Corrosion response of steels fabricated through arc directed energy deposition additive manufacturing: a review
Bibliographic record
Abstract
-shape steel components through various additive manufacturing (AM) modalities, utilizing intricate 3D model data. Initially, powder bed fusion (PBF) technology garnered significant attention for the fabrication of steel components. Nonetheless, arc-directed energy deposition (arc-DED), also known as wire arc additive manufacturing (WAAM) technology, is progressively gaining prominence in the AM enterprise due to its high production rate, the ability to print large-scale components, and notably, reduced capital investment. While early research on WAAM-fabricated steels primarily focused on microstructural and mechanical characteristics, there is an increasing emphasis on the corrosion performance of WAAM steel components. These components often encounter exposure to corrosive environments in their intended applications. The existing literature lacks a comprehensive review that delves into the nuanced factors influencing the corrosion behavior of WAAM-fabricated steels and the primary corrosion mechanisms governing their degradation. Therefore, this review is dedicated to exploring the corrosion properties of WAAM-fabricated steels, identifying key parameters influencing their degradation behavior. Moreover, it offers an in-depth examination and discussion of the underlying mechanisms governing corrosion-induced deterioration. Furthermore, this review meticulously scrutinizes the microstructural features and WAAM technologies, providing clarity and organization regarding details relevant to the corrosion of WAAM steel components. To conclude, the paper highlights the existing research gaps related to the corrosion of WAAM steel, delineating potential avenues for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".