Effect of Platform Temperature and Post-Processing Heat Treatment on the Fatigue Life of Additively Manufactured AlSi7Mg Alloy
Bibliographic record
Abstract
The effect of platform temperature combined with a T5 heat treatment on the fatigue life of additively manufactured aluminum alloy AlSi7Mg was characterized and understood. High-cycle fatigue tests were carried out on samples built with four platform temperatures (35 °C, 60 °C, 80 °C and 200 °C) and post-processing heat treatment strategies (F and T5). Microstructural and fractographic observations combined with microhardness measurements were performed. A log-normal statistical distribution regressed with 90% B-basis probabilities of survival revealed that specimens produced on a platform maintained at 80 °C and post-processed with a T5 heat treatment presented the highest fatigue life among the conditions tested. Precipitation of silicon within the aluminum cells during the T5 heat treatment is the proposed explanation for the improved fatigue life of the T5 samples. In the as-built condition, specimens produced at 200 °C were found to be less resistant to fatigue than the specimens built at lower temperatures. The coarser microstructure and lowest microhardness resulting from high-temperature manufacturing explain this reduced fatigue strength. All fatigue cracks initiated from manufacturing discontinuities. This led to a fatigue life prediction model based upon linear elastic fracture mechanics. The model was fitted to the experimental results of the F and T5 samples separately. With the exception of the 35 °C—T5 specimens, the predicted fatigue lives agree with the experimental results and literature.
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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) | 0.001 | 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".