Material Characterization of Uncoated Boron Steel for Automotive Body Structure Applications
Bibliographic record
Abstract
Use of Advanced High Strength Steels in automotive applications is increasing. One of these materials is boron steel, which is commercially available in coated and uncoated sheets. Automotive manufacturers are using boron steel in body structure applications to produce light weight parts and to address safety requirements. Boron steel is available in a non heat-treated condition (also referred to as “green state”) which typically has a yield strength around 350 MPa. The yield strength for a fully temperature hardened boron steel increases to above 1000 MPa, depending on heat treatment temperature and quenching methods used. In this report, the static and fatigue properties of uncoated boron steel were evaluated. One objective was to understand whether these properties varied with respect to the material rolling direction (longitudinal, transverse and diagonal). For static strength analysis three different gages (1.0 mm, 1.5 mm and 2.0 mm) were evaluated. For fatigue evaluation, 3.0 mm thickness boron steel was evaluated. Based on the mechanical test data, ultimate tensile strength was not statistically significant in all three directions (longitudinal, transverse and diagonal) among three gages chosen. However, within the same gage, ultimate tensile strength is statistically significant in all three directions. 0.2% offset Yield strength and total elongation are uniform in all gages as well as in all three directions within each gage. However, uniform elongation (at max. load condition) was significant among the gages as well as within the same gages. A comparison of the monotonic and cyclic stress strain curves indicates boron steel is a strain-softening material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".