A framework for computer-aided high performance titanium alloy design based on machine learning

Titanium alloy exhibits exceptional performance and a wide range of applications, with the high performance serving as the foundation for the development. However, traditional material design methods encounter numerous calculations and experimental trial-and-error processes, leading to increased costs and decreased efficiency in material design. The data-driven model presents an intriguing alternative to traditional material design methods by offering a novel approach to expedite the materials design process. In this study, a framework for computer-aided design high performance titanium alloys based on machine learning is proposed, which constructs an intelligent search space encompassing various combinations of 18 elements to facilitate alloy design. Firstly, a proprietary dataset was constructed for titanium alloy materials using feature design and a combination of unsupervised and supervised feature engineering methods. Secondly, six machine learning algorithms were employed to establish regression models, and the hyperparameters of each algorithm were optimized to improve model performance. Thirdly, the model was screened using five regression algorithm evaluation methods. The results demonstrated that the selected optimized model achieved an R 2 value of 0.95 on the verification set and 0.93 on the test set, yielding satisfactory outcomes. Finally, a comprehensive model framework along with an intelligent search methodology for designing high-strength titanium alloys has been established. It is believed that this method is also applicable to other properties of titanium alloys and the optimization of other materials.