Abstract
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.
Highlights
As a special functional material, Invar[36] alloy is especially attractive for electronics industry, LNG storage tanks, long-distance power transmission lines, the forming mold of composite material and other fields on account of its extremely low coefficient of thermal expansion (CTE) below Curie temperature[1,2,3]
The results reveal that the artificial neural network (ANN) model has better accuracy and it is more excellent to model the flow behaviors of Invar[36] alloy
At compression conditions of 1098 K–1173 K and 0.01 s−1-0.1 s−1, the flow stress curves are characterized by a single peak followed by a continuous descent towards a steady state, which exhibits the occurrence of DRX during hot deformation
Summary
As a special functional material, Invar[36] alloy is especially attractive for electronics industry, LNG storage tanks, long-distance power transmission lines, the forming mold of composite material and other fields on account of its extremely low coefficient of thermal expansion (CTE) below Curie temperature[1,2,3]. The artificial neural network (ANN), as a relatively new Artificial intelligence algorithm, is able to solve the complex problems well by means of simulating the behavior of biological neural systems in computers[7] This approach makes it possible to manage the constitutive relationships of the flow stress, strain, strain rate and temperature with a collection of representative examples from the expected mapping functions for training instead of a well-defined mathematical model[8,9,10]. The ANN model for Invar[36] alloy was successfully applied to numerical simulation by using FEM on DEFORM-3D software, and the dependability of finite element simulation based on stress-strain data predicted by ANN model has been demonstrated through a hot forming experiment of a V-shaped part
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