Maraging Steel Powder Recycling Effect on the Tensile and Fatigue Behavior of Parts Produced Through the Laser Powder Bed Fusion (L-PBF) Process
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) has advanced the manufacturing industry and has been employed in a wide range of industrial applications, including in aerospace, automotive, medical and die-casting equipment. To ensure the cost-effectiveness of the AM process, unfused powder must be recycled even if its characteristics may change after each cycle, making essential the validation of powder quality and component mechanical performances. Despite the research published to date, predicting the mechanical performance of printed parts issued from reused powder remains challenging since it is dependent on many AM process variables. Until now, no research has looked at the impact of powder recycling on the fatigue behavior of maraging steel components. This study investigates the impact of maraging steel powder reuse on powder characteristics, as well as on the tensile and fatigue properties of printed components. Our results indicate that the powder particle size distribution increased after eight powder recycling, particle morphology showed the presence of aggregates, broken particles, shattered and deformed particles, while powder apparent density remained constant. Powder reusing had no significant impact on the surface roughness of as-built specimens. Although there was a slight decrease in mechanical properties over reuse cycles, tensile and fatigue performance remained globally stable, while the standard deviation of fatigue stress became narrower after eight cycles. Finally, fractography revealed that the fatigue fracture surfaces of components manufactured from an eight-time recycled powder have more fusion defects and carbon inclusions than the parts made from virgin powder.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".