The Effect of Thermal Field on the Deposition of Fe-TiC on Carbon Steel Using Laser Cladding
Bibliographic record
Abstract
In this paper, a numerical and experimental method is used to investigate the effect of thermal fields on the deposition of Fe-TiC using the laser cladding process. Since in laser cladding temperature distributions and consequent rapid cooling rates determine the microstructure and final physical properties of the deposited layers, a 3D time-dependent numerical model is used to simulate the cladding process parallel to experimental analysis. The numerical results are used to study the temperature distributions and their evolutions throughout the deposition process. The experimental and verified numerical outcomes are then employed to study the variations of the microstructures of the deposited material as well as correlation between the formed microstructures and temperature distributions across the deposition domain. The numerical and experimental investigations are conducted through the deposition of Fe-TiC on the substrate of AISI 1030 carbon steel using a 1.1 kW fiber laser. The experimental results confirm that by increasing the substrate temperature throughout the process the distribution of the TiC particles changes along with the deposited tracks and the TiC particles start forming clusters at the top of the clad.
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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 | 0.001 |
| 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".