Microstructural, corrosion and mechanical properties of additively manufactured alloys: a review
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Bibliographic record
Abstract
Additive manufacturing (AM) of metallic alloys offers a new avenue to print objects having complex geometries. This exclusive benefit of AM has made it an alternative route to conventional manufacturing. Importantly, additively manufactured (AMed) alloys often exhibit improved microstructures, which may provide better properties. The microstructure of an alloy can be tuned by controlling the processing parameters. This study includes an overview of the processing parameters that can influence the microstructural, mechanical, and corrosion properties of AMed alloys. Moreover, the effects of heat treatment on AMed alloys are also discussed. Among various processing parameters, it is observed that the laser power significantly influences the microstructure. The microstructures produced with high laser power are similar to heat-treated samples for 316L stainless steel (SS) and Ti6Al4V. Similarly, variation in scanning speed results in distinct morphology of grains in Ti6Al4V. Moreover, different AM processes, such as SLM and EBM, produce coarse and fine β grains, respectively, in Ti6Al4V. The fabrication of AlSi10Mg yields various sizes of melt pool due to different scanning strategies. Furthermore, mechanical properties such as microhardness is higher and the yield strength is lower for Ti6Al4V fabricated at lower laser power. The corrosion behavior of SLMed Ti6Al4V is different on the perpendicular and parallel planes to the build direction. Due to the increase in grain size after heat treatment, the corrosion resistance of AMed Ti6Al4V and AlSi10Mg is reduced. In contrast, heat treatment applied on 316L, Ti6Al4V, AlSi10Mg, and Inconel 718 is beneficial for mechanical properties. After the development of materials with optimized processing parameters, the research should be conducted on replacement of the wrought alloys with the AMed alloys. It is expected that new applications such as fuel cells and biomedical devices will utilize the AM technology to build parts in the recent future.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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