Prediction of the High Structural Strength of System-Alloyed Maraging Steels Using Neural-Network Modeling

Prediction of the High Structural Strength of System-Alloyed Maraging Steels Using Neural-Network Modeling

The effectiveness of neural-network modeling for the multi-objective optimization of the composition and the prediction of the strength of system-alloyed Fe–Cr–Ni–Mo-based maraging steels is shown. Analyzing the dependence of the mechanical characteristics of this class of steels on their compositio...

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Veröffentlicht in:Metallurgist (New York) 2023-05, Vol.67 (1-2), p.249-255
Hauptverfasser: Dvoynikov, D. A., Gladkovsky, S. V., Zalazinsky, A. G., Titov, V. G., Shveikin, V. P.
Format: Artikel
Sprache:eng
Schlagworte:
Additives
Algorithms
Alloying elements
Analysis
Characterization and Evaluation of Materials
Chemistry and Materials Science
Chromium
Composition
Crack propagation
Fracture toughness
Maraging steels
Materials Science
Mechanical properties
Metallic Materials
Modelling
Molybdenum
Multiple objective analysis
Neural networks
Officials and employees
Optimization
Specialty metals industry
Steel, Structural
Structural strength
Yield stress
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Zusammenfassung:The effectiveness of neural-network modeling for the multi-objective optimization of the composition and the prediction of the strength of system-alloyed Fe–Cr–Ni–Mo-based maraging steels is shown. Analyzing the dependence of the mechanical characteristics of this class of steels on their composition, it is possible to determine the effect of small additives of alloying elements on yield stress, strength, toughness, and static crack resistance. For multi-objective optimization of the composition of maraging steels, the ideal-point method and the linear-sequence algorithm are used.
ISSN:0026-0894
1573-8892
DOI:10.1007/s11015-023-01508-1