Microstructural evolution of a low-carbon steel fabricated via wire-arc additive manufacturing: the effect of interpass time and number of layers
Bibliographic record
Abstract
Abstract Wire-arc additive manufacturing (WAAM) provides advantages in deposition rate and design complexity compared to conventional fabrication processes. However, despite its advantages, studies on the influence of processing parameters on the microstructural evolution of WAAM components remain limited. To address the gap, this work examines the effects of interpass time and build height on microstructural development and microhardness across different regions of WAAM single-bead structures. Specifically, mild steel samples were fabricated following a two-factor, three-level, single-replicate design of experiments. Their microstructures were analyzed using optical microscopy (OM), scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) techniques, while phase transformation histories were correlated with different printing configurations through parent austenite grain reconstruction. The findings reveal that single-layer deposits exhibit a dual-phase microstructure consisting of ferrite and martensite. Furthermore, the addition of subsequent layers increases the ferrite phase fraction in the first layer, leading to a reduction in microhardness measurements. Interpass time and build height were found to influence the local morphology of parent grains, phase distribution, and grain topology. These results provide insights into the effects of thermal cycling and deposition strategies on solidification and grain growth associated with fabrication of mild steels. A deeper understanding of these relationships could enable process optimization for modifying microstructure and mechanical properties in WAAM-fabricated components, resulting to improved performance in structural and engineering applications.
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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 | 0.000 | 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 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".