Strengthening of additively manufactured SS316L by in-situ laser remelting
Bibliographic record
Abstract
Laser-directed energy deposition (L-DED) is a highly effective and adaptable additive manufacturing process, capable of creating large, intricate shapes. Nevertheless, the build quality of the L-DED component is often compromised at high build rates due to problems such as porosity, poor surface finish, and inherent anisotropy in material properties. These flaws require costly and labour-intensive post-processing methods such as heat treatment, hot isostatic pressing, and machining to make L-DED components usable. Laser remelting is recognized as an efficient in situ treatment, serving as an alternative to conventional post-processing methods. It has shown improvements in reducing porosity and surface roughness and has significantly increased micro–hardness. This study demonstrates the effect of laser remelting on the improvement of tensile properties at a high build rate. Samples were deposited at a powder feed rate of 25 g/min, exceeding the optimal rate of 7 g/min, and subsequently subjected to laser remelting. Comparative analysis between high-deposition samples and high-deposition samples with laser remelting was conducted through tensile testing and microstructural examination. Laser remelting resulted in finer, equiaxed sub-grains, leading to a substantial increase in yield strength by 29 % and ultimate tensile strength (UTS) by 14 %. However, this strength gain was accompanied by a 22 % decrease in elongation. In summary, this study underscores the capability of in situ laser remelting to greatly enhance both deposition rate and part quality in L-DED, providing a viable strategy for large-scale component fabrication by integrating periodic laser remelting during the deposition process.
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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 | 0.000 |
| 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) | 0.002 | 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".