Review of high-strength aluminium alloys for additive manufacturing by laser powder bed fusion
Bibliographic record
Abstract
Laser powder bed fusion (LPBF) is one of the major additive manufacturing techniques that industries have adopted to produce complex metal components. The scientific and industrial literature from the past few years reveals that there is a growing demand for the development of high-strength aluminium alloys for LPBF. However, some major challenges remain for high-strength aluminium alloys, especially in relation to printability and the control of defects. Possible strategies that have been identified to achieve high strength with printability include the adaptation of existing high-strength cast and wrought alloys to LPBF, the design of new alloys specifically for LPBF, and the development of aluminium-based composites to achieve unique combinations of properties and processability. Whilst review papers exist for aluminium alloys in general for the related work up to 2019, the purpose of this paper is to review the latest developments related to high-strength aluminium alloys for LPBF up to early 2022, including alloy and process design strategies to achieve high strength without cracking. It aims to provide fresh insights into the current state-of-the-art based on a review of extensive yield strength data for a wide spectrum of aluminium alloys and tempers that have been studied and/or commercialised for LPBF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".