Wear performance and microstructures of Fe-Cr-C alloy cladding on heterogeneous welded joints of NM450/ER70-G/ZG30SiMn
Bibliographic record
Abstract
This work employed Fe-Cr-C alloy as the cladding material to fabricate wear-resistant coatings on dissimilar steel welded joints composed of NM450 wear-resistant steel and ZG30SiMn cast steel with ER70-G welding wire. The investigation focused on elucidating the effect of dilution ratio levels on microstructural distribution and wear behavior within this heterogeneous multi-material system. Owing to its inherently higher chromium (Cr) content, the NM450 region demonstrated more pronounced carbide formation compared to the ER70-G and ZG30SiMn regions. Microstructural analysis revealed that Cr7C3 carbides predominantly formed along grain boundaries, whereas Cr23C6 carbides mainly precipitated within grains. After cladding, the wear resistance of the NM450/ER70-G/ZG30SiMn welded joint was markedly improved, with the high-dilution coating exhibiting superior performance. This enhancement was attributed to favorable thermal conditions in the high-dilution scenario, promoting a more homogeneous precipitation of Cr23C6. Although significant elemental mixing occurred at the interfacial zones, the top region of the cladding layer remained minimally affected by dilution-induced drawbacks. Accordingly, the high-dilution sample demonstrated better wear resistance than its low-dilution counterpart due to optimized carbide precipitation characteristics. These findings provide basic insights for designing effective cladding strategies for complex multi-material components in demanding industrial applications.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".