The Microstructure Characterization of a Titanium Alloy Based on a Laser Ultrasonic Random Forest Regression

The traditional microstructure detecting methods such as metallography and electron backscatter diffraction are destructive to the sample and time-consuming and they cannot meet the needs of rapid online inspection. In this paper, a random forest regression microstructure characterization method based on a laser ultrasound technique is investigated for evaluating the microstructure of a titanium alloy (Ti-6Al-4V). Based on the high correlation between the longitudinal wave velocity of ultrasonic waves, the average grain size of the primary alpha phase, and the volume fraction of the transformed beta matrix of the titanium alloy, and with the longitudinal wave velocity as the input feature and the average grain size of the primary alpha phase and the volume fraction of the transformed beta matrix as the output features, prediction models for the average grain size of the primary alpha phase and the volume fraction of the transformed beta matrix were developed based on a random forest regression. The results show that the mean values of the mean relative errors of the predicted mean grain size of the native alpha phase and the volume fraction of the transformed beta matrix for the six samples in the two prediction models were 11.55% and 10.19%, respectively, and the RMSE and MAE obtained from both prediction models were relatively small, which indicates that the two established random forest regression models have a high prediction accuracy.