Minimalizing spring back of metal plates bending effect by squishing with press tool
Bibliographic record
Abstract
The aim of this research is to obtain the minimum spring back value in bending metal plates using a press tool with a squishing process for the case of forming aluminium plates on S-shaped clothes hanger products with a 90o angle. The problem faced in bending metal plates is the occurrence of spring back which has more serious consequences if for example aircraft door products which must have accurate product dimensions so that they can be opened and closed tightly without any air or rainwater leaks. The research method includes simulation with Simufact and Solidwork softwares on a 2.8 mm thick aluminium plate with varying squishing depths, namely 0.1 mm, 0.2 mm, and 0.3 mm after the bending step, measuring the resulting spring back, making a radius optimal punch from the simulation results, testing the bending of the plate with a press tool, measuring the spring back value that occurs, analyzing the spring back results, and drawing conclusions. Results from research with an optimal punch radius on an S-shaped clothes hanger product with a 90o angle with a value of 1.5 mm, an optimal squishing depth of 0.1 mm on 2.8 mm thick aluminium material with a width of 19 mm producing a spring back of 0.058o with compressive force worth 3009.6 kg.
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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 | 0.000 |
| 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".