A multi-faceted investigative approach to ram speed, extrusion temperature and die exit width effects on mechanical properties of extruded Al 6063 alloy
Bibliographic record
Abstract
This research focused on the die exit width, ram speed and temperature effects on extruded Al 6063 alloy mechanical properties. It is a multiple approach that involves numerical, experimental and simulation methods in optimizing the extrusion process. The Q-Form was used in extruded sample flow stress and strain distribution analysis. The result revealed die exit width as the parameter with the most significant influence on Al 6063 alloy tensile strength and hardness, followed by extrusion temperature and then ram speed. The die width increase from 6mm to 8mm yields 73.5 % and 75.8 % tensile strength and hardness increase. The optimized process parameters predicted by the model are a speed of 16.2567 mm/s, a temperature of 526.334 °C, and a die exit diameter of 7.1862 mm, which yields a tensile strength of 151.031 MPa and a hardness of 183.644 HB, respectively. Based on Qform findings, the sample extruded using these optimal parameters yielded uniform metal flow products with low stress concentration. The research enables deep knowledge into the extrusion parameters and mechanical properties relationship, leading to aluminum alloy hot extrusion process optimization. This research has contributed to the more effective and efficient extrusion process development that can be applied in many aluminum extrusion industries. The product quality can be improved through optimized process parameters, thereby reducing the cost of production and boosting the extrusion process's overall efficiency.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".