DOUBLE GLOW PLASMA SURFACE TITANIZING ON AISI 316 STAINLESS STEEL WITH IMPROVED WEAR RESISTANCE: EFFECTS OF PROCESS PARAMETERS
Bibliographic record
Abstract
Using the double glow plasma surface alloying technique, a titanizing coating with improved wear resistance can be prepared on AISI 316 stainless steel. The purpose of this paper is to investigate process parameter effects by orthogonal array design. Four main factors, titanizing temperature, holding time, voltage difference and electrode distance, are adopted in orthogonal experiments. For each factor, four levels are set. The range analysis is used to investigate the factor and level influences on the coating thickness and specific wear rate. Meanwhile, the analysis of variance method is applied to calculate the contributions of each factor. The results indicate that temperature is most critical. In balancing the coating thickness and the wear property, the optimal process parameters are 950∘C, 3[Formula: see text]h, 200[Formula: see text]V and 18[Formula: see text]mm. Corresponding to the optimal process, the thickness and the specific wear rate of the titanizing coating are 10[Formula: see text][Formula: see text]m and 2.609E−05 mm3⋅ N−1⋅ m−1, respectively.
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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".