Optimization of wire type and current welding on the strength of welding connection in two types of material testing via response surface methodology
Bibliographic record
Abstract
The number of types of wire welding that circulate on the market and the development of welding technology, especially related to current welding, create a lack of information related to the quality of welding results for several types of materials. This can cause the strength of the welding connection to not be maximized. Therefore, this study aims to optimize the use of three types of wire welding and three current welding on the strength of the welding connection in two types of material testing using the response surface methodology. Box-Behnken, coupled with the RSM and the desirability function, was used to optimize the strength of the welding connection of the wire types (RD-46, LB-52, and RB-26), and current welding (100, 130, and 160 A) against two types of material testing (IWF-150 and ASTM-A517-G70). The strength of the welding connection observed in response included the tensile strength, Charpy impact-absorbed energy, hardness values in the welding metal and the hardness values in the main metal. Optimization of the strength of the welding connection in this study recommends the application of wire types RB-26, current welding of 100 A, and ASTM-A517-G70 material testing with the highest desirability value of 71.6%. Optimization of tensile strength, Charpy impact-absorbed energy, hardness values in welding metal, and hardness values in main metal by applying this parameter are 575.64 MPa, 110.69 J, 216.75 (HV10) and 126.6 (HV10), respectively. The results proved that an appropriate welding connection strength could be achieved using wire welding types and current welding in material testing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".