IR Heat Treatment of Hybrid Steel-Al Joints
Bibliographic record
Abstract
The technical objective of this CRADA is to develop and model a heat treatment process based on Infra- Red (IR) heating of a overcast Al/steel bimetallic joint to produce a T5 temper in a shorter period of time than is currently achievable and, separately, to produce a modified T6 temper for improved mechanical properties without the loss of joint integrity. IR heat treatments have been demonstrated to provide reduced processing time, reduced energy requirements, and improved material properties of Al components, including strength and elongation, relative to convective thermal heat treatment methods. A prototype IR furnace was assembled to heat treat these joints. The residual stress state of the joints were modeled in the as-cast condition, as well as the T5 and T6 condition. Neutrons were used to measure the residual stresses in the joints for various heat treatments. ORNL is uniquely equipped to partner with Vehma in the experimental evaluation and modeling of Al/steel bi-metallic joints. ORNL possesses a long history of microstructural, crystallographic and mechanical characterization of structural materials. Unique facilities at ORNL for this work include the NRSF2/HB-2B beamline at the High Flux Isotope Reactor (HFIR) for residual stress measurements with neutron diffraction, metallurgical expertise for heat treating and modeling capability of the manufacturing of these joints for residual stress prediction. All of these capabilities were utilized in this CRADA. The CRADA began in April 2011 and ended in September 2014 (41 months).
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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 | 0.000 | 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) | 0.002 | 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".