Experimental Studies of Corrosion Resistance of Welded Seams of Steel Structures with a High Sulfur Content in Surface Layers Using Modern Methods of Modeling
Notice bibliographique
Résumé
Analysis of literature sources and empirical data indicate a variety and a large volume of experimental material, which often characterizes the uncertainty and contradiction of information regarding the effect of sulphur and manganese on the corrosion behaviour of steels obtained using traditional methods. This leads to the need to search for new, alternative methods for its effective analysis. The task of assessing and predicting the corrosion properties of structural steels is a key one in the general problem of managing the operational reliability of welded metal structures and equipment for the chemical, metallurgical, oil and gas, mining and other industries. The possibilities of its solution consist of using new information technologies, a component of which is intelligent means of information processing, such as artificial neural networks (ANN). The use of ANN makes it possible to create qualitatively new hardware and software that significantly expand the classes of emerging problems and increase the efficiency of analysis and forecasting. In the process of long-term operation of metal structures in many industrial industries, the metal is in direct contact with sulfur-containing agents at high temperatures. This leads to the saturation of the surface layer of the metal with sulfur with a concentration of up to 0.6%, which further makes it impossible to carry out repair and welding operations due to the formation of hot cracks. It was found that adding metallic manganese into the electrode coating in an amount of 20-25% significantly increases (4-5 times) the resistance against the formation of hot cracks. Sulfur content in the deposited metal has the opposite effect on the appearance of hot cracks. So, with a sulfur content of 0.042% and more, the resistance of the metal against the formation of hot cracks decreases sharply. It is shown that an increase in the content of metallic manganese in the electrode coating significantly reduces the content of dissolved sulfur in the deposited metal. Moreover, this tendency is typical for steels with different sulfur content in the surface layers and with different service life. For example, for steel with a service life of 20 years, the initial sulfur content in the surface layer of the metal (up to 1 mm) was about 0.52%. Adding metallic manganese in the coating of electrodes in an amount of 20-25% made it possible to reduce the sulfur content in the deposited metal to 0.03-0.045%, i.e. 12.6-17.3 times. In addition, the corrosion rate decreases with an increase in the content of metallic manganese in the electrode coating. The lowest corrosion rate for all steels involved in the research was established at 20-25% manganese content in the coating.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».