Neural Networks Approach to Optimization of Steel Alloys Composition - Engineering Applications of Neural Networks - Part I Access content directly
Conference Papers Year : 2011

Neural Networks Approach to Optimization of Steel Alloys Composition

Petia Koprinkova-Hristova
  • Function : Author
  • PersonId : 1014118
Silviya Popova
  • Function : Author
  • PersonId : 1014120

Abstract

The paper presents modeling of steels strength characteristics in dependence from their alloying components quantities using neural networks as nonlinear approximation functions. Further, for optimization purpose the neural network models are used. The gradient descent algorithm based on utility function backpropagation through the models is applied. The approach is aimed at synthesis of steel alloys compositions with improved strength characteristics by solving multi-criteria optimization task. The obtained optimal alloying compositions fall into martenzite region of steels. They will be subject of further experimental testing in order to synthesize new steels with desired characteristics.
Fichier principal
Vignette du fichier
978-3-642-23957-1_36_Chapter.pdf (216.43 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01571366 , version 1 (02-08-2017)

Licence

Attribution

Identifiers

Cite

Petia Koprinkova-Hristova, Nikolay Tontchev, Silviya Popova. Neural Networks Approach to Optimization of Steel Alloys Composition. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.315-324, ⟨10.1007/978-3-642-23957-1_36⟩. ⟨hal-01571366⟩
47 View
106 Download

Altmetric

Share

Gmail Facebook X LinkedIn More