A new approach of the constrained groove pressing process on Al5083-O alloy using PMMA polymer, without die non-friction coefficient: nanostructure, mechanical Properties and hardness
Bibliographic record
Abstract
The Constrained Groove Pressing (CGP) on annealed Al5083 (Al5083-O) alloy sheets by means of Polymethyl Methacrylate (PMMA) polymer is investigated in this research. Input parameters include Traverse Speed (1, 2, and 3 mm/min), Pass Number (1, 2, 3, and 4), and Lubricants (Grease, Oil, and without lubricants). Reciprocally, output parameters consist of Nanostructure (SEM and EBSD images), Hardness (HV), and Mechanical Properties – Impact Strength (kJ/m2), Tensile Strength (MPa), Young's Modulus (MPa), and Elongation (%). Revealed by the results, the impact strength is gradually improved in the 1st, 2nd, 3rd, and 4th passes by respectively adding oil and grease, compared to the state of using no lubricant. The impact strength results demonstrated that the addition of grease has a significant effect on the impact strength of composite samples. In order to build a high-quality product through the CGP process, it is very important for the mechanical properties to be based on the input parameters. In CGP processes, the Continuous Dynamic Recrystallization (CDRX) is considered as the ultrafine-grained mechanism. To predict the grain size evolution and the plastic deformation rate, the combination of Finite Element (FE) method and the ETMB model is utilized. Indicating the high generalizability and reliability as compared to other modeling methods, both the experimental test results and the ETMB model data, which have a higher degree of accuracy, are presented.
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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 | 0.000 |
| 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) | 0.001 | 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".