Plasma Transferred Arc Additive Manufacturing of High-Chromium White Iron: Parameter Optimization and Interpass Temperature Control for Repair Applications
Bibliographic record
Abstract
Abstract High-chromium white iron (HCWI) is a highly abrasion-resistant alloy, typically containing 23–28% chromium, known for its exceptional hardness and wear resistance due to hard M7C3 carbides embedded within a martensitic or austenitic matrix. This structure provides superior resistance to erosion, impact, and corrosion, making HCWI ideal for components like crusher liners, slurry pump parts, and chute liners in mining, cement, and dredging industries. Routine maintenance of these components is essential due to extreme wear, and additive manufacturing (AM) offers a promising solution for fast, cost-effective repairs. Plasma transferred arc additive manufacturing (PTA-AM), a directed energy deposition (DED) technique, provides precise control over dilution, microstructure, and material composition, making it suitable for producing high-hardness, corrosion-resistant deposits. This study aims to optimize processing conditions for PTA-AM’d HCWI (ASTM A532 Class III Type A) by controlling preheating and interpass temperatures (400°C to 800°C) to reduce thermal gradients. The resulting deposits are analyzed for microstructure, phase composition, and microhardness to establish correlations between thermal conditions and material properties.
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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".