β转变温度 (β tr ) 是钛合金最重要的特性之一。通常作为钛合金热处理工艺设计的指标。β tr也是优化钛合金加工工艺的重要参数。本研究开发了四种机器学习算法和一个经验公式来估计 β tr钛合金的研究：人工神经网络 (ANN)、高斯处理回归 (GPR)、超级向量机 (SVM) 和集成回归树 (ERT)。根据相关系数（R）、平均绝对误差（MAE）和均方根误差（RMSE）验证模型的准确性，同时利用实验测得的Ti600合金相变温度验证了模型的泛化能力模型。选择最佳模型来分析元素的敏感性并确定每个组件如何影响 β tr. 结果表明，ANN模型在五种模型中的预测精度最高，不同的模型结构对新数据的预测效果不同。10个神经元的ANN模型预测精度最高，而8个神经元的ANN模型泛化能力最强。敏感性分析的结果证明所有用作输入参数的合金成分都是有效参数。
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Machine learning-based beta transus temperature prediction for titanium alloys
Beta transus temperature (βtr) is one of the most crucial features of titanium alloys. It is typically used as the index while designing the heat treatment process for titanium alloys. The βtr is also a significant parameter to optimize the processing technology of titanium alloys. Four machine learning algorithms and one empirical formula is developed in this study to estimate the βtr of titanium alloys: Artificial Neural Networks (ANN), Gauss Processing Regression (GPR), Super Vector Machine (SVM), and Ensemble Regression Trees (ERT). According to the correlation coefficient (R), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to verify the accuracy of models, and the experimentally measured phase transition temperature of Ti600 alloy was also used to verify the generalization ability of the model. Choosing the best model to analyze the sensitivity of the elements and determine how each component affects the βtr. The result demonstrated that the ANN model has the highest prediction accuracy among the five models, and different model structures have different effects on predicting new data. The ANN model with 10 neurons has the highest prediction accuracy, while the ANN model with 8 neurons has the strongest generalization ability. The results of the sensitivity analysis proved that all the alloy compositions used as input parameters were valid parameters.