Prediction of the Emissivity Curve at High Temperatures of Low Carbon Steel
Bibliographic record
Abstract
The thermography is an attractive technique to record the real-time temperature during the continuous welding processes. The temperature distribution during the weld zone is essential for understanding and evaluating the metallurgical properties of the joints. The knowledge of the material emissivity curve is necessary for the precise acquisition of thermography data. This emissivity value is usually assumed constant in the thermography software data acquisition, resulting in inaccurate thermographic data. The surface emissivity usually depends on the temperature. Thus, the values obtained in the literature may not be valid for materials of interest under the process’s conditions. Especially in the case of the low carbon steels, the emissivity data available are scarce and frequently obtained at low temperatures (below 200oC). Therefore, we proposed a methodological procedure to measure the effective surface emissivity, which considers the effect of temperatures and surface conditions. This research was focused on the development of an experimental methodology for determining the emissivity curve, using as the sample, a low carbon steel (0.07% C) with 0.21 mm thickness obtained from the productive stock of a continuous annealing line in the steel plant of the Companhia Siderúrgica Nacional (CSN). The normal sample emissivity was evaluated in the temperature range from 100ºC to 800ºC. It was proposed a model based on a sigmoid function to represent the effective emissivity during temperature rise. The sigmoid model parameters were obtained by a fitting procedure using temperature measurements obtained by thermocouples. The results showed an effective emissivity variation as a function of temperature, where emissivity values raged in the interval of 0.09 to 0.83. Thus, the useful emissivity curve was used to correct the thermography data obtained in electrical resistance seam welding (RSEW) with three levels of heats inputs. The correlation for the emissivity curve incorporated in the thermography software was applied to the thermal profiles of the welds analyzed from 441ºC to 713ºC. These values are consistent with the welding process used. The developed methodology can be applied, in a similar way, in the correction of profiles in other types of welding processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".