Prediction of processing map based on machine learning for low-cost Ti-5Al-1.5Mo-1.8Fe titanium alloy
MU Yiqiang
ZHANG Mingchuan
QIAO Ze
WANG Feng
QIN Meiling
XU Qinsi
Abstract：In order to study the thermal deformation behavior of low-cost titanium alloy,a Ti-5Al-1.5Mo-1.8Fe alloy was compressed for hot compression experiment by using Instron 5869 thermal compressor.Six machine learning models taking deformation temperature,strain rate and the degree of strain as input variables and adopting flow stresses as output variables were established.The flow stress values of alloy under different conditions were predicted and the prediction performance of these models were evaluated.The predicted processing map was drawn according to the prediction data of LSTM neural network model with the best prediction performance,and the predictive ability of the model was evaluated and verified by its comparison with experimental processing map.The results show that the processable regions of Ti-5Al-1.5Mo-1.8Fe alloy with the strain of 0.499 can be accurately reflected by the predicted processing map,in good accordance with the experimental map.The as-proposed method has better prediction for the thermal deformation behavior of Ti-5Al-1.5Mo-1.8Fe alloy.
Keywords：low-cost titanium alloyTi-5Al-1.5Mo-1.8Fe alloythermal deformation behaviorhot compression experimentflow stressmachine learning modelLSTM neural network modelprocessing map
Publication Date：2024-05-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 291-297 )