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- Title
The Microstructure Characterization of a Titanium Alloy Based on a Laser Ultrasonic Random Forest Regression.
- Authors
Wu, Jinfeng; Yuan, Shuxian; Wang, Xiaogang; Chen, Huaidong; Huang, Fei; Yu, Chang; He, Yeqing; Yin, Anmin
- Abstract
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.
- Subjects
RANDOM forest algorithms; TITANIUM alloys; LONGITUDINAL waves; ULTRASONIC waves; GRAIN size; LASER ultrasonics
- Publication
Crystals (2073-4352), 2024, Vol 14, Issue 7, p607
- ISSN
2073-4352
- Publication type
Article
- DOI
10.3390/cryst14070607