Thermal Processing History and Resulting Impact Response of a Hot-Formed Component with Tailored Properties – Numerical Study
Bibliographic record
Abstract
The impact modeling of a hot-formed component with tailored mechanical properties is studied to understand the influence of the thermal processing history and how the final properties of the component will affect its impact response. This paper presents a numerical study of the forming and quenching process and subsequent impact simulations. The processing simulations serve to predict the final microstructure and hardness distribution within a lab-scale B-pillar component that is processed using a tool with separate heated and cooled regions. A remapping algorithm is used to translate the results of the forming simulation to the impact simulation. A strain-rate sensitive material model is applied to model the response of these tailored microstructures during impact events. A comparison between a component that is fully hardened and a tailored component with regions of lower strength but increased ductility is presented in this work. Simulations that do not consider the onset of fracture predict superior peak impact load and energy absorption of the fully martensitic component due to its higher overall strength. However, the bainitic regions within the tailored component exhibit much higher ductility. Current work is addressing the introduction of failure criteria into simulations of tailored hot stamped components under impact loading for which the tailored component is expected to demonstrate superior resistance to cracking relative to the fully hardened component.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".