Artificial neural network and constitutive equations to predict the hot deformation behavior of modified 2.25Cr-1Mo steel

作者：Li, Hong-Ying^*; Hu, Ji-Dong; Wei, Dong-Dong; Wang, Xiao-Feng; Li, Yang-Hua

来源：Materials and Design, 2012, 42(Dec.): 192-197.

DOI：10.1016/j.matdes.2012.05.056

• Ferrous metals and alloys
• Mechanical
• Plastic behavior

Hot compression tests of modified 2.25Cr-1Mo steel were conducted on a Gleeble-3500 thermomechanical simulator at the temperatures ranging from 1173 to 1473 K with the strain rate of 0.01-10 s(-1) and the height reduction of 60%. Based on the experimental results, an artificial neural network (ANN) model and constitutive equations were developed to predict the hot deformation behavior of modified 2.25Cr-1Mo steel. A comparative evaluation of the constitutive equations and the ANN model was carried out. It was found that the relative errors based on the ANN model varied from 4.63% to 2.23% and those were in the range from 20.48% to 12.11% by using the constitutive equations, and the average root mean square errors were 0.62 MPa and 7.66 MPa corresponding to the ANN model and constitutive equations, respectively. These results showed that the well-trained ANN model was more accurate and efficient in predicting the hot deformation behavior of modified 2.25Cr-1Mo steel than the constitutive equations.

• 出版日期2012-12
• 单位中南大学