The mechanical properties of TiAl alloy prepared by directional solidification were predicted through a machine learning algorithm model. The composition, input power, and pulling speed were designated as input variables as representative factors influencing mechanical properties, and multiple linear regression analysis was conducted by collecting data obtained from the literature. In this study, the R² value of the tensile strength prediction result was 0.7159, elongation was 0.8459, nanoindentation hardness was 0.7573, and interlamellar spacing was 0.9674. As the R² value of the elongation obtained through the analysis was higher than the R² value of the tensile strength, it was confirmed that the elongation had a closer relationship with the input variables (composition, input power, pulling speed) than the tensile strength. By adding the elongation to the tensile strength as an input variable, it was observed that the R² value was further increased. The tensile test prediction results were divided into four groups: The group with the lowest residual value (predicted value - actual value) was designated as group A, and the group with the largest residual value was designated as group D. When comparing the values of group A and group D, more overpredictions occurred in group A, while more underpredictions occurred in group D. Using the residuals and R² values, the cause of the well-prediction was studied, and through this, the relationship between the mechanical properties and the microstructure was quantitatively investigated.

通过机器学习算法模型预测定向凝固制备的 TiAl 合金的力学性能。将成分、输入功率和牵引速度指定为输入变量，作为影响力学性能的代表性因素，通过收集文献数据进行多元线性回归分析。本研究抗拉强度预测结果的R ²值为0.7159，伸长率为0.8459，纳米压痕硬度为0.7573，层间距为0.9674。由于分析得到的伸长率的R ²值高于R ²的拉伸强度值，证实伸长率与输入变量（成分、输入功率、拉伸速度）的关系比拉伸强度更密切。通过将伸长率添加到拉伸强度作为输入变量，观察到R ²值进一步增加。拉伸试验预测结果分为四组：残值（预测值-实际值）最低的组为A组，残值最大的组为D组。 A组和D组，A组出现更多的高估，而D组出现更多的低估。 使用残差和R ² 值，研究了良好预测的原因，并通过此定量研究了力学性能与微观结构之间的关系。