Experimental Characterization and Numerical Modelling of the Interfacial Heat Transfer Coefficient in Hot Stamping of Al-Si Coated 22MnB5 Steel
Bibliographic record
Abstract
In automotive hot stamping of Al-Si coated 22MnB5 steel, the heat transfer coefficient (HTC) between the blank and die is crucial for predicting the mechanical properties of the as-formed part. While average HTCs associated to a range of interfacial pressures are used in hot stamping operations design, in reality the HTC varies during quenching and depends on many other process parameters in addition to interfacial pressure. An improved understanding of the transient behavior of the HTC is the overarching focus here. This work involves an experimental investigation to study the effects of pressure, coating weight, and surface roughness on the HTC evolution. Flat dies were mounted on a hydraulic press to obtain the temperature history within the blank and die. The data was analyzed using an inverse heat conduction algorithm to infer the time-resolved HTC. The HTC increases in two stages with the first being attributed to press tonnage ramp-up that gradually increases interfacial pressure. The second stage is attributed to an increase in the thermal conductivity and volume of the blank as its microstructure transforms from austenite to martensite. The experimental work also highlights how seemingly subtle aspects of this experiment, like the positioning and time-constant of the thermocouples, may impact the inferred HTC. Furthermore, nonuniformity of the interfacial pressure appears to lower the target pressure at which the HTC saturates, and diminish the time-averaged HTC with increasing target pressures. This work also presents a physics-based model that explains and predicts the HTC evolution. The model simulates imperfect contact using the measured blank surface topography, an explicit finite difference scheme to solve the transient heat conduction, and a nonlinear mechanical submodel to solve the microscale displacement of the die due to uniaxial compression of surface asperities. The predicted HTC history is in fair agreement with the experimental result. The model suggests that the evolving thermal conductivity of 22MnB5 does not affect the HTC due to the shielding behavior of the resolidified Al-Fe-Si coating layer at the interface. The model also explains how all hot stamping process parameters influence the HTC and lays the groundwork for a unified model that captures the physics of the entire hot 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".