Utilisation of fuzzy logic and genetic algorithm to seek optimal corrugated die design for CGP of AZ31 magnesium alloy
Bibliographic record
Abstract
Magnesium alloys are the first choice among other lightweight structural metals like aluminium, titanium and beryllium due to low density and excellent corrosion resistance. However, low slip and cold plastic processing ability limits its applications. Constrained groove pressing (CGP), one among the SPD techniques, is well suited for improving the material properties. Corrugated dies are designed to investigate the deformation behaviour of AZ31 Mg alloy samples. To minimise the number of numerical simulations, Taguchi’s L9 orthogonal array is selected for the grooved die dimensions (viz. groove angle, groove width and coefficient of friction). Elasto-plastic finite element analysis is performed by implementing a multi-criterion-based genetic algorithm optimisation tool for obtaining the optimal die geometry. Reduction in total deformation, increase in the equivalent stress and elastic strain of AZ31 sheet were possible with 50° groove angle, 3 mm groove width and 0.22 friction coefficient. Mamdani-based fuzzy logic soft computing tool is implemented for the model to examine the deformation behaviour of AZ31 Mg alloy. 7.86 % deviation is observed in the fuzzy logic model predictions in comparison with simulation results. The suggested die design is well suitable for multiple CGP passes, to improve the grain refinement in the processed sheet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".