Study on Constitutive Relation of Nickel-Base Superalloy Inconel 718 Based on Long Short Term Memory Recurrent Neural Network

• Main Authors: Han Mei, Lihui Lang, Xiaoguang Yang, Zheng Liu, Xiaoxing Li
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Metals
• Subjects: Inconel 718; constitutive model; long short term memory (LSTM); high-temperature stretching; recurrent neural network
• Online Access: https://www.mdpi.com/2075-4701/10/12/1588

The high temperature tensile test of Inconel 718 under the conditions of deformation temperature of 950 °C–1100 °C and strain rate of 0.0005 s⁻¹–0.1 s⁻¹ was carried out, and its true stress–true strain curve was drawn. Through the analysis of the flow stress of Inconel 718 under different conditions, it can be seen that the high-temperature rheological behavior of Inconel 718 is affected by the coupling of strain hardening effect and dynamic softening effect, and has significant loading history correlation. By applying the stretched data, a long short term memory (LSTM) recurrent neural network was trained to characterize the constitutive relationship of Inconel 718. The experimental results show that the prediction results of the LSTM constitutive model are extremely consistent with the experimental data, which is significantly better than the modified Johnson–Cook (M-JC) model. Finally, high temperature tensile experiments under variable strain rates were carried out to verify the feasibility of the LSTM constitutive model in the complex loading and unloading stages.