Applying Thermo-Reactive Diffusion Method to Surface Coating of H13 Tool Steel Using Ferrovanadium, Ferrochromium, and Misch Metal
Bibliographic record
Abstract
The thermo-reactive diffusion (TRD) process is a method for surface hardening that improves the hardness, corrosion resistance, and wear resistance of the materials. In this study, the effect of composition was investigated, by using 8, 12, 16, 20, and 24 wt% of ferrochrome and ferrovanadium with equal amounts, along with 3 wt% aluminum and borax. The TRD process was carried out at 1000°C for 7 hours on AISI H13 hot work tool steel so that a complex coating of chromium-vanadium carbide was formed on the H13 substrate. Optical and scanning electron microscopes were used to evaluate the thickness and microstructure of the samples, respectively. Moreover, the amount of alloying elements and constituent phases on the surface were analyzed by point analysis and X-ray diffraction. The micro-hardness method was used to evaluate the hardness of the samples. The results of this study showed that by increasing the amount of ferroalloy, the hardness and thickness of the coating increase. The optimal composition with 8% ferrochrome and 8% ferrovanadium was found to yield a coating with a thickness of 7.65 μm and a hardness of 2115 HV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".