Mapping Residual Stress Distributions in Advanced High Strength Steel Automotive Body Panels and Structural Components
Bibliographic record
Abstract
Automobile manufacturers have experienced increasing consumer and regulatory pressure to improve fuel efficiency and crashworthiness while simultaneously decreasing overall vehicle body weight. As such, the use of advanced high strength steels (AHSS) in body panels and other structural elements is becoming more and more prevalent because these advanced materials present an economical and elegant solution to the problem. To ensure the quality and safety of AHSS components, residual stress (RS) specifications (among others) have been introduced with the intent to minimize failures experienced both in the field and during production. Moreover, when welding processes are applied to AHSS components, the localized loss of ductility in combination with tensile RS can result in localized cracking, distortion, and/or failures. Spot checks at critical locations (often selected using either modeling methods and by experience) are currently being performed however, the highest RS present in the component may be overlooked in some cases. To achieve a more thorough understanding of the range and distribution of potentially harmful RS in AHSS components, large areas can be characterized using RS mapping techniques. This paper will present several examples of RS maps collected on various geometries on AHSS automotive body panels and structural elements demonstrating the variation and range of RS present.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".