Microstructural Analysis of Optimized Carbon Steel Cladding Using Response Surface Methodology and Genetic Algorithms
Bibliographic record
Abstract
In this study, an innovative approach has been employed to evaluate the cladding performance characteristics of medium carbon steel using super duplex stainless steel filler material.This research explores the effect of welding current, gas flow rate, and welding speed on key performance indicators such as hardness, corrosion rate, bead width, and penetration depth.The maximum hardness of 137.4 Hv, maximum bead width of 6.728 mm, maximum penetration of 2.016mm and minimum corrosion rate of 26.25 x 10-3 mils/year were observed in proposed sample.The primary objective is to establish the empirical relationship between dependent and independent variables to identify the optimal parameters for cladding process.To accomplish this, the RSM and GA optimization has been utilized for modeling and optimization, respectively.The regression models are subjected to GA optimization to identify the best dependent and independent variables were 84.95A current, 7.12 lpm gas flow rate and 74.42 mm/min weld speed.The findings demonstrate that the predicted empirical model output the experimental values validating our approach.In addition the cladding process delivers a significant contribution in the field of welding and provides valuable insights into this process.The SEM & OM analysis was carried out for optimized sample which microstructure is fine-grained also desirable for good weld quality.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".