Prediction improvement of compressive strength and strain of directionally solidified TiAl alloy based on training data size adjustment

Abstract

Compressive strength and compressive strain, which are important mechanical properties of directionally solidified TiAl alloy, were predicted using machine learning algorithms, specifically Multiple Linear Regression (MLR), and Random Forest Regression (RFR). The input variables for the machine learning model were designated as the composition of the directionally solidified TiAl alloy, experimental parameters (pulling velocity, input power), and compression test parameters (strain rate, compression temperature). Compressive strength and compressive strain were designated as the output variables. Although the typical ratio of training and test data is 8:2, this study used different ratios of 9:1, 7:3, and 6:4 for machine learning, and excellent R2 values were obtained for all ratios. The feature importance, which can identify the factor that has the most influence on the output variables, was obtained through the RFR algorithm. According to the feature importance, temperature was found to have the greatest influence on compressive strength, while the Erbium (Er) element had the most significant influence on compressive strain. Through the results of feature importance, it was possible to quantitatively investigate the relationship between the Er element, a microalloying element that affects the microstructure of TiAl alloy, and the compressive properties. Furthermore, the study was conducted on which data ratio between training and test data is most suitable for predicting the compressive strength and strain of TiAl alloy.

Keywords

• Directionally solidified TiAl alloy
• Compressive strength
• Interlamellar space
• Prediction
• Machine learning algorithm
• Training data size