| 摘要: |
| 在经济增长新旧动能转换的关键时期，实施“创新驱动发展战略”成为当前的重点目标，材料研究
急需一个新的发展方式来突破瓶颈。人工智能技术在材料领域的应用正在迅速发展，现阶段，仅依靠传统的理
论研究与大量试验已无法满足高性能钛合金材料的性能预测。因此，本文综述了机器学习在应力-应变预测、弹
性模量预测、疲劳寿命预测、微观组织预测、材料成分设计等几个领域的突出成果，探究机器学习在钛合金材
料力学性能预测方面的前沿应用，总结其可优化之处，并对未来的合金材料性能预测做出展望 |
| 关键词: 钛合金 力学性能 机器学习 材料基因工程 |
| 基金项目:天津市科技军民融合重大专项（18ZXJM****160）；国家科技支撑计划资助项目（2015B****B04） |
|
| Progress in the Application of Machine Learning Assisted Prediction of Mechanical Properties of Titanium AlloysFlexible Manufacturing of Large-scale Aerospace Complex Components |
|
Gao Yunxi1,21,2, Wang Yuanyuan11, Deng Sanpeng1*1, Shi Jie22, Tian Xin22
|
|
1. Institute of Robotics and Intelligent Equipment, Tianjin University of Technology and Education, Tianjin 300222;2. Department of Mechanical and Electrical Engineering, Heilongjiang Agricultural Economy Vocational
College, Mudanjiang 157041
|
| Abstract: |
| In the critical period of transitioning between old and new drivers of economic growth, implementing
the“innovation-driven development strategy”has become a key objective and material research urgently requires a
new approach to overcome existing bottlenecks. The application of artificial intelligence technology in the materials
field is rapidly advancing. At present, traditional theoretical research and extensive experimentation are no longer
sufficient to meet the performance prediction needs of high-performance titanium alloys. Therefore, this paper reviews
the prominent achievements of machine learning in the areas of stress-strain prediction, elastic modulus prediction,
fatigue life prediction, microstructure prediction, and material composition design. It explores the frontier applications
of machine learning in predicting the mechanical properties of titanium alloys, summarizes potential areas for
optimization, and provides an outlook for future alloy material performance predictions. |
| Key words: titanium alloy mechanical property machine learning materials genetic engineering |
