Analyse the performance characteristics of mild steel plates at varying weld parameters by using artificial intelligence approaches
Why this work is in the frame
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Bibliographic record
Abstract
This systematic study shows how welding parameters (voltage, current, and gas flow rate) affect mild steel plate performance. The experimental MIG welding device changed various parameters. The experiment modifies welding parameters: welding current from 130 to 170 A, welding voltage from 23 to 27 V, and gas flow rate from 13 to 17 L/min. Welding specimens were tested for tensile strength (TS) and hardness (HBR). Weld joints get softer as the gas flow rate and welding current rise. As gas flow rate and welding current increase, experimental data shows an opposite impact. Weld tensile strength (TS) increases with gas flow rate and current but decreases with voltage. Certain instances show contradictory connections. Artificial intelligence was used to sustainably evaluate MIG welding test factor impacts. Ridge, Lasso, and Neural Network methods are less accurate than regression analysis. Tensile strength had a -0.47 monotonic correlation with welding current strength, while welding voltage had a positive correlation (+0.27). The tensile strength of welded connections is mostly affected by welding current, not gas flow rate or welding voltage. Gradient Boosting and Random Forest show improved prediction consistency, with lower error scores and higher R2 values.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it