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Erschienen in: Metallurgical and Materials Transactions A 6/2018

16.03.2018

Design Optimization of Microalloyed Steels Using Thermodynamics Principles and Neural-Network-Based Modeling

verfasst von: Itishree Mohanty, Appa Rao Chintha, Saurabh Kundu

Erschienen in: Metallurgical and Materials Transactions A | Ausgabe 6/2018

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Abstract

The optimization of process parameters and composition is essential to achieve the desired properties with minimal additions of alloying elements in microalloyed steels. In some cases, it may be possible to substitute such steels for those which are more richly alloyed. However, process control involves a larger number of parameters, making the relationship between structure and properties difficult to assess. In this work, neural network models have been developed to estimate the mechanical properties of steels containing Nb + V or Nb + Ti. The outcomes have been validated by thermodynamic calculations and plant data. It has been shown that subtle thermodynamic trends can be captured by the neural network model. Some experimental rolling data have also been used to support the model, which in addition has been applied to calculate the costs of optimizing microalloyed steel. The generated pareto fronts identify many combinations of strength and elongation, making it possible to select composition and process parameters for a range of applications. The ANN model and the optimization model are being used for prediction of properties in a running plant and for development of new alloys, respectively.

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Metadaten
Titel
Design Optimization of Microalloyed Steels Using Thermodynamics Principles and Neural-Network-Based Modeling
verfasst von
Itishree Mohanty
Appa Rao Chintha
Saurabh Kundu
Publikationsdatum
16.03.2018
Verlag
Springer US
Erschienen in
Metallurgical and Materials Transactions A / Ausgabe 6/2018
Print ISSN: 1073-5623
Elektronische ISSN: 1543-1940
DOI
https://doi.org/10.1007/s11661-018-4540-4

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