ANN Model to predict the bake hardenability of Transformation-InducedPlasticity steels

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Neural networks are useful tools for optimizing material properties, considering the material's microstructure and therefore the thermal treatments it has undergone. In this research an artificial neural network (ANN) with a Bayesian framework able to predict the bake hardening and the mechanical properties of the Transformation-Induced-Plasticity (TRIP) steels was designed. The forecast ability of the ANN model is achieved taking into account the operating parameters involved in the Intercritical Annealing (IA), in the Isothermal Bainite Treatment (IBT) and also considering the different prestrain values and the volume fraction of the retained austenite before the Bake Hardening (BH) treatment. This approach allowed one to overcome the need to know the metallurgical rules that describe all the active phenomena in multiphase steels. The neural network approach allowed one to overcome the lack of prediction capability in the existing numerical models
Original languageEnglish
Title of host publicationMaterials Characterisation IV
Pages33-45
Number of pages12
Publication statusPublished - 2009

Fingerprint

Steel
Neural networks
Hardening
Bainite
Austenite
Plasticity
Numerical models
Volume fraction
Materials properties
Heat treatment
Annealing
Mechanical properties
Microstructure

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Materials Science(all)
  • Mechanics of Materials
  • Fluid Flow and Transfer Processes
  • Electrochemistry

Cite this

ANN Model to predict the bake hardenability of Transformation-InducedPlasticity steels. / Riccobono, Roberto; Barcellona, Antonio; Palmeri, Dina.

Materials Characterisation IV. 2009. p. 33-45.

Research output: Chapter in Book/Report/Conference proceedingChapter

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AB - Neural networks are useful tools for optimizing material properties, considering the material's microstructure and therefore the thermal treatments it has undergone. In this research an artificial neural network (ANN) with a Bayesian framework able to predict the bake hardening and the mechanical properties of the Transformation-Induced-Plasticity (TRIP) steels was designed. The forecast ability of the ANN model is achieved taking into account the operating parameters involved in the Intercritical Annealing (IA), in the Isothermal Bainite Treatment (IBT) and also considering the different prestrain values and the volume fraction of the retained austenite before the Bake Hardening (BH) treatment. This approach allowed one to overcome the need to know the metallurgical rules that describe all the active phenomena in multiphase steels. The neural network approach allowed one to overcome the lack of prediction capability in the existing numerical models

KW - Artificial Neural Network

KW - Bake hardening

KW - Trip Steel

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BT - Materials Characterisation IV

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