Microstructure and Electrochemical Behavior of a 3D-Printed Ti-6Al-4V Alloy
Bibliographic record
Abstract
3D printing (or more formally called additive manufacturing) has the potential to revolutionize the way objects are manufactured, ranging from critical applications such as aerospace components to medical devices, making the materials stronger, lighter and more durable than those manufactured via conventional methods. While the mechanical properties of Ti-6Al-4V parts manufactured with two major 3D printing techniques: selective laser melting (SLM) and electron beam melting (EBM), have been reported, it is unknown if the corrosion resistance of the 3D-printed parts is comparable to that of the alloy made with isothermal forging (ISF). The aim of this study was to identify the corrosion resistance and mechanisms of Ti-6Al-4V alloy manufactured by SLM, EBM and ISF via electrochemical corrosion tests in 3.5% NaCl solution, focusing on the effect of microstructures. It was observed that the equiaxed α + β microstructure in the ISF-manufactured Ti-6Al-4V alloy had a superior corrosion resistance to the acicular martensitic α' + β and lamellar α + β microstructures of the 3D-printed samples via SLM and EBM, respectively. This was mainly due to the fact that (1) a higher amount of β phase was present in the ISF-manufactured sample, and (2) the fraction of phase interfaces was lower in the equiaxed α + β microstructure than in the acicular α' + β and lamellar α + β microstructures, leading to fewer microgalvanic cells. The lower corrosion resistance of SLM-manufactured sample was also related to the higher strain energy and lower electrochemical potential induced by the presence of martensitic twins, resulting in faster anodic dissolution and higher corrosion rate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".