Effect of Tempering Temperature on Hydrogen Desorption of AISI4140: Neural Networks Analysis
Bibliographic record
Abstract
Over the past ten years, Cr-Mo is used for the manufacture of hydro treating reactors, in order to meet the demand of the refining industry, steelmakers have had to develop Cr-Mo steel, which until then was widely used for the manufacture of devices operating at high temperatures under hydrogen pressure The purpose of this work is to study the susceptibility of steel 42CrMo4 (AISI4041) to hydrogen this steel is a martensitic, that is widely use especially in the hydrogen storage industry, this one is tempered at different temperatures (500, 550, 600, 650 and 700℃), the application of artificial neural networks is proposed to predict the optimum tempering temperature to have a minimal quantity of hydrogen by analyzing the hydrogen concentration parameter that escapes through the desorption phenomenon through time at room temperature the Mathematical model proposed by the neural network method has proved this and this has been validated once more by comparing it with a global statistical study following a coupling between the variables "time / concentration", "Tempering Temperature / concentration".In addition, the data used in the model were taken from real experimental data and are arranged in a format of two input parameters: time and tempering temperature, and output parameter is hydrogen concentration.The model was arbitrated using Mean Absolute Error (MAE) and average quadratic error (MSE) and correlation coefficient and model performance values found as 0.0686 and 0.0071, 98.87%, 0.0071 for the training part and 0.0916 and 0.0112, 99.10%, 0.0112 for the test.Finally, the major conclusions of this research show that ANN as powerful computational techniques in modeling of nonlinear systems, can be reliably used in the prediction and correlation to obtain tempering temperature to get lower hydrogen concentration lesser.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".