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Prediction of mechanical properties of DP steels using neural network model

Bahrami, A ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jallcom.2004.09.014
  3. Publisher: 2005
  4. Abstract:
  5. In this investigation, a neural network model was used to predict mechanical properties of dual phase (DP) steels and sensivity analysis was performed to investigate the importance of the effects of pre-strain, deformation temperature, volume fraction and morphology of martensite on room temperature mechanical behavior of these steels. In order to train the neural network, dual-phase (DP) steels with different morphology and volume fractions of martensite were deformed between 2 and 8%, at high temperature range of 150-450 °C. The results of this investigation show that there is a good agreement between experimental and predicted values and the well-trained neural network has a great potential in mechanical behavior modeling of DP steels. © 2004 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Crystal growth ; Deformation ; Differential scanning calorimetry ; Integration ; Mathematical models ; Melting ; Neural networks ; Nucleation ; Quenching ; Tensile strength ; Thermoanalysis ; X ray diffraction analysis ; Complex networks ; Dual-phase steels ; High temperatures ; Neurons ; Carbon steel
  8. Source: Journal of Alloys and Compounds ; Volume 392, Issue 1-2 , 2005 , Pages 177-182 ; 09258388 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0925838804012034