Process Variable Optimization of Cold Metal Transfer Technique in Cladding of Stellite-6 on AISI 316 L Alloy Using Grey Relational Analysis (GRA)
Bibliographic record
Abstract
In this work, an attempt was made to identify the optimised parameter combination in cold metal transfer (CMT) cladding process of AISI 316 L austenitic stainless steel. cladding process was carried out using stellite 6 filler wire. Experiments were carried out based on L31 central composite design (CCD). Cladding was done with current, Voltage, torch angle and travel speed as input parameters. Quality of the clad was analysed by measuring depth of penetration, weld area, hardness of the clad surface, corrosion rate and clad interface thickness. Grey relation analysis was used to identify the optimised parameter combination. Trial number 18 was identified as the optimised parameter combination. The optimised input parameters are Welding Current 200 Amps, Voltage 19 V, Torch Angle 70⁰ and Welding Speed 150 m/min. ANOVA was used to identify the most influencing parameters on the overall multi-objective function and it was understood that the combined effect of torch angle, travel speed had a significant influence on the clad quality. Further investigation was carried out through an optimised set of parameters. The cladding experiment was conducted and their surface was investigated through clad profile, hardness of the cladded area, interface thickness of cladding region and corrosion rate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".