Nanoparticle-induced control (MWCNTs–TiO<sub>2</sub>) on grain size and tensile strength response and multi-response optimization on TIG welded joints
Why this work is in the frame
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Bibliographic record
Abstract
This study discusses the effectual variable parameters of multi-walled carbon nanotubes and titanium oxide (MWCNT–TiO 2 ) contents and welding current in obtaining a response on mechanical behavior. The experimental work involved three levels of both parameters, which included 1, 1.5, and 2 wt.% of MWCNTs–TiO 2 and 160, 180, and 200 A of current by forming a full design that represented a 3 2 (3 k ) factorial model. The results of an analysis of variance (ANOVA) were critically employed to assess the variation between the variables and within their levels for approaching the significant combination of parameters. The joints welded with MWCNT–TiO 2 -coated fillers tendered a significant reduction in grain size (GS) along with high values of tensile strength (TS). From the ANOVA, it was determined that both the investigated parameters and their combined effect created a significant response for GS reduction and increment in TS. The presented empirical regression quadratic model was validated for the adequacy of the projected results that were shown to be acceptable with the experimental investigated data. In addition, the desirability function approach was applied for a multiple response optimization in yielding the desired responses of GS and TS and simultaneously providing an optimized set of process parameters. Thus, the function declared 1.5 wt.% MWCNT–TiO 2 and 180 A as the optimized process set for delivering the maximum TS at the designated range of GS for this system.
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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