A Characterization of Hot Flow Behaviors of Invar36 Alloy by an Artificial Neural Network with Back-Propagation Algorithm

In order to investigate the hot deformation behaviors of Invar36 alloy, isothermal compressive tests were conducted on a Gleeble 1500 thermo-mechanical simulator at the temperatures of 873, 948, 1023, 1098 and 1173 K and the strain rates of 0.01, 0.1, 1 and 10 s⁻¹. The effects of strain, temperature and strain rate on flow stress were analyzed, and a dynamic recrystallization type softening characteristic with unimodal flow behavior is determined. An artificial neural network based on back-propagation algorithm was proposed to handle the complex deformation behavior characteristics. The ANN model was evaluated in terms of correlation coefficient and average absolute relative error. A comparative study was performed on ANN model and constitutive equation by regression method for Invar36 alloy. Finally, the ANN model was applied to the finite element simulation, and an experimental study on trial hot forming of a V-shaped part was conducted to demonstrate the precision of the finite element simulation based on predicted flow stress data by ANN model. The results have sufficiently showed that the well-trained ANN model with BP algorithm is able to deal with the complex flow behaviors of Invar36 alloy and has great application potentiality in hot deformation.

Keywords:
Invar36 Alloy; artificial neural network; constitutive equation; finite element simulation