A Comparative Study of Material Flow Stress Modeling by Artificial Neural Networks and Statistical Methods

신경망을 이용한 HSLA 강의 고온 유동응력 예측 및 통계방법과의 비교

  • Published : 1997.01.01


The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.


Feed Forward Back-propagation;Neural Network;Hyperbolic-sine Function;Training Data;Testing Data;Zener-Hollomon Parameter;Learning Factor;Momentum Factor