A Study on the Formability of IF and Plain Carbon Mild Steels
Bibliographic record
Abstract
Steel sheet metals are widely used in different industries due to their high strength, good weldability, availability, moderate cost, and the ability to form to complex 3D parts. The study of the formability of sheet metals is often done by means of Forming Limit Diagram (FLD) which presents the major and minor engineering strain thresholds under different deformation states. In this article, the formability parameters of three different steel sheet metals with the same thickness have been determined by uniaxial tension test and their FLDs have been produced by Hecker method: RRSt14O3, Zinc coated IF (Interstitial Free) steel and uncoated IF steel. Also the materials’ formability during the stamping process of a car door inner panel has been investigated as a case study to substitute the original design of raw material, coated IF steel, with a cheaper alternative. Among the tested materials to form the part, the uniaxial tension results showed that the formability parameters of uncoated IF steel was higher than the coated IF steel and the parameters of RRSt14O3 sheet metal was the lowest. The FLD of coated IF steel sheet was the highest (best formability). Differences among the formability parameters in uniaxial tension, the FLDs, and the stamping behavior of the part with different steel sheet metals have been explained by their surface roughnesses and the friction coefficients that affect the material flow during the FLD test as well as the stamping process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".