Tailoring mechanical properties and microstructures in laser powder bed fusion of 316 L stainless steel through aluminium alloying and combined ex-situ and in-situ heat treatments
Why this work is in the frame
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Bibliographic record
Abstract
Laser powder bed fusion (LPBF) is a bottom-up manufacturing technique using a high-energy laser to selectively melt metallic alloys, enabling the creation of complex microstructures. While rapid cooling rates and process stochasticity can lead to unpredictable properties, LPBF's layer-wise powder deposition method unlocks unprecedented opportunities for in-situ alloying and microstructure engineering. This work introduces a novel blend of 316 L stainless steel with less than two percent of aluminium, demonstrating the potential for readily tunable microstructures leading to versatile mechanical properties – either as-built or following a post-process heat treatment. Compared to conventional fully austenitic LPBF 316 L, this new alloy solidifies into a combination of large BCC delta-ferrite grains and fine FCC gamma-austenite grains. Medium temperature furnace heat treatments significantly reinforce the BCC phase through the formation of NiAl B2 precipitates, leading to hardness values up to 708 HV. Furthermore, in situ selective laser heat treatments allow the transformation from columnar to equiaxed microstructure in a very short time. This unique alloy therefore appears well suited for tailoring local mechanical properties of phases through appropriate ex situ or in situ heat treatments, paving the way for designing composite-like materials within a single build. • We introduce a novel blend based on the widely used 316 L stainless steel alloy and pure aluminium through in-situ alloying. • We demonstrate a new way of manufacturing next-generation components with spatially optimized properties. • A unique microstructure combining large ferrite grains with fine austenite grains leads to versatile mechanical properties. • Nanometric B2 precipitates form within BCC grains, which allows reaching strengths competing with Ultra-high Strength Steels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it