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 α phase, and the volume fraction of the transformed β matrix of the titanium alloy, and with the longitudinal wave velocity as the input feature and the average grain size of the primary α phase and the volume fraction of the transformed β matrix as the output features, prediction models for the average grain size of the primary α phase and the volume fraction of the transformed β 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 α phase and the volume fraction of the transformed β 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.