Laser Additive Manufacturing Of High Reflectivity Aluminium Alloys
Bibliographic record
Abstract
As metal additive manufacturing (AM) technologies are increasingly adopted for end-use products, the bank of materials manufactured by this technology are bound to grow at a rapid rate. AM is often deployed as it enables the ability to manufacture components with high geometric complexity and has the potential to reduce cost and lead times. Ultimately, there is a potential to locally tailor material properties from the microscale to the macroscale using this approach. The AM of each material comes with its own set of challengesthe most common issues being porosity and residual stresses due to the localized thermal loads, particularly for the laser powder bed fusion (LPBF) AM technology, which currently has the highest industrial uptake. The issue of porosity in particularly critical for the automotive, aerospace, and defense industries wherein aluminium (Al) alloys are commonly used. Aluminium alloys commonly contain highly volatile elements, which when interacting with the low beam spot sizes (<100 m) of most common LPBF systems are very easily vaporized, leading to multiple issues such as porosity and cracks in the final printed part. In this work, a combination of physics-driven LPBF processing diagrams, beam path planning, and advanced material characterization equipment including X-ray computed tomography and surface profilometry are used to understand the effect of laser power, scan speed, and beam spot radius on the porosity of two aluminium alloys -AlSi10Mg and Scalmalloy , with reported densities >99.98%. Additionally, the influence of the LPBF processing parameters on microstructure of these alloys and thereby on the surface roughness and mechanical properties such as hardness and tensile strength is highlighted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".