Innovative Flexible Metal Forming Processes based on Hybrid Thermo‐Mechanical Interaction
Bibliographic record
Abstract
A new approach towards functional gradation of structural parts is presented. This approach is based on the utilization of locally varying thermo‐mechanically coupled effects applied to different initial workpiece geometries. The possible degree of freedom for the gradation of material properties and geometrical shape for sheet metal forming applications as well as for parts produced by bulk metal forming is characterized by the results of metallographic investigations, by mechanical testing and by an indication of the remaining residual stress state. On the basis of experimental results and process simulations, it could be revealed that the ability to exactly control the dynamic microstructural evolution by thermal and mechanical process parameters combined with predefined material design parameters constitutes a key towards the adjustment of flexible material property profiles even for parts with complex three‐dimensional geometry. Beyond that, the integrative aspect of thermal and mechanical treatment already implies the high level of obtainable efficiency resulting from shortening of process chains. However, it is not only the ability to integrate shape generation and property gradation, but also the automatically included positive effect of tailoring process behaviour by a gradation of formability finally allowing to improve process efficiency e.g. by a reduction of forming steps or reduction of (local) tool load.
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 | 0.000 |
| 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 | 0.000 |
| 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".