A bioinspired method for fatigue crack path tailoring and life enhancement in metals via additive manufacturing
Bibliographic record
Abstract
Inspired by natural systems, weak features can be strategically incorporated into materials to enhance mechanical response. This study presents, for the first time, a bioinspired approach enabled by additive manufacturing to tailor crack paths and enhance fatigue life in metallic components. Cylindrical holes of varying diameters and patterns were embedded as site-specific weak features in 18Ni(300) maraging steel produced via laser powder bed fusion. Fatigue crack growth tests in solution-annealed and aged conditions revealed that a region of weak features can effectively tailor crack path in solution-annealed condition while improving fatigue life in both conditions. In solution-annealed condition, crack deviation, bifurcation and transition of fatigue crack growth stage improved the fatigue life by 26%. In the aged condition, however, the lower fracture toughness limited the crack path tailoring, and retardation due to the crack re-initiation enhanced fatigue life by 14%. In both conditions, rough surfaces of holes restricted the maximum fatigue life improvement. This study demonstrates the unique potential of AM to integrate weak features into metallic materials for crack engineering. By refining the holes roughness in further steps, crack engineering via AM can be realized, offering a pathway to further optimize components’ durability.
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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 | 0.000 |
| 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 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".