Microstructural analysis of critical geometries and heat treated laser powder bed fusion produced aluminum alloy A356
Bibliographic record
Abstract
Additive Manufacturing (AM) has garnered much attention due to the considerable advantages it offers over traditional manufacturing.Laser Powder Bed Fusion (LPBF) is a common AM method for metals, particularly in aerospace, due to the complex components that traditional manufacturing methods are able to produce, albeit with difficulty.Aluminum alloys are among those that are being examined for use in the aerospace industry, as the trend for lightweight and helping to make McGill a welcoming place.I would like to thank Lucie Nguyen, and especially Jose Alberto "Beto" Muniz, for answering my endless questions.I would especially like to thank Jason Danovitch, Andrew Walker, and Joseph Chou for being such great friends and people I could truly rely on.Love and appreciation must also be expressed for the friends I made at McGill outside of the lab.I definitely appreciate the long lunches taken with Frédéric Voisard, Sara Imbriglio, and Marianna Uceda.You guys have taught me a lot, and helped me through so much.It has been a pleasure to get to know you all.I would also like to thank Connor Aiken; I never would have thought we'd get to be such good friends.You drive me crazy, but I think you've helped me be a better person.I'd also like express my gratitude for my friends and family cheering me on from home in Nova Scotia.Lauren Cariou, you've been a great friend for
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".