Effect of Chemical Etching Time on the Fatigue behaviour of Ti-6Al-4V produced by Laser Powder Bed Fusion
Bibliographic record
Abstract
This study focuses on the evolution of the fatigue strength of L-PBF produced Ti-6Al-4V as a function of the chemical etching finishing process. The aim is to identify the critical fatigue crack initiation mechanisms and the transitions between them in terms of the evolution of the surface micro-geometry caused by the finishing process. This has been done using three different geometries and six different surface states, including the machined reference surface state. The evolution of the crack initiation mechanisms, identified via SEM observations of the fatigue failure surfaces, is then used to explain the evolutions of the fatigue strength and the fatigue scatter, in both the finite and the high cycle fatigue-life domains. As expected, chemical etching effects the fatigue life via a polishing effect on the micro-geometry, which directly influences both the finite and the high cycle fatigue domains. It is shown that chemical etching makes it possible to obtain fatigue strengths that are almost similar to those of the machined surface, despite the fact that the roughness is higher, even after an optimal etching time. However, it is also observed that etching cannot fully counter the effects of large surface connected porosities (typically lack-of-fusion) that lead to large surface cavities. Some minor scale or size effects can also be noted between the different coupon geometries. This does not modify the conclusions concerning the initiation mechanisms and their effect on the fatigue life.
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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".