibliographic record
Abstract
Laser Powder Bed Fusion (LPBF) offers a promising approach for creating metallic components with tailored properties. However, low production speed and high manufacturing costs remain challenges. Enhancing the building rate improves economic efficiency and reduces energy consumption, contributing to a greener environment. This study proposes a numerical-experimental approach to increase the LPBF build rate for Ti-5553 while maintaining part quality and performance. High-speed scanning parameters that produce parts with high density, low surface roughness, and sufficiently large melt pools were chosen from the optimized process parameters. A time-efficient and cost-effective simulation, developed by the authors, was used to predict the effects of process parameters on melt pool geometry and the possibility of lack-of-fusion defect formation due to increased hatch distance and layer thickness. This predictive analysis led to the selection of two parameter sets for fast and dense sample printing. The results indicate that increasing the hatch distance and the layer thickness enhances productivity by approximately 54 % and 104 %, respectively, compared to the original optimized parameters. The printed samples with fast-print parameters exhibit an excellent density (>99.98 %) and a favorable surface roughness (<17 μm). While these samples demonstrate a slightly finer grain size than the reference, their tensile strength, elongation, and hardness differ by less than 4 %, confirming negligible mechanical impact. These findings demonstrate a viable strategy for significantly improving LPBF efficiency while maintaining part quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".