ANN Approach to Predict the Flow Stress of CMn (Nb-Ti-V) Micro Alloyed Steel

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Abdelhalim A., Abdelmoumene G., Lamia D., Abderrazek D.

FRATTURA ED INTEGRITA STRUTTURALE, vol.13, no.49, pp.350-359, 2019 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 49
  • Publication Date: 2019
  • Doi Number: 10.3221/igf-esis.49.35
  • Journal Name: FRATTURA ED INTEGRITA STRUTTURALE
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.350-359
  • Keywords: Flow Stress, Micro Alloyed Steel, Artificial Neural Network, Hot Compression Tests
  • Ankara Haci Bayram Veli University Affiliated: No

Abstract

The flow behavior of CMn (Nb-Ti-V) micro alloyed steel was studied by hot compression tests in a wide range of temperatures (700 degrees C to 1050 degrees C, Step 50 degrees C), strain rates (0.000734 s(-1), 0.0029 s(-1), and 0.0146 s(-1)) and true strain of 0 to 0.8. Based on the experimental true stress-plastic strain data, the artificial neural network (ANN) methods were employed to predict the flow stress of CMn (Nb-Ti-V). The ANN model was trained with Levenberg-Marquardt (LM) algorithm. The optimal LM neural network model with two hidden layer network with ten neurons in the first and ten neurons in the second gives the best predictions is developed. It is demonstrated that the LV neural network model has better performance in predicting the flow stress. The results can be further used in mathematical simulation of hot metal forming processes.