Dynamic loading of direct metal laser sintered AlSi10Mg alloy: Strengthening behavior in different building directions
Bibliographic record
Abstract
Rod shaped samples of AlSi10Mg alloy were additively manufactured in vertical and horizontal directions using direct metal laser sintering technique and subjected to dynamic loading using Split Hopkinson Pressure Bar apparatus at a strain rate of 1400 s−1. Despite employing the same process parameters to fabricate the samples in two directions, the as-built samples possessed different microstructures, where columnar and equiaxed microstructures were developed in the vertical and horizontal samples, respectively. Moreover, fine and coherent Si precipitates were observed in the vertical sample while coarse and semi-coherent ones were developed in the horizontal sample. In addition, changing the building direction from horizontal to vertical led to a three-fold increase in dislocation density. After applying the compressive impact loads on the vertical and horizontal samples, it was found that the dynamic loading behavior of the two samples was almost similar despite the crucial differences in the initial microstructures. The microstructural analysis of the deformed samples revealed entangled networks of dislocations. In addition, over some locations, low angle grain boundaries developed due to partial dynamic recovery. The strengthening behaviors of the two samples additively manufactured in vertical and horizontal directions were investigated using the fundamentals of alloy hardening to unveil the similarities.
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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".