In-situ synthesis of titanium carbide particles in iron matrix using laser cladding
Bibliographic record
Abstract
Conventionally, the majority of reinforced phases are directly added into coating materials to form reinforcement in the matrix. The interface between the particles and matrix is often a potential source of weakness owing to different thermal expansion coefficients between them. When the surface of particles is not clean or is polluted, cracks may propagate from the interface. These issues can be resolved if the reinforcements are formed in the matrix by reacting between added pure elements. The reinforcements may be more compatible with the matrix and the interface may be cleaner than that of composites made conventionally. Because the in situ formed dispersions are thermally stable, this will ensure that the composite matrix has sufficient strength to transfer stress. Therefore, this methodology finds wide attention to form reinforcement in the matrix and various techniques are being tried to develop the process. This paper describes an in-situ synthesis of TiC particles in Fe matrix using laser cladding. A precursor mixture of graphite and titanium is deposited on low-carbon steel using pre-placed technique and promising results are observed. The comprehensive study is under progress. The paper presents the optimization of the process parameters and material characterization using various techniques, including – optical microscopy, scanning electron microscopy and microhardness.
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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".